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f7256c23b14e3bf3204687769f2e778d96ed7ed4 | 2,818 | py | Python | MA cross.py | 0xTDF/Quant-Trading-Strategy-Backtesting-Framework | d77089bab3513013d456819e9790e67e44adec8e | [
"MIT"
] | 1 | 2022-03-25T07:50:15.000Z | 2022-03-25T07:50:15.000Z | MA cross.py | Elisik/Quant-Trading-Strategy-Backtesting-Framework | d77089bab3513013d456819e9790e67e44adec8e | [
"MIT"
] | null | null | null | MA cross.py | Elisik/Quant-Trading-Strategy-Backtesting-Framework | d77089bab3513013d456819e9790e67e44adec8e | [
"MIT"
] | null | null | null | import backtrader as bt
import backtrader.analyzers as bta
from datetime import datetime
import matplotlib.pyplot as plt
import yfinance
class MaCrossStrategy(bt.Strategy):
# signal generator
def __init__(self):
ma_fast = bt.ind.SMA(period = 10)
ma_slow = bt.ind.SMA(period = 20)
self.crossover = bt.ind.CrossOver(ma_fast, ma_slow)
# executes order from the signals
def next(self):
if not self.position:
if self.crossover > 0:
self.buy()
elif self.crossover < 0:
self.close()
cerebro = bt.Cerebro()
# pulls price data from yahoo finance
data = bt.feeds.YahooFinanceCSVData(dataname='BTC-USD.csv')
# converts to log chart
data.plotinfo.plotlog = True
# adds data to engine
cerebro.adddata(data)
# adds strategy to engine
cerebro.addstrategy(MaCrossStrategy)
# sets starting capital
cerebro.broker.setcash(1000.0)
# sets size per trade
cerebro.addsizer(bt.sizers.PercentSizer, percents = 10)
# analysis
cerebro.addanalyzer(bta.SharpeRatio, _name = "sharpe")
cerebro.addanalyzer(bta.Transactions, _name = "trans")
cerebro.addanalyzer(bta.TradeAnalyzer, _name = "trades")
# runs back test
back = cerebro.run()
print(cerebro.broker.getvalue())
# useful output data
sharpeRatio = back[0].analyzers.sharpe.get_analysis()
print(sharpeRatio)
transactions = back[0].analyzers.trans.get_analysis()
#print(transactions)
tradeAnalyzer = back[0].analyzers.trades.get_analysis()
#print(tradeAnalyzer)
# colour scheme of plot
plt.style.use('fivethirtyeight')
plt.rcParams["figure.figsize"] = (10, 6)
plt.rcParams['lines.linewidth'] = 1
SIZE = 7
plt.rcParams['axes.labelsize'] = SIZE
plt.rcParams['ytick.labelsize'] = SIZE
plt.rcParams['xtick.labelsize'] = SIZE
plt.rcParams["font.size"] = SIZE
COLOR = '1'
plt.rcParams['text.color'] = COLOR
plt.rcParams['axes.labelcolor'] = COLOR
plt.rcParams['xtick.color'] = COLOR
plt.rcParams['ytick.color'] = COLOR
plt.rcParams['grid.linewidth']=0.1
plt.rcParams['grid.color']="#101622"
plt.rcParams['lines.color']="0.5"
plt.rcParams['axes.edgecolor']="0.2"
plt.rcParams['axes.linewidth']=0.5
plt.rcParams['figure.facecolor']="#101622"
plt.rcParams['axes.facecolor']="#101622"
plt.rcParams["savefig.dpi"]=120
dpi = plt.rcParams["savefig.dpi"]
width = 1080
height = 1920
plt.rcParams['figure.figsize'] = height/dpi, width/dpi
plt.rcParams["savefig.facecolor"] ="#101622"
plt.rcParams["savefig.edgecolor"]="#101622"
plt.rcParams['legend.fontsize'] = SIZE
plt.rcParams['legend.title_fontsize'] = SIZE + 1
plt.rcParams['legend.labelspacing'] =0.25
plt.rcParams['image.cmap']='tab10'
cerebro.plot(style = 'candle',barup='white', bardown='#1973c2',volume = False)
plt.show()
| 26.584906 | 79 | 0.694109 | import backtrader as bt
import backtrader.analyzers as bta
from datetime import datetime
import matplotlib.pyplot as plt
import yfinance
class MaCrossStrategy(bt.Strategy):
def __init__(self):
ma_fast = bt.ind.SMA(period = 10)
ma_slow = bt.ind.SMA(period = 20)
self.crossover = bt.ind.CrossOver(ma_fast, ma_slow)
def next(self):
if not self.position:
if self.crossover > 0:
self.buy()
elif self.crossover < 0:
self.close()
cerebro = bt.Cerebro()
data = bt.feeds.YahooFinanceCSVData(dataname='BTC-USD.csv')
data.plotinfo.plotlog = True
cerebro.adddata(data)
cerebro.addstrategy(MaCrossStrategy)
cerebro.broker.setcash(1000.0)
cerebro.addsizer(bt.sizers.PercentSizer, percents = 10)
cerebro.addanalyzer(bta.SharpeRatio, _name = "sharpe")
cerebro.addanalyzer(bta.Transactions, _name = "trans")
cerebro.addanalyzer(bta.TradeAnalyzer, _name = "trades")
back = cerebro.run()
print(cerebro.broker.getvalue())
sharpeRatio = back[0].analyzers.sharpe.get_analysis()
print(sharpeRatio)
transactions = back[0].analyzers.trans.get_analysis()
tradeAnalyzer = back[0].analyzers.trades.get_analysis()
plt.style.use('fivethirtyeight')
plt.rcParams["figure.figsize"] = (10, 6)
plt.rcParams['lines.linewidth'] = 1
SIZE = 7
plt.rcParams['axes.labelsize'] = SIZE
plt.rcParams['ytick.labelsize'] = SIZE
plt.rcParams['xtick.labelsize'] = SIZE
plt.rcParams["font.size"] = SIZE
COLOR = '1'
plt.rcParams['text.color'] = COLOR
plt.rcParams['axes.labelcolor'] = COLOR
plt.rcParams['xtick.color'] = COLOR
plt.rcParams['ytick.color'] = COLOR
plt.rcParams['grid.linewidth']=0.1
plt.rcParams['grid.color']="#101622"
plt.rcParams['lines.color']="0.5"
plt.rcParams['axes.edgecolor']="0.2"
plt.rcParams['axes.linewidth']=0.5
plt.rcParams['figure.facecolor']="#101622"
plt.rcParams['axes.facecolor']="#101622"
plt.rcParams["savefig.dpi"]=120
dpi = plt.rcParams["savefig.dpi"]
width = 1080
height = 1920
plt.rcParams['figure.figsize'] = height/dpi, width/dpi
plt.rcParams["savefig.facecolor"] ="#101622"
plt.rcParams["savefig.edgecolor"]="#101622"
plt.rcParams['legend.fontsize'] = SIZE
plt.rcParams['legend.title_fontsize'] = SIZE + 1
plt.rcParams['legend.labelspacing'] =0.25
plt.rcParams['image.cmap']='tab10'
cerebro.plot(style = 'candle',barup='white', bardown='#1973c2',volume = False)
plt.show()
| true | true |
f7256d283ba52ce9290a4bd6ce811edcc7a1208a | 8,082 | py | Python | juriscraper/opinions/united_states_backscrapers/federal_appellate/ca5.py | drewsilcock/juriscraper | 706a05f739e10f22b81b9bb16767415d810e49d1 | [
"BSD-2-Clause"
] | null | null | null | juriscraper/opinions/united_states_backscrapers/federal_appellate/ca5.py | drewsilcock/juriscraper | 706a05f739e10f22b81b9bb16767415d810e49d1 | [
"BSD-2-Clause"
] | null | null | null | juriscraper/opinions/united_states_backscrapers/federal_appellate/ca5.py | drewsilcock/juriscraper | 706a05f739e10f22b81b9bb16767415d810e49d1 | [
"BSD-2-Clause"
] | null | null | null | from lxml import html
from datetime import datetime, timedelta, date
from dateutil.rrule import DAILY, rrule
from selenium.common.exceptions import NoSuchElementException
from juriscraper.AbstractSite import logger
from juriscraper.OpinionSiteWebDriven import OpinionSiteWebDriven
class Site(OpinionSiteWebDriven):
def __init__(self, *args, **kwargs):
super(Site, self).__init__(*args, **kwargs)
self.url = "http://www.ca5.uscourts.gov/electronic-case-filing/case-information/current-opinions"
self.court_id = self.__module__
self.interval = 5
self.case_date = datetime.today()
self.back_scrape_iterable = [
i.date()
for i in rrule(
DAILY,
interval=self.interval, # Every interval days
dtstart=date(1992, 5, 14),
until=date(2015, 1, 1),
)
]
self.uses_selenium = True
def _download(self, request_dict={}):
if self.test_mode_enabled():
html_tree_list = [
super(Site, self)._download(request_dict=request_dict)
]
self.records_nr = len(
html_tree_list[0].xpath(
"//tr[contains(concat('', @id, ''), 'ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00')]"
)
)
return html_tree_list
else:
logger.info("Running Selenium browser...")
self.initiate_webdriven_session()
self.wait_for_id("ctl00_Body_C010_ctl00_ctl00_endDate_dateInput")
start_date = self.webdriver.find_element_by_id(
"ctl00_Body_C010_ctl00_ctl00_startDate_dateInput"
)
start_date.send_keys(
(self.case_date - timedelta(days=self.interval)).strftime(
"%m/%d/%Y"
)
)
end_date = self.webdriver.find_element_by_id(
"ctl00_Body_C010_ctl00_ctl00_endDate_dateInput"
)
end_date.send_keys(self.case_date.strftime("%m/%d/%Y"))
submit = self.webdriver.find_element_by_id(
"Body_C010_ctl00_ctl00_btnSearch"
)
submit.click()
self.wait_for_id(
"ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00"
)
self.status = 200
try:
nr_of_pages = self.webdriver.find_element_by_xpath(
'//div[contains(concat(" ", @class, " "), "rgInfoPart")]/strong[2]'
)
records_nr = self.webdriver.find_element_by_xpath(
'//div[contains(concat(" ", @class, " "), "rgInfoPart")]/strong[1]'
)
self.records_nr = int(records_nr.text)
nr_of_pages = int(nr_of_pages.text)
except NoSuchElementException:
try:
self.records_nr = len(
self.webdriver.find_elements_by_xpath(
"//tr[contains(concat('', @id, ''), 'ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00')]"
)
)
nr_of_pages = 1
except NoSuchElementException:
self.webdriver.quit()
return []
html_pages = []
logger.info(
"records: {}, pages: {}".format(self.records_nr, nr_of_pages)
)
if nr_of_pages == 1:
text = self.webdriver.page_source
self.webdriver.quit()
html_tree = html.fromstring(text)
html_tree.make_links_absolute(self.url)
remove_anchors = lambda url: url.split("#")[0]
html_tree.rewrite_links(remove_anchors)
html_pages.append(html_tree)
else:
logger.info(
"Paginating through %s pages of results." % nr_of_pages
)
logger.info(" Getting page 1")
text = self.webdriver.page_source
html_tree = html.fromstring(text)
html_tree.make_links_absolute(self.url)
remove_anchors = lambda url: url.split("#")[0]
html_tree.rewrite_links(remove_anchors)
html_pages.append(html_tree)
for i in range(nr_of_pages - 1):
logger.info(" Getting page %s" % (i + 2))
next_page = self.webdriver.find_element_by_class_name(
"rgPageNext"
)
next_page.click()
self.webdriver.implicitly_wait(5)
text = self.webdriver.page_source
html_tree = html.fromstring(text)
html_tree.make_links_absolute(self.url)
remove_anchors = lambda url: url.split("#")[0]
html_tree.rewrite_links(remove_anchors)
html_pages.append(html_tree)
self.webdriver.quit()
return html_pages
def _get_case_names(self):
case_names = []
for html_tree in self.html:
page_records_count = self._get_opinion_count(html_tree)
for record in range(page_records_count):
path = "id('ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00__{n}')/td[4]/text()".format(
n=record
)
case_names.append(html_tree.xpath(path)[0])
return case_names
def _get_download_urls(self):
for html_tree in self.html:
page_records_count = self._get_opinion_count(html_tree)
for record in range(page_records_count):
path = "id('ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00__{n}')/td[2]/a/@href".format(
n=record
)
yield html_tree.xpath(path)[0]
def _get_case_dates(self):
for html_tree in self.html:
page_records_count = self._get_opinion_count(html_tree)
for record in range(page_records_count):
path = "id('ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00__{n}')/td[3]/text()".format(
n=record
)
yield datetime.strptime(html_tree.xpath(path)[0], "%m/%d/%Y")
def _get_docket_numbers(self):
for html_tree in self.html:
page_records_count = self._get_opinion_count(html_tree)
for record in range(page_records_count):
path = "id('ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00__{n}')/td[2]/a/text()".format(
n=record
)
yield html_tree.xpath(path)[0]
def _get_precedential_statuses(self):
for html_tree in self.html:
page_records_count = self._get_opinion_count(html_tree)
for record in range(page_records_count):
path = "id('ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00__{n}')/td[5]/text()".format(
n=record
)
yield "Unpublished" if "unpub" in html_tree.xpath(path)[
0
] else "Published"
@staticmethod
def _get_opinion_count(html_tree):
return int(
html_tree.xpath(
"count(//tr[contains(concat('', @id, ''), 'ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00')])"
)
)
def _download_backwards(self, d):
self.case_date = d
logger.info(
"Running backscraper with date range: %s to %s"
% (
self.case_date - timedelta(days=self.interval),
self.case_date,
)
)
self.html = self._download()
if self.html is not None:
# Setting status is important because it prevents the download
# function from being run a second time by the parse method.
self.status = 200
| 39.23301 | 118 | 0.548627 | from lxml import html
from datetime import datetime, timedelta, date
from dateutil.rrule import DAILY, rrule
from selenium.common.exceptions import NoSuchElementException
from juriscraper.AbstractSite import logger
from juriscraper.OpinionSiteWebDriven import OpinionSiteWebDriven
class Site(OpinionSiteWebDriven):
def __init__(self, *args, **kwargs):
super(Site, self).__init__(*args, **kwargs)
self.url = "http://www.ca5.uscourts.gov/electronic-case-filing/case-information/current-opinions"
self.court_id = self.__module__
self.interval = 5
self.case_date = datetime.today()
self.back_scrape_iterable = [
i.date()
for i in rrule(
DAILY,
interval=self.interval,
dtstart=date(1992, 5, 14),
until=date(2015, 1, 1),
)
]
self.uses_selenium = True
def _download(self, request_dict={}):
if self.test_mode_enabled():
html_tree_list = [
super(Site, self)._download(request_dict=request_dict)
]
self.records_nr = len(
html_tree_list[0].xpath(
"//tr[contains(concat('', @id, ''), 'ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00')]"
)
)
return html_tree_list
else:
logger.info("Running Selenium browser...")
self.initiate_webdriven_session()
self.wait_for_id("ctl00_Body_C010_ctl00_ctl00_endDate_dateInput")
start_date = self.webdriver.find_element_by_id(
"ctl00_Body_C010_ctl00_ctl00_startDate_dateInput"
)
start_date.send_keys(
(self.case_date - timedelta(days=self.interval)).strftime(
"%m/%d/%Y"
)
)
end_date = self.webdriver.find_element_by_id(
"ctl00_Body_C010_ctl00_ctl00_endDate_dateInput"
)
end_date.send_keys(self.case_date.strftime("%m/%d/%Y"))
submit = self.webdriver.find_element_by_id(
"Body_C010_ctl00_ctl00_btnSearch"
)
submit.click()
self.wait_for_id(
"ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00"
)
self.status = 200
try:
nr_of_pages = self.webdriver.find_element_by_xpath(
'//div[contains(concat(" ", @class, " "), "rgInfoPart")]/strong[2]'
)
records_nr = self.webdriver.find_element_by_xpath(
'//div[contains(concat(" ", @class, " "), "rgInfoPart")]/strong[1]'
)
self.records_nr = int(records_nr.text)
nr_of_pages = int(nr_of_pages.text)
except NoSuchElementException:
try:
self.records_nr = len(
self.webdriver.find_elements_by_xpath(
"//tr[contains(concat('', @id, ''), 'ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00')]"
)
)
nr_of_pages = 1
except NoSuchElementException:
self.webdriver.quit()
return []
html_pages = []
logger.info(
"records: {}, pages: {}".format(self.records_nr, nr_of_pages)
)
if nr_of_pages == 1:
text = self.webdriver.page_source
self.webdriver.quit()
html_tree = html.fromstring(text)
html_tree.make_links_absolute(self.url)
remove_anchors = lambda url: url.split("#")[0]
html_tree.rewrite_links(remove_anchors)
html_pages.append(html_tree)
else:
logger.info(
"Paginating through %s pages of results." % nr_of_pages
)
logger.info(" Getting page 1")
text = self.webdriver.page_source
html_tree = html.fromstring(text)
html_tree.make_links_absolute(self.url)
remove_anchors = lambda url: url.split("#")[0]
html_tree.rewrite_links(remove_anchors)
html_pages.append(html_tree)
for i in range(nr_of_pages - 1):
logger.info(" Getting page %s" % (i + 2))
next_page = self.webdriver.find_element_by_class_name(
"rgPageNext"
)
next_page.click()
self.webdriver.implicitly_wait(5)
text = self.webdriver.page_source
html_tree = html.fromstring(text)
html_tree.make_links_absolute(self.url)
remove_anchors = lambda url: url.split("#")[0]
html_tree.rewrite_links(remove_anchors)
html_pages.append(html_tree)
self.webdriver.quit()
return html_pages
def _get_case_names(self):
case_names = []
for html_tree in self.html:
page_records_count = self._get_opinion_count(html_tree)
for record in range(page_records_count):
path = "id('ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00__{n}')/td[4]/text()".format(
n=record
)
case_names.append(html_tree.xpath(path)[0])
return case_names
def _get_download_urls(self):
for html_tree in self.html:
page_records_count = self._get_opinion_count(html_tree)
for record in range(page_records_count):
path = "id('ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00__{n}')/td[2]/a/@href".format(
n=record
)
yield html_tree.xpath(path)[0]
def _get_case_dates(self):
for html_tree in self.html:
page_records_count = self._get_opinion_count(html_tree)
for record in range(page_records_count):
path = "id('ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00__{n}')/td[3]/text()".format(
n=record
)
yield datetime.strptime(html_tree.xpath(path)[0], "%m/%d/%Y")
def _get_docket_numbers(self):
for html_tree in self.html:
page_records_count = self._get_opinion_count(html_tree)
for record in range(page_records_count):
path = "id('ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00__{n}')/td[2]/a/text()".format(
n=record
)
yield html_tree.xpath(path)[0]
def _get_precedential_statuses(self):
for html_tree in self.html:
page_records_count = self._get_opinion_count(html_tree)
for record in range(page_records_count):
path = "id('ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00__{n}')/td[5]/text()".format(
n=record
)
yield "Unpublished" if "unpub" in html_tree.xpath(path)[
0
] else "Published"
@staticmethod
def _get_opinion_count(html_tree):
return int(
html_tree.xpath(
"count(//tr[contains(concat('', @id, ''), 'ctl00_Body_C010_ctl00_ctl00_radGridOpinions_ctl00')])"
)
)
def _download_backwards(self, d):
self.case_date = d
logger.info(
"Running backscraper with date range: %s to %s"
% (
self.case_date - timedelta(days=self.interval),
self.case_date,
)
)
self.html = self._download()
if self.html is not None:
self.status = 200
| true | true |
f7256de9d9a438b0f395aac6da42babe3f3800f4 | 11,937 | py | Python | tests/integration/test_polymorphic_parts/test.py | monadbobo/ClickHouse | 73b0f8db8c327a1d63cc7ebcc56087a3f9866dae | [
"Apache-2.0"
] | 3 | 2021-09-14T08:36:18.000Z | 2022-02-24T02:55:38.000Z | tests/integration/test_polymorphic_parts/test.py | monadbobo/ClickHouse | 73b0f8db8c327a1d63cc7ebcc56087a3f9866dae | [
"Apache-2.0"
] | 1 | 2020-04-04T04:25:47.000Z | 2020-04-04T04:25:47.000Z | tests/integration/test_polymorphic_parts/test.py | monadbobo/ClickHouse | 73b0f8db8c327a1d63cc7ebcc56087a3f9866dae | [
"Apache-2.0"
] | 1 | 2020-05-18T11:31:48.000Z | 2020-05-18T11:31:48.000Z | import time
import pytest
import random
import string
from helpers.test_tools import TSV
from helpers.test_tools import assert_eq_with_retry
from helpers.cluster import ClickHouseCluster
cluster = ClickHouseCluster(__file__)
def get_random_array():
return [random.randint(0, 1000) % 1000 for _ in range(random.randint(0, 1000))]
def get_random_string():
length = random.randint(0, 1000)
return ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(length))
def insert_random_data(table, node, size):
data = [
'(' + ','.join((
"'2019-10-11'",
str(i),
"'" + get_random_string() + "'",
str(get_random_array()))) +
')' for i in range(size)
]
node.query("INSERT INTO {} VALUES {}".format(table, ','.join(data)))
def create_tables(name, nodes, node_settings, shard):
for i, (node, settings) in enumerate(zip(nodes, node_settings)):
node.query(
'''
CREATE TABLE {name}(date Date, id UInt32, s String, arr Array(Int32))
ENGINE = ReplicatedMergeTree('/clickhouse/tables/test/{shard}/{name}', '{repl}')
PARTITION BY toYYYYMM(date)
ORDER BY id
SETTINGS index_granularity = {index_granularity}, index_granularity_bytes = {index_granularity_bytes},
min_rows_for_wide_part = {min_rows_for_wide_part}, min_bytes_for_wide_part = {min_bytes_for_wide_part}
'''.format(name=name, shard=shard, repl=i, **settings))
def create_tables_old_format(name, nodes, shard):
for i, node in enumerate(nodes):
node.query(
'''
CREATE TABLE {name}(date Date, id UInt32, s String, arr Array(Int32))
ENGINE = ReplicatedMergeTree('/clickhouse/tables/test/{shard}/{name}', '{repl}', date, id, 64)
'''.format(name=name, shard=shard, repl=i))
node1 = cluster.add_instance('node1', config_dir="configs", with_zookeeper=True)
node2 = cluster.add_instance('node2', config_dir="configs", with_zookeeper=True)
settings_default = {'index_granularity' : 64, 'index_granularity_bytes' : 10485760, 'min_rows_for_wide_part' : 512, 'min_bytes_for_wide_part' : 0}
settings_not_adaptive = {'index_granularity' : 64, 'index_granularity_bytes' : 0, 'min_rows_for_wide_part' : 512, 'min_bytes_for_wide_part' : 0}
node3 = cluster.add_instance('node3', config_dir="configs", with_zookeeper=True)
node4 = cluster.add_instance('node4', config_dir="configs", main_configs=['configs/no_leader.xml'], with_zookeeper=True)
settings_compact = {'index_granularity' : 64, 'index_granularity_bytes' : 10485760, 'min_rows_for_wide_part' : 512, 'min_bytes_for_wide_part' : 0}
settings_wide = {'index_granularity' : 64, 'index_granularity_bytes' : 10485760, 'min_rows_for_wide_part' : 0, 'min_bytes_for_wide_part' : 0}
node5 = cluster.add_instance('node5', config_dir='configs', main_configs=['configs/compact_parts.xml'], with_zookeeper=True)
node6 = cluster.add_instance('node6', config_dir='configs', main_configs=['configs/compact_parts.xml'], with_zookeeper=True)
@pytest.fixture(scope="module")
def start_cluster():
try:
cluster.start()
create_tables('polymorphic_table', [node1, node2], [settings_default, settings_default], "shard1")
create_tables('non_adaptive_table', [node1, node2], [settings_not_adaptive, settings_default], "shard1")
create_tables('polymorphic_table_compact', [node3, node4], [settings_compact, settings_wide], "shard2")
create_tables('polymorphic_table_wide', [node3, node4], [settings_wide, settings_compact], "shard2")
create_tables_old_format('polymorphic_table', [node5, node6], "shard3")
yield cluster
finally:
cluster.shutdown()
@pytest.mark.parametrize(
('first_node', 'second_node'),
[
(node1, node2),
(node5, node6)
]
)
def test_polymorphic_parts_basics(start_cluster, first_node, second_node):
first_node.query("SYSTEM STOP MERGES")
second_node.query("SYSTEM STOP MERGES")
for size in [300, 300, 600]:
insert_random_data('polymorphic_table', first_node, size)
second_node.query("SYSTEM SYNC REPLICA polymorphic_table", timeout=20)
assert first_node.query("SELECT count() FROM polymorphic_table") == "1200\n"
assert second_node.query("SELECT count() FROM polymorphic_table") == "1200\n"
expected = "Compact\t2\nWide\t1\n"
assert TSV(first_node.query("SELECT part_type, count() FROM system.parts " \
"WHERE table = 'polymorphic_table' AND active GROUP BY part_type ORDER BY part_type")) == TSV(expected)
assert TSV(second_node.query("SELECT part_type, count() FROM system.parts " \
"WHERE table = 'polymorphic_table' AND active GROUP BY part_type ORDER BY part_type")) == TSV(expected)
first_node.query("SYSTEM START MERGES")
second_node.query("SYSTEM START MERGES")
for _ in range(40):
insert_random_data('polymorphic_table', first_node, 10)
insert_random_data('polymorphic_table', second_node, 10)
first_node.query("SYSTEM SYNC REPLICA polymorphic_table", timeout=20)
second_node.query("SYSTEM SYNC REPLICA polymorphic_table", timeout=20)
assert first_node.query("SELECT count() FROM polymorphic_table") == "2000\n"
assert second_node.query("SELECT count() FROM polymorphic_table") == "2000\n"
first_node.query("OPTIMIZE TABLE polymorphic_table FINAL")
second_node.query("SYSTEM SYNC REPLICA polymorphic_table", timeout=20)
assert first_node.query("SELECT count() FROM polymorphic_table") == "2000\n"
assert second_node.query("SELECT count() FROM polymorphic_table") == "2000\n"
assert first_node.query("SELECT DISTINCT part_type FROM system.parts WHERE table = 'polymorphic_table' AND active") == "Wide\n"
assert second_node.query("SELECT DISTINCT part_type FROM system.parts WHERE table = 'polymorphic_table' AND active") == "Wide\n"
# Check alters and mutations also work
first_node.query("ALTER TABLE polymorphic_table ADD COLUMN ss String")
first_node.query("ALTER TABLE polymorphic_table UPDATE ss = toString(id) WHERE 1")
second_node.query("SYSTEM SYNC REPLICA polymorphic_table", timeout=20)
first_node.query("SELECT count(ss) FROM polymorphic_table") == "2000\n"
first_node.query("SELECT uniqExact(ss) FROM polymorphic_table") == "600\n"
second_node.query("SELECT count(ss) FROM polymorphic_table") == "2000\n"
second_node.query("SELECT uniqExact(ss) FROM polymorphic_table") == "600\n"
# Check that follower replicas create parts of the same type, which leader has chosen at merge.
@pytest.mark.parametrize(
('table', 'part_type'),
[
('polymorphic_table_compact', 'Compact'),
('polymorphic_table_wide', 'Wide')
]
)
def test_different_part_types_on_replicas(start_cluster, table, part_type):
leader = node3
follower = node4
assert leader.query("SELECT is_leader FROM system.replicas WHERE table = '{}'".format(table)) == "1\n"
assert node4.query("SELECT is_leader FROM system.replicas WHERE table = '{}'".format(table)) == "0\n"
for _ in range(3):
insert_random_data(table, leader, 100)
leader.query("OPTIMIZE TABLE {} FINAL".format(table))
follower.query("SYSTEM SYNC REPLICA {}".format(table), timeout=20)
expected = "{}\t1\n".format(part_type)
assert TSV(leader.query("SELECT part_type, count() FROM system.parts " \
"WHERE table = '{}' AND active GROUP BY part_type ORDER BY part_type".format(table))) == TSV(expected)
assert TSV(follower.query("SELECT part_type, count() FROM system.parts " \
"WHERE table = '{}' AND active GROUP BY part_type ORDER BY part_type".format(table))) == TSV(expected)
node7 = cluster.add_instance('node7', config_dir="configs", with_zookeeper=True, image='yandex/clickhouse-server:19.17.8.54', stay_alive=True, with_installed_binary=True)
node8 = cluster.add_instance('node8', config_dir="configs", with_zookeeper=True)
settings7 = {'index_granularity' : 64, 'index_granularity_bytes' : 10485760}
settings8 = {'index_granularity' : 64, 'index_granularity_bytes' : 10485760, 'min_rows_for_wide_part' : 512, 'min_bytes_for_wide_part' : 0}
@pytest.fixture(scope="module")
def start_cluster_diff_versions():
try:
for name in ['polymorphic_table', 'polymorphic_table_2']:
cluster.start()
node7.query(
'''
CREATE TABLE {name}(date Date, id UInt32, s String, arr Array(Int32))
ENGINE = ReplicatedMergeTree('/clickhouse/tables/test/shard5/{name}', '1')
PARTITION BY toYYYYMM(date)
ORDER BY id
SETTINGS index_granularity = {index_granularity}, index_granularity_bytes = {index_granularity_bytes}
'''.format(name=name, **settings7)
)
node8.query(
'''
CREATE TABLE {name}(date Date, id UInt32, s String, arr Array(Int32))
ENGINE = ReplicatedMergeTree('/clickhouse/tables/test/shard5/{name}', '2')
PARTITION BY toYYYYMM(date)
ORDER BY id
SETTINGS index_granularity = {index_granularity}, index_granularity_bytes = {index_granularity_bytes},
min_rows_for_wide_part = {min_rows_for_wide_part}, min_bytes_for_wide_part = {min_bytes_for_wide_part}
'''.format(name=name, **settings8)
)
yield cluster
finally:
cluster.shutdown()
@pytest.mark.skip(reason="compatability is temporary broken")
def test_polymorphic_parts_diff_versions(start_cluster_diff_versions):
# Check that replication with Wide parts works between different versions.
node_old = node7
node_new = node8
insert_random_data('polymorphic_table', node7, 100)
node8.query("SYSTEM SYNC REPLICA polymorphic_table", timeout=20)
assert node8.query("SELECT count() FROM polymorphic_table") == "100\n"
assert node8.query("SELECT DISTINCT part_type FROM system.parts WHERE table = 'polymorphic_table' and active") == "Wide\n"
@pytest.mark.skip(reason="compatability is temporary broken")
def test_polymorphic_parts_diff_versions_2(start_cluster_diff_versions):
# Replication doesn't work on old version if part is created in compact format, because
# this version doesn't know anything about it. It's considered to be ok.
node_old = node7
node_new = node8
insert_random_data('polymorphic_table_2', node_new, 100)
assert node_new.query("SELECT count() FROM polymorphic_table_2") == "100\n"
assert node_old.query("SELECT count() FROM polymorphic_table_2") == "0\n"
with pytest.raises(Exception):
node_old.query("SYSTEM SYNC REPLICA polymorphic_table_2", timeout=3)
node_old.restart_with_latest_version()
node_old.query("SYSTEM SYNC REPLICA polymorphic_table_2", timeout=20)
# Works after update
assert node_old.query("SELECT count() FROM polymorphic_table_2") == "100\n"
assert node_old.query("SELECT DISTINCT part_type FROM system.parts WHERE table = 'polymorphic_table_2' and active") == "Compact\n"
def test_polymorphic_parts_non_adaptive(start_cluster):
node1.query("SYSTEM STOP MERGES")
node2.query("SYSTEM STOP MERGES")
insert_random_data('non_adaptive_table', node1, 100)
node2.query("SYSTEM SYNC REPLICA non_adaptive_table", timeout=20)
insert_random_data('non_adaptive_table', node2, 100)
node1.query("SYSTEM SYNC REPLICA non_adaptive_table", timeout=20)
assert TSV(node1.query("SELECT part_type, count() FROM system.parts " \
"WHERE table = 'non_adaptive_table' AND active GROUP BY part_type ORDER BY part_type")) == TSV("Wide\t2\n")
assert TSV(node2.query("SELECT part_type, count() FROM system.parts " \
"WHERE table = 'non_adaptive_table' AND active GROUP BY part_type ORDER BY part_type")) == TSV("Wide\t2\n")
assert node1.contains_in_log("<Warning> default.non_adaptive_table: Table can't create parts with adaptive granularity")
| 45.387833 | 170 | 0.70981 | import time
import pytest
import random
import string
from helpers.test_tools import TSV
from helpers.test_tools import assert_eq_with_retry
from helpers.cluster import ClickHouseCluster
cluster = ClickHouseCluster(__file__)
def get_random_array():
return [random.randint(0, 1000) % 1000 for _ in range(random.randint(0, 1000))]
def get_random_string():
length = random.randint(0, 1000)
return ''.join(random.choice(string.ascii_uppercase + string.digits) for _ in range(length))
def insert_random_data(table, node, size):
data = [
'(' + ','.join((
"'2019-10-11'",
str(i),
"'" + get_random_string() + "'",
str(get_random_array()))) +
')' for i in range(size)
]
node.query("INSERT INTO {} VALUES {}".format(table, ','.join(data)))
def create_tables(name, nodes, node_settings, shard):
for i, (node, settings) in enumerate(zip(nodes, node_settings)):
node.query(
'''
CREATE TABLE {name}(date Date, id UInt32, s String, arr Array(Int32))
ENGINE = ReplicatedMergeTree('/clickhouse/tables/test/{shard}/{name}', '{repl}')
PARTITION BY toYYYYMM(date)
ORDER BY id
SETTINGS index_granularity = {index_granularity}, index_granularity_bytes = {index_granularity_bytes},
min_rows_for_wide_part = {min_rows_for_wide_part}, min_bytes_for_wide_part = {min_bytes_for_wide_part}
'''.format(name=name, shard=shard, repl=i, **settings))
def create_tables_old_format(name, nodes, shard):
for i, node in enumerate(nodes):
node.query(
'''
CREATE TABLE {name}(date Date, id UInt32, s String, arr Array(Int32))
ENGINE = ReplicatedMergeTree('/clickhouse/tables/test/{shard}/{name}', '{repl}', date, id, 64)
'''.format(name=name, shard=shard, repl=i))
node1 = cluster.add_instance('node1', config_dir="configs", with_zookeeper=True)
node2 = cluster.add_instance('node2', config_dir="configs", with_zookeeper=True)
settings_default = {'index_granularity' : 64, 'index_granularity_bytes' : 10485760, 'min_rows_for_wide_part' : 512, 'min_bytes_for_wide_part' : 0}
settings_not_adaptive = {'index_granularity' : 64, 'index_granularity_bytes' : 0, 'min_rows_for_wide_part' : 512, 'min_bytes_for_wide_part' : 0}
node3 = cluster.add_instance('node3', config_dir="configs", with_zookeeper=True)
node4 = cluster.add_instance('node4', config_dir="configs", main_configs=['configs/no_leader.xml'], with_zookeeper=True)
settings_compact = {'index_granularity' : 64, 'index_granularity_bytes' : 10485760, 'min_rows_for_wide_part' : 512, 'min_bytes_for_wide_part' : 0}
settings_wide = {'index_granularity' : 64, 'index_granularity_bytes' : 10485760, 'min_rows_for_wide_part' : 0, 'min_bytes_for_wide_part' : 0}
node5 = cluster.add_instance('node5', config_dir='configs', main_configs=['configs/compact_parts.xml'], with_zookeeper=True)
node6 = cluster.add_instance('node6', config_dir='configs', main_configs=['configs/compact_parts.xml'], with_zookeeper=True)
@pytest.fixture(scope="module")
def start_cluster():
try:
cluster.start()
create_tables('polymorphic_table', [node1, node2], [settings_default, settings_default], "shard1")
create_tables('non_adaptive_table', [node1, node2], [settings_not_adaptive, settings_default], "shard1")
create_tables('polymorphic_table_compact', [node3, node4], [settings_compact, settings_wide], "shard2")
create_tables('polymorphic_table_wide', [node3, node4], [settings_wide, settings_compact], "shard2")
create_tables_old_format('polymorphic_table', [node5, node6], "shard3")
yield cluster
finally:
cluster.shutdown()
@pytest.mark.parametrize(
('first_node', 'second_node'),
[
(node1, node2),
(node5, node6)
]
)
def test_polymorphic_parts_basics(start_cluster, first_node, second_node):
first_node.query("SYSTEM STOP MERGES")
second_node.query("SYSTEM STOP MERGES")
for size in [300, 300, 600]:
insert_random_data('polymorphic_table', first_node, size)
second_node.query("SYSTEM SYNC REPLICA polymorphic_table", timeout=20)
assert first_node.query("SELECT count() FROM polymorphic_table") == "1200\n"
assert second_node.query("SELECT count() FROM polymorphic_table") == "1200\n"
expected = "Compact\t2\nWide\t1\n"
assert TSV(first_node.query("SELECT part_type, count() FROM system.parts " \
"WHERE table = 'polymorphic_table' AND active GROUP BY part_type ORDER BY part_type")) == TSV(expected)
assert TSV(second_node.query("SELECT part_type, count() FROM system.parts " \
"WHERE table = 'polymorphic_table' AND active GROUP BY part_type ORDER BY part_type")) == TSV(expected)
first_node.query("SYSTEM START MERGES")
second_node.query("SYSTEM START MERGES")
for _ in range(40):
insert_random_data('polymorphic_table', first_node, 10)
insert_random_data('polymorphic_table', second_node, 10)
first_node.query("SYSTEM SYNC REPLICA polymorphic_table", timeout=20)
second_node.query("SYSTEM SYNC REPLICA polymorphic_table", timeout=20)
assert first_node.query("SELECT count() FROM polymorphic_table") == "2000\n"
assert second_node.query("SELECT count() FROM polymorphic_table") == "2000\n"
first_node.query("OPTIMIZE TABLE polymorphic_table FINAL")
second_node.query("SYSTEM SYNC REPLICA polymorphic_table", timeout=20)
assert first_node.query("SELECT count() FROM polymorphic_table") == "2000\n"
assert second_node.query("SELECT count() FROM polymorphic_table") == "2000\n"
assert first_node.query("SELECT DISTINCT part_type FROM system.parts WHERE table = 'polymorphic_table' AND active") == "Wide\n"
assert second_node.query("SELECT DISTINCT part_type FROM system.parts WHERE table = 'polymorphic_table' AND active") == "Wide\n"
first_node.query("ALTER TABLE polymorphic_table ADD COLUMN ss String")
first_node.query("ALTER TABLE polymorphic_table UPDATE ss = toString(id) WHERE 1")
second_node.query("SYSTEM SYNC REPLICA polymorphic_table", timeout=20)
first_node.query("SELECT count(ss) FROM polymorphic_table") == "2000\n"
first_node.query("SELECT uniqExact(ss) FROM polymorphic_table") == "600\n"
second_node.query("SELECT count(ss) FROM polymorphic_table") == "2000\n"
second_node.query("SELECT uniqExact(ss) FROM polymorphic_table") == "600\n"
@pytest.mark.parametrize(
('table', 'part_type'),
[
('polymorphic_table_compact', 'Compact'),
('polymorphic_table_wide', 'Wide')
]
)
def test_different_part_types_on_replicas(start_cluster, table, part_type):
leader = node3
follower = node4
assert leader.query("SELECT is_leader FROM system.replicas WHERE table = '{}'".format(table)) == "1\n"
assert node4.query("SELECT is_leader FROM system.replicas WHERE table = '{}'".format(table)) == "0\n"
for _ in range(3):
insert_random_data(table, leader, 100)
leader.query("OPTIMIZE TABLE {} FINAL".format(table))
follower.query("SYSTEM SYNC REPLICA {}".format(table), timeout=20)
expected = "{}\t1\n".format(part_type)
assert TSV(leader.query("SELECT part_type, count() FROM system.parts " \
"WHERE table = '{}' AND active GROUP BY part_type ORDER BY part_type".format(table))) == TSV(expected)
assert TSV(follower.query("SELECT part_type, count() FROM system.parts " \
"WHERE table = '{}' AND active GROUP BY part_type ORDER BY part_type".format(table))) == TSV(expected)
node7 = cluster.add_instance('node7', config_dir="configs", with_zookeeper=True, image='yandex/clickhouse-server:19.17.8.54', stay_alive=True, with_installed_binary=True)
node8 = cluster.add_instance('node8', config_dir="configs", with_zookeeper=True)
settings7 = {'index_granularity' : 64, 'index_granularity_bytes' : 10485760}
settings8 = {'index_granularity' : 64, 'index_granularity_bytes' : 10485760, 'min_rows_for_wide_part' : 512, 'min_bytes_for_wide_part' : 0}
@pytest.fixture(scope="module")
def start_cluster_diff_versions():
try:
for name in ['polymorphic_table', 'polymorphic_table_2']:
cluster.start()
node7.query(
'''
CREATE TABLE {name}(date Date, id UInt32, s String, arr Array(Int32))
ENGINE = ReplicatedMergeTree('/clickhouse/tables/test/shard5/{name}', '1')
PARTITION BY toYYYYMM(date)
ORDER BY id
SETTINGS index_granularity = {index_granularity}, index_granularity_bytes = {index_granularity_bytes}
'''.format(name=name, **settings7)
)
node8.query(
'''
CREATE TABLE {name}(date Date, id UInt32, s String, arr Array(Int32))
ENGINE = ReplicatedMergeTree('/clickhouse/tables/test/shard5/{name}', '2')
PARTITION BY toYYYYMM(date)
ORDER BY id
SETTINGS index_granularity = {index_granularity}, index_granularity_bytes = {index_granularity_bytes},
min_rows_for_wide_part = {min_rows_for_wide_part}, min_bytes_for_wide_part = {min_bytes_for_wide_part}
'''.format(name=name, **settings8)
)
yield cluster
finally:
cluster.shutdown()
@pytest.mark.skip(reason="compatability is temporary broken")
def test_polymorphic_parts_diff_versions(start_cluster_diff_versions):
node_old = node7
node_new = node8
insert_random_data('polymorphic_table', node7, 100)
node8.query("SYSTEM SYNC REPLICA polymorphic_table", timeout=20)
assert node8.query("SELECT count() FROM polymorphic_table") == "100\n"
assert node8.query("SELECT DISTINCT part_type FROM system.parts WHERE table = 'polymorphic_table' and active") == "Wide\n"
@pytest.mark.skip(reason="compatability is temporary broken")
def test_polymorphic_parts_diff_versions_2(start_cluster_diff_versions):
# this version doesn't know anything about it. It's considered to be ok.
node_old = node7
node_new = node8
insert_random_data('polymorphic_table_2', node_new, 100)
assert node_new.query("SELECT count() FROM polymorphic_table_2") == "100\n"
assert node_old.query("SELECT count() FROM polymorphic_table_2") == "0\n"
with pytest.raises(Exception):
node_old.query("SYSTEM SYNC REPLICA polymorphic_table_2", timeout=3)
node_old.restart_with_latest_version()
node_old.query("SYSTEM SYNC REPLICA polymorphic_table_2", timeout=20)
# Works after update
assert node_old.query("SELECT count() FROM polymorphic_table_2") == "100\n"
assert node_old.query("SELECT DISTINCT part_type FROM system.parts WHERE table = 'polymorphic_table_2' and active") == "Compact\n"
def test_polymorphic_parts_non_adaptive(start_cluster):
node1.query("SYSTEM STOP MERGES")
node2.query("SYSTEM STOP MERGES")
insert_random_data('non_adaptive_table', node1, 100)
node2.query("SYSTEM SYNC REPLICA non_adaptive_table", timeout=20)
insert_random_data('non_adaptive_table', node2, 100)
node1.query("SYSTEM SYNC REPLICA non_adaptive_table", timeout=20)
assert TSV(node1.query("SELECT part_type, count() FROM system.parts " \
"WHERE table = 'non_adaptive_table' AND active GROUP BY part_type ORDER BY part_type")) == TSV("Wide\t2\n")
assert TSV(node2.query("SELECT part_type, count() FROM system.parts " \
"WHERE table = 'non_adaptive_table' AND active GROUP BY part_type ORDER BY part_type")) == TSV("Wide\t2\n")
assert node1.contains_in_log("<Warning> default.non_adaptive_table: Table can't create parts with adaptive granularity")
| true | true |
f7256dea781fbaf1c92c2fd539b3e3cfbad4cd6e | 214 | py | Python | Lectures/9 - Matlib/test.py | JensRL/PPaNM | a28d9826d24c821cbc35a2e5fb5c478118f1e693 | [
"MIT"
] | null | null | null | Lectures/9 - Matlib/test.py | JensRL/PPaNM | a28d9826d24c821cbc35a2e5fb5c478118f1e693 | [
"MIT"
] | null | null | null | Lectures/9 - Matlib/test.py | JensRL/PPaNM | a28d9826d24c821cbc35a2e5fb5c478118f1e693 | [
"MIT"
] | null | null | null | import math
import scipy.integrate as integrate
ncalls = 0
def f(x):
global ncalls
ncalls +=1
return math.log(x)/math.sqrt(x)
result = integrate.quad(f,0,1)
print("result=", result, "ncalls =",ncalls) | 214 | 214 | 0.682243 | import math
import scipy.integrate as integrate
ncalls = 0
def f(x):
global ncalls
ncalls +=1
return math.log(x)/math.sqrt(x)
result = integrate.quad(f,0,1)
print("result=", result, "ncalls =",ncalls) | true | true |
f7256e40446cfaf2d265b8165ba99a61224d4a30 | 1,336 | py | Python | apache2/htdocs/syntax/string4.py | tigerish009/mampstack-8.0.0-0 | d4d0550e0d29d850ebd9a2b70c3f16641de0e1bf | [
"Apache-2.0"
] | null | null | null | apache2/htdocs/syntax/string4.py | tigerish009/mampstack-8.0.0-0 | d4d0550e0d29d850ebd9a2b70c3f16641de0e1bf | [
"Apache-2.0"
] | null | null | null | apache2/htdocs/syntax/string4.py | tigerish009/mampstack-8.0.0-0 | d4d0550e0d29d850ebd9a2b70c3f16641de0e1bf | [
"Apache-2.0"
] | null | null | null | #positional formatting
print('to {}.Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry sstandard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. {} It was popularised {} in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of {} Lorem Ipsum.'.format('egoing', 12, 'egoing', 'egoing'))
#named placeholder
print('to {name}.Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry sstandard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. {age:d} It was popularised {name} in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of {name} Lorem Ipsum.'.format(name='apple!!!!!!!', age=11111))
| 222.666667 | 653 | 0.797156 |
print('to {}.Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry sstandard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. {} It was popularised {} in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of {} Lorem Ipsum.'.format('egoing', 12, 'egoing', 'egoing'))
print('to {name}.Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry sstandard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, remaining essentially unchanged. {age:d} It was popularised {name} in the 1960s with the release of Letraset sheets containing Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions of {name} Lorem Ipsum.'.format(name='apple!!!!!!!', age=11111))
| true | true |
f7256ea03f1ba9c7d22b3e985d8dc7af2edfcdc4 | 2,525 | py | Python | bentoml/adapters/dataframe_output.py | d3m0n-r00t/BentoML | e5c53b821369f5391de9ab3a20ecad5db9e77202 | [
"Apache-2.0"
] | null | null | null | bentoml/adapters/dataframe_output.py | d3m0n-r00t/BentoML | e5c53b821369f5391de9ab3a20ecad5db9e77202 | [
"Apache-2.0"
] | null | null | null | bentoml/adapters/dataframe_output.py | d3m0n-r00t/BentoML | e5c53b821369f5391de9ab3a20ecad5db9e77202 | [
"Apache-2.0"
] | null | null | null | import json
from typing import Sequence
from bentoml.adapters.json_output import JsonOutput
from bentoml.types import InferenceError, InferenceResult, InferenceTask
from bentoml.utils.dataframe_util import PANDAS_DATAFRAME_TO_JSON_ORIENT_OPTIONS
def df_to_json(result, pandas_dataframe_orient="records"):
import pandas as pd
assert (
pandas_dataframe_orient in PANDAS_DATAFRAME_TO_JSON_ORIENT_OPTIONS
), f"unknown pandas dataframe orient '{pandas_dataframe_orient}'"
if isinstance(result, pd.DataFrame):
return result.to_json(orient=pandas_dataframe_orient)
if isinstance(result, pd.Series):
return pd.DataFrame(result).to_json(orient=pandas_dataframe_orient)
return json.dumps(result)
class DataframeOutput(JsonOutput):
"""
Converts result of user defined API function into specific output.
Args:
cors (str): The value of the Access-Control-Allow-Origin header set in the
AWS Lambda response object. Default is "*". If set to None,
the header will not be set.
"""
BATCH_MODE_SUPPORTED = True
def __init__(self, output_orient='records', **kwargs):
super().__init__(**kwargs)
self.output_orient = output_orient
assert self.output_orient in PANDAS_DATAFRAME_TO_JSON_ORIENT_OPTIONS, (
f"Invalid 'output_orient'='{self.orient}', valid options are "
f"{PANDAS_DATAFRAME_TO_JSON_ORIENT_OPTIONS}"
)
@property
def config(self):
base_config = super(DataframeOutput, self).config
return dict(base_config, output_orient=self.output_orient)
@property
def pip_dependencies(self):
"""
:return: List of PyPI package names required by this OutputAdapter
"""
return ['pandas']
def pack_user_func_return_value(
self, return_result, tasks: Sequence[InferenceTask]
) -> Sequence[InferenceResult[str]]:
rv = []
i = 0
for task in tasks:
if task.batch is None:
result = return_result[i : i + 1]
i += 1
else:
result = return_result[i : i + task.batch]
i += task.batch
try:
result = df_to_json(result, self.output_orient)
rv.append(InferenceResult(http_status=200, data=result))
except Exception as e: # pylint: disable=broad-except
rv.append(InferenceError(err_msg=str(e), http_status=500))
return rv
| 33.666667 | 82 | 0.65901 | import json
from typing import Sequence
from bentoml.adapters.json_output import JsonOutput
from bentoml.types import InferenceError, InferenceResult, InferenceTask
from bentoml.utils.dataframe_util import PANDAS_DATAFRAME_TO_JSON_ORIENT_OPTIONS
def df_to_json(result, pandas_dataframe_orient="records"):
import pandas as pd
assert (
pandas_dataframe_orient in PANDAS_DATAFRAME_TO_JSON_ORIENT_OPTIONS
), f"unknown pandas dataframe orient '{pandas_dataframe_orient}'"
if isinstance(result, pd.DataFrame):
return result.to_json(orient=pandas_dataframe_orient)
if isinstance(result, pd.Series):
return pd.DataFrame(result).to_json(orient=pandas_dataframe_orient)
return json.dumps(result)
class DataframeOutput(JsonOutput):
BATCH_MODE_SUPPORTED = True
def __init__(self, output_orient='records', **kwargs):
super().__init__(**kwargs)
self.output_orient = output_orient
assert self.output_orient in PANDAS_DATAFRAME_TO_JSON_ORIENT_OPTIONS, (
f"Invalid 'output_orient'='{self.orient}', valid options are "
f"{PANDAS_DATAFRAME_TO_JSON_ORIENT_OPTIONS}"
)
@property
def config(self):
base_config = super(DataframeOutput, self).config
return dict(base_config, output_orient=self.output_orient)
@property
def pip_dependencies(self):
return ['pandas']
def pack_user_func_return_value(
self, return_result, tasks: Sequence[InferenceTask]
) -> Sequence[InferenceResult[str]]:
rv = []
i = 0
for task in tasks:
if task.batch is None:
result = return_result[i : i + 1]
i += 1
else:
result = return_result[i : i + task.batch]
i += task.batch
try:
result = df_to_json(result, self.output_orient)
rv.append(InferenceResult(http_status=200, data=result))
except Exception as e:
rv.append(InferenceError(err_msg=str(e), http_status=500))
return rv
| true | true |
f7256eb9c87e025bed52453bc1d07f3c08e79dcc | 1,581 | py | Python | samples/generated_samples/vmmigration_v1_generated_vm_migration_create_source_sync.py | renovate-bot/python-vmmigration | 80a2cf46a21f516899da818a7aec0f2a67222047 | [
"Apache-2.0"
] | null | null | null | samples/generated_samples/vmmigration_v1_generated_vm_migration_create_source_sync.py | renovate-bot/python-vmmigration | 80a2cf46a21f516899da818a7aec0f2a67222047 | [
"Apache-2.0"
] | 10 | 2021-11-18T10:47:48.000Z | 2022-03-07T15:48:54.000Z | samples/generated_samples/vmmigration_v1_generated_vm_migration_create_source_sync.py | renovate-bot/python-vmmigration | 80a2cf46a21f516899da818a7aec0f2a67222047 | [
"Apache-2.0"
] | 1 | 2022-01-29T08:15:02.000Z | 2022-01-29T08:15:02.000Z | # -*- coding: utf-8 -*-
# Copyright 2022 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Generated code. DO NOT EDIT!
#
# Snippet for CreateSource
# NOTE: This snippet has been automatically generated for illustrative purposes only.
# It may require modifications to work in your environment.
# To install the latest published package dependency, execute the following:
# python3 -m pip install google-cloud-vm-migration
# [START vmmigration_v1_generated_VmMigration_CreateSource_sync]
from google.cloud import vmmigration_v1
def sample_create_source():
# Create a client
client = vmmigration_v1.VmMigrationClient()
# Initialize request argument(s)
request = vmmigration_v1.CreateSourceRequest(
parent="parent_value",
source_id="source_id_value",
)
# Make the request
operation = client.create_source(request=request)
print("Waiting for operation to complete...")
response = operation.result()
# Handle the response
print(response)
# [END vmmigration_v1_generated_VmMigration_CreateSource_sync]
| 31 | 85 | 0.752056 |
from google.cloud import vmmigration_v1
def sample_create_source():
client = vmmigration_v1.VmMigrationClient()
request = vmmigration_v1.CreateSourceRequest(
parent="parent_value",
source_id="source_id_value",
)
operation = client.create_source(request=request)
print("Waiting for operation to complete...")
response = operation.result()
print(response)
| true | true |
f7256ed5b108211f2458fd64f6a79da2e72b170a | 62,996 | py | Python | site-packages/sklearn/linear_model/_stochastic_gradient.py | linusg/Pyto | eab3c3e093a8cace53d5b9425d1af2f535d456ee | [
"MIT"
] | 2 | 2020-08-25T13:55:00.000Z | 2020-08-25T16:36:03.000Z | site-packages/sklearn/linear_model/_stochastic_gradient.py | linusg/Pyto | eab3c3e093a8cace53d5b9425d1af2f535d456ee | [
"MIT"
] | 1 | 2020-04-25T20:36:07.000Z | 2020-04-25T20:36:07.000Z | site-packages/sklearn/linear_model/_stochastic_gradient.py | Wristlebane/Pyto | 901ac307b68486d8289105c159ca702318bea5b0 | [
"MIT"
] | null | null | null | # Authors: Peter Prettenhofer <peter.prettenhofer@gmail.com> (main author)
# Mathieu Blondel (partial_fit support)
#
# License: BSD 3 clause
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import warnings
from abc import ABCMeta, abstractmethod
from joblib import Parallel, delayed
from ..base import clone, is_classifier
from ._base import LinearClassifierMixin, SparseCoefMixin
from ._base import make_dataset
from ..base import BaseEstimator, RegressorMixin
from ..utils import check_array, check_random_state, check_X_y
from ..utils.extmath import safe_sparse_dot
from ..utils.multiclass import _check_partial_fit_first_call
from ..utils.validation import check_is_fitted, _check_sample_weight
from ..exceptions import ConvergenceWarning
from ..model_selection import StratifiedShuffleSplit, ShuffleSplit
from ._sgd_fast import plain_sgd, average_sgd
from ..utils import compute_class_weight
from ._sgd_fast import Hinge
from ._sgd_fast import SquaredHinge
from ._sgd_fast import Log
from ._sgd_fast import ModifiedHuber
from ._sgd_fast import SquaredLoss
from ._sgd_fast import Huber
from ._sgd_fast import EpsilonInsensitive
from ._sgd_fast import SquaredEpsilonInsensitive
from ..utils.fixes import _joblib_parallel_args
LEARNING_RATE_TYPES = {"constant": 1, "optimal": 2, "invscaling": 3,
"adaptive": 4, "pa1": 5, "pa2": 6}
PENALTY_TYPES = {"none": 0, "l2": 2, "l1": 1, "elasticnet": 3}
DEFAULT_EPSILON = 0.1
# Default value of ``epsilon`` parameter.
MAX_INT = np.iinfo(np.int32).max
class _ValidationScoreCallback:
"""Callback for early stopping based on validation score"""
def __init__(self, estimator, X_val, y_val, sample_weight_val,
classes=None):
self.estimator = clone(estimator)
self.estimator.t_ = 1 # to pass check_is_fitted
if classes is not None:
self.estimator.classes_ = classes
self.X_val = X_val
self.y_val = y_val
self.sample_weight_val = sample_weight_val
def __call__(self, coef, intercept):
est = self.estimator
est.coef_ = coef.reshape(1, -1)
est.intercept_ = np.atleast_1d(intercept)
return est.score(self.X_val, self.y_val, self.sample_weight_val)
class BaseSGD(SparseCoefMixin, BaseEstimator, metaclass=ABCMeta):
"""Base class for SGD classification and regression."""
def __init__(self, loss, penalty='l2', alpha=0.0001, C=1.0,
l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=1e-3,
shuffle=True, verbose=0, epsilon=0.1, random_state=None,
learning_rate="optimal", eta0=0.0, power_t=0.5,
early_stopping=False, validation_fraction=0.1,
n_iter_no_change=5, warm_start=False, average=False):
self.loss = loss
self.penalty = penalty
self.learning_rate = learning_rate
self.epsilon = epsilon
self.alpha = alpha
self.C = C
self.l1_ratio = l1_ratio
self.fit_intercept = fit_intercept
self.shuffle = shuffle
self.random_state = random_state
self.verbose = verbose
self.eta0 = eta0
self.power_t = power_t
self.early_stopping = early_stopping
self.validation_fraction = validation_fraction
self.n_iter_no_change = n_iter_no_change
self.warm_start = warm_start
self.average = average
self.max_iter = max_iter
self.tol = tol
# current tests expect init to do parameter validation
# but we are not allowed to set attributes
self._validate_params()
def set_params(self, **kwargs):
super().set_params(**kwargs)
self._validate_params()
return self
@abstractmethod
def fit(self, X, y):
"""Fit model."""
def _validate_params(self, for_partial_fit=False):
"""Validate input params. """
if not isinstance(self.shuffle, bool):
raise ValueError("shuffle must be either True or False")
if not isinstance(self.early_stopping, bool):
raise ValueError("early_stopping must be either True or False")
if self.early_stopping and for_partial_fit:
raise ValueError("early_stopping should be False with partial_fit")
if self.max_iter is not None and self.max_iter <= 0:
raise ValueError("max_iter must be > zero. Got %f" % self.max_iter)
if not (0.0 <= self.l1_ratio <= 1.0):
raise ValueError("l1_ratio must be in [0, 1]")
if self.alpha < 0.0:
raise ValueError("alpha must be >= 0")
if self.n_iter_no_change < 1:
raise ValueError("n_iter_no_change must be >= 1")
if not (0.0 < self.validation_fraction < 1.0):
raise ValueError("validation_fraction must be in range (0, 1)")
if self.learning_rate in ("constant", "invscaling", "adaptive"):
if self.eta0 <= 0.0:
raise ValueError("eta0 must be > 0")
if self.learning_rate == "optimal" and self.alpha == 0:
raise ValueError("alpha must be > 0 since "
"learning_rate is 'optimal'. alpha is used "
"to compute the optimal learning rate.")
# raises ValueError if not registered
self._get_penalty_type(self.penalty)
self._get_learning_rate_type(self.learning_rate)
if self.loss not in self.loss_functions:
raise ValueError("The loss %s is not supported. " % self.loss)
def _get_loss_function(self, loss):
"""Get concrete ``LossFunction`` object for str ``loss``. """
try:
loss_ = self.loss_functions[loss]
loss_class, args = loss_[0], loss_[1:]
if loss in ('huber', 'epsilon_insensitive',
'squared_epsilon_insensitive'):
args = (self.epsilon, )
return loss_class(*args)
except KeyError:
raise ValueError("The loss %s is not supported. " % loss)
def _get_learning_rate_type(self, learning_rate):
try:
return LEARNING_RATE_TYPES[learning_rate]
except KeyError:
raise ValueError("learning rate %s "
"is not supported. " % learning_rate)
def _get_penalty_type(self, penalty):
penalty = str(penalty).lower()
try:
return PENALTY_TYPES[penalty]
except KeyError:
raise ValueError("Penalty %s is not supported. " % penalty)
def _allocate_parameter_mem(self, n_classes, n_features, coef_init=None,
intercept_init=None):
"""Allocate mem for parameters; initialize if provided."""
if n_classes > 2:
# allocate coef_ for multi-class
if coef_init is not None:
coef_init = np.asarray(coef_init, order="C")
if coef_init.shape != (n_classes, n_features):
raise ValueError("Provided ``coef_`` does not match "
"dataset. ")
self.coef_ = coef_init
else:
self.coef_ = np.zeros((n_classes, n_features),
dtype=np.float64, order="C")
# allocate intercept_ for multi-class
if intercept_init is not None:
intercept_init = np.asarray(intercept_init, order="C")
if intercept_init.shape != (n_classes, ):
raise ValueError("Provided intercept_init "
"does not match dataset.")
self.intercept_ = intercept_init
else:
self.intercept_ = np.zeros(n_classes, dtype=np.float64,
order="C")
else:
# allocate coef_ for binary problem
if coef_init is not None:
coef_init = np.asarray(coef_init, dtype=np.float64,
order="C")
coef_init = coef_init.ravel()
if coef_init.shape != (n_features,):
raise ValueError("Provided coef_init does not "
"match dataset.")
self.coef_ = coef_init
else:
self.coef_ = np.zeros(n_features,
dtype=np.float64,
order="C")
# allocate intercept_ for binary problem
if intercept_init is not None:
intercept_init = np.asarray(intercept_init, dtype=np.float64)
if intercept_init.shape != (1,) and intercept_init.shape != ():
raise ValueError("Provided intercept_init "
"does not match dataset.")
self.intercept_ = intercept_init.reshape(1,)
else:
self.intercept_ = np.zeros(1, dtype=np.float64, order="C")
# initialize average parameters
if self.average > 0:
self.standard_coef_ = self.coef_
self.standard_intercept_ = self.intercept_
self.average_coef_ = np.zeros(self.coef_.shape,
dtype=np.float64,
order="C")
self.average_intercept_ = np.zeros(self.standard_intercept_.shape,
dtype=np.float64,
order="C")
def _make_validation_split(self, y):
"""Split the dataset between training set and validation set.
Parameters
----------
y : array, shape (n_samples, )
Target values.
Returns
-------
validation_mask : array, shape (n_samples, )
Equal to 1 on the validation set, 0 on the training set.
"""
n_samples = y.shape[0]
validation_mask = np.zeros(n_samples, dtype=np.uint8)
if not self.early_stopping:
# use the full set for training, with an empty validation set
return validation_mask
if is_classifier(self):
splitter_type = StratifiedShuffleSplit
else:
splitter_type = ShuffleSplit
cv = splitter_type(test_size=self.validation_fraction,
random_state=self.random_state)
idx_train, idx_val = next(cv.split(np.zeros(shape=(y.shape[0], 1)), y))
if idx_train.shape[0] == 0 or idx_val.shape[0] == 0:
raise ValueError(
"Splitting %d samples into a train set and a validation set "
"with validation_fraction=%r led to an empty set (%d and %d "
"samples). Please either change validation_fraction, increase "
"number of samples, or disable early_stopping."
% (n_samples, self.validation_fraction, idx_train.shape[0],
idx_val.shape[0]))
validation_mask[idx_val] = 1
return validation_mask
def _make_validation_score_cb(self, validation_mask, X, y, sample_weight,
classes=None):
if not self.early_stopping:
return None
return _ValidationScoreCallback(
self, X[validation_mask], y[validation_mask],
sample_weight[validation_mask], classes=classes)
def _prepare_fit_binary(est, y, i):
"""Initialization for fit_binary.
Returns y, coef, intercept, average_coef, average_intercept.
"""
y_i = np.ones(y.shape, dtype=np.float64, order="C")
y_i[y != est.classes_[i]] = -1.0
average_intercept = 0
average_coef = None
if len(est.classes_) == 2:
if not est.average:
coef = est.coef_.ravel()
intercept = est.intercept_[0]
else:
coef = est.standard_coef_.ravel()
intercept = est.standard_intercept_[0]
average_coef = est.average_coef_.ravel()
average_intercept = est.average_intercept_[0]
else:
if not est.average:
coef = est.coef_[i]
intercept = est.intercept_[i]
else:
coef = est.standard_coef_[i]
intercept = est.standard_intercept_[i]
average_coef = est.average_coef_[i]
average_intercept = est.average_intercept_[i]
return y_i, coef, intercept, average_coef, average_intercept
def fit_binary(est, i, X, y, alpha, C, learning_rate, max_iter,
pos_weight, neg_weight, sample_weight, validation_mask=None,
random_state=None):
"""Fit a single binary classifier.
The i'th class is considered the "positive" class.
Parameters
----------
est : Estimator object
The estimator to fit
i : int
Index of the positive class
X : numpy array or sparse matrix of shape [n_samples,n_features]
Training data
y : numpy array of shape [n_samples, ]
Target values
alpha : float
The regularization parameter
C : float
Maximum step size for passive aggressive
learning_rate : string
The learning rate. Accepted values are 'constant', 'optimal',
'invscaling', 'pa1' and 'pa2'.
max_iter : int
The maximum number of iterations (epochs)
pos_weight : float
The weight of the positive class
neg_weight : float
The weight of the negative class
sample_weight : numpy array of shape [n_samples, ]
The weight of each sample
validation_mask : numpy array of shape [n_samples, ] or None
Precomputed validation mask in case _fit_binary is called in the
context of a one-vs-rest reduction.
random_state : int, RandomState instance or None, optional (default=None)
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`.
"""
# if average is not true, average_coef, and average_intercept will be
# unused
y_i, coef, intercept, average_coef, average_intercept = \
_prepare_fit_binary(est, y, i)
assert y_i.shape[0] == y.shape[0] == sample_weight.shape[0]
random_state = check_random_state(random_state)
dataset, intercept_decay = make_dataset(
X, y_i, sample_weight, random_state=random_state)
penalty_type = est._get_penalty_type(est.penalty)
learning_rate_type = est._get_learning_rate_type(learning_rate)
if validation_mask is None:
validation_mask = est._make_validation_split(y_i)
classes = np.array([-1, 1], dtype=y_i.dtype)
validation_score_cb = est._make_validation_score_cb(
validation_mask, X, y_i, sample_weight, classes=classes)
# numpy mtrand expects a C long which is a signed 32 bit integer under
# Windows
seed = random_state.randint(MAX_INT)
tol = est.tol if est.tol is not None else -np.inf
if not est.average:
result = plain_sgd(coef, intercept, est.loss_function_,
penalty_type, alpha, C, est.l1_ratio,
dataset, validation_mask, est.early_stopping,
validation_score_cb, int(est.n_iter_no_change),
max_iter, tol, int(est.fit_intercept),
int(est.verbose), int(est.shuffle), seed,
pos_weight, neg_weight,
learning_rate_type, est.eta0,
est.power_t, est.t_, intercept_decay)
else:
standard_coef, standard_intercept, average_coef, average_intercept, \
n_iter_ = average_sgd(coef, intercept, average_coef,
average_intercept, est.loss_function_,
penalty_type, alpha, C, est.l1_ratio,
dataset, validation_mask, est.early_stopping,
validation_score_cb,
int(est.n_iter_no_change), max_iter, tol,
int(est.fit_intercept), int(est.verbose),
int(est.shuffle), seed, pos_weight,
neg_weight, learning_rate_type, est.eta0,
est.power_t, est.t_, intercept_decay,
est.average)
if len(est.classes_) == 2:
est.average_intercept_[0] = average_intercept
else:
est.average_intercept_[i] = average_intercept
result = standard_coef, standard_intercept, n_iter_
return result
class BaseSGDClassifier(LinearClassifierMixin, BaseSGD, metaclass=ABCMeta):
loss_functions = {
"hinge": (Hinge, 1.0),
"squared_hinge": (SquaredHinge, 1.0),
"perceptron": (Hinge, 0.0),
"log": (Log, ),
"modified_huber": (ModifiedHuber, ),
"squared_loss": (SquaredLoss, ),
"huber": (Huber, DEFAULT_EPSILON),
"epsilon_insensitive": (EpsilonInsensitive, DEFAULT_EPSILON),
"squared_epsilon_insensitive": (SquaredEpsilonInsensitive,
DEFAULT_EPSILON),
}
@abstractmethod
def __init__(self, loss="hinge", penalty='l2', alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=1e-3,
shuffle=True, verbose=0, epsilon=DEFAULT_EPSILON, n_jobs=None,
random_state=None, learning_rate="optimal", eta0=0.0,
power_t=0.5, early_stopping=False,
validation_fraction=0.1, n_iter_no_change=5,
class_weight=None, warm_start=False, average=False):
super().__init__(
loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,
shuffle=shuffle, verbose=verbose, epsilon=epsilon,
random_state=random_state, learning_rate=learning_rate, eta0=eta0,
power_t=power_t, early_stopping=early_stopping,
validation_fraction=validation_fraction,
n_iter_no_change=n_iter_no_change, warm_start=warm_start,
average=average)
self.class_weight = class_weight
self.n_jobs = n_jobs
def _partial_fit(self, X, y, alpha, C,
loss, learning_rate, max_iter,
classes, sample_weight,
coef_init, intercept_init):
X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C",
accept_large_sparse=False)
n_samples, n_features = X.shape
_check_partial_fit_first_call(self, classes)
n_classes = self.classes_.shape[0]
# Allocate datastructures from input arguments
self._expanded_class_weight = compute_class_weight(self.class_weight,
self.classes_, y)
sample_weight = _check_sample_weight(sample_weight, X)
if getattr(self, "coef_", None) is None or coef_init is not None:
self._allocate_parameter_mem(n_classes, n_features,
coef_init, intercept_init)
elif n_features != self.coef_.shape[-1]:
raise ValueError("Number of features %d does not match previous "
"data %d." % (n_features, self.coef_.shape[-1]))
self.loss_function_ = self._get_loss_function(loss)
if not hasattr(self, "t_"):
self.t_ = 1.0
# delegate to concrete training procedure
if n_classes > 2:
self._fit_multiclass(X, y, alpha=alpha, C=C,
learning_rate=learning_rate,
sample_weight=sample_weight,
max_iter=max_iter)
elif n_classes == 2:
self._fit_binary(X, y, alpha=alpha, C=C,
learning_rate=learning_rate,
sample_weight=sample_weight,
max_iter=max_iter)
else:
raise ValueError(
"The number of classes has to be greater than one;"
" got %d class" % n_classes)
return self
def _fit(self, X, y, alpha, C, loss, learning_rate, coef_init=None,
intercept_init=None, sample_weight=None):
self._validate_params()
if hasattr(self, "classes_"):
self.classes_ = None
X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C",
accept_large_sparse=False)
# labels can be encoded as float, int, or string literals
# np.unique sorts in asc order; largest class id is positive class
classes = np.unique(y)
if self.warm_start and hasattr(self, "coef_"):
if coef_init is None:
coef_init = self.coef_
if intercept_init is None:
intercept_init = self.intercept_
else:
self.coef_ = None
self.intercept_ = None
if self.average > 0:
self.standard_coef_ = self.coef_
self.standard_intercept_ = self.intercept_
self.average_coef_ = None
self.average_intercept_ = None
# Clear iteration count for multiple call to fit.
self.t_ = 1.0
self._partial_fit(X, y, alpha, C, loss, learning_rate, self.max_iter,
classes, sample_weight, coef_init, intercept_init)
if (self.tol is not None and self.tol > -np.inf
and self.n_iter_ == self.max_iter):
warnings.warn("Maximum number of iteration reached before "
"convergence. Consider increasing max_iter to "
"improve the fit.",
ConvergenceWarning)
return self
def _fit_binary(self, X, y, alpha, C, sample_weight,
learning_rate, max_iter):
"""Fit a binary classifier on X and y. """
coef, intercept, n_iter_ = fit_binary(self, 1, X, y, alpha, C,
learning_rate, max_iter,
self._expanded_class_weight[1],
self._expanded_class_weight[0],
sample_weight,
random_state=self.random_state)
self.t_ += n_iter_ * X.shape[0]
self.n_iter_ = n_iter_
# need to be 2d
if self.average > 0:
if self.average <= self.t_ - 1:
self.coef_ = self.average_coef_.reshape(1, -1)
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_.reshape(1, -1)
self.standard_intercept_ = np.atleast_1d(intercept)
self.intercept_ = self.standard_intercept_
else:
self.coef_ = coef.reshape(1, -1)
# intercept is a float, need to convert it to an array of length 1
self.intercept_ = np.atleast_1d(intercept)
def _fit_multiclass(self, X, y, alpha, C, learning_rate,
sample_weight, max_iter):
"""Fit a multi-class classifier by combining binary classifiers
Each binary classifier predicts one class versus all others. This
strategy is called OvA (One versus All) or OvR (One versus Rest).
"""
# Precompute the validation split using the multiclass labels
# to ensure proper balancing of the classes.
validation_mask = self._make_validation_split(y)
# Use joblib to fit OvA in parallel.
# Pick the random seed for each job outside of fit_binary to avoid
# sharing the estimator random state between threads which could lead
# to non-deterministic behavior
random_state = check_random_state(self.random_state)
seeds = random_state.randint(MAX_INT, size=len(self.classes_))
result = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
**_joblib_parallel_args(require="sharedmem"))(
delayed(fit_binary)(self, i, X, y, alpha, C, learning_rate,
max_iter, self._expanded_class_weight[i],
1., sample_weight,
validation_mask=validation_mask,
random_state=seed)
for i, seed in enumerate(seeds))
# take the maximum of n_iter_ over every binary fit
n_iter_ = 0.
for i, (_, intercept, n_iter_i) in enumerate(result):
self.intercept_[i] = intercept
n_iter_ = max(n_iter_, n_iter_i)
self.t_ += n_iter_ * X.shape[0]
self.n_iter_ = n_iter_
if self.average > 0:
if self.average <= self.t_ - 1.0:
self.coef_ = self.average_coef_
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_
self.standard_intercept_ = np.atleast_1d(self.intercept_)
self.intercept_ = self.standard_intercept_
def partial_fit(self, X, y, classes=None, sample_weight=None):
"""Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses ``max_iter = 1``. Therefore, it is not
guaranteed that a minimum of the cost function is reached after calling
it once. Matters such as objective convergence and early stopping
should be handled by the user.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Subset of the training data
y : numpy array, shape (n_samples,)
Subset of the target values
classes : array, shape (n_classes,)
Classes across all calls to partial_fit.
Can be obtained by via `np.unique(y_all)`, where y_all is the
target vector of the entire dataset.
This argument is required for the first call to partial_fit
and can be omitted in the subsequent calls.
Note that y doesn't need to contain all labels in `classes`.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples.
If not provided, uniform weights are assumed.
Returns
-------
self : returns an instance of self.
"""
self._validate_params(for_partial_fit=True)
if self.class_weight in ['balanced']:
raise ValueError("class_weight '{0}' is not supported for "
"partial_fit. In order to use 'balanced' weights,"
" use compute_class_weight('{0}', classes, y). "
"In place of y you can us a large enough sample "
"of the full training set target to properly "
"estimate the class frequency distributions. "
"Pass the resulting weights as the class_weight "
"parameter.".format(self.class_weight))
return self._partial_fit(X, y, alpha=self.alpha, C=1.0, loss=self.loss,
learning_rate=self.learning_rate, max_iter=1,
classes=classes, sample_weight=sample_weight,
coef_init=None, intercept_init=None)
def fit(self, X, y, coef_init=None, intercept_init=None,
sample_weight=None):
"""Fit linear model with Stochastic Gradient Descent.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data
y : numpy array, shape (n_samples,)
Target values
coef_init : array, shape (n_classes, n_features)
The initial coefficients to warm-start the optimization.
intercept_init : array, shape (n_classes,)
The initial intercept to warm-start the optimization.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples.
If not provided, uniform weights are assumed. These weights will
be multiplied with class_weight (passed through the
constructor) if class_weight is specified
Returns
-------
self : returns an instance of self.
"""
return self._fit(X, y, alpha=self.alpha, C=1.0,
loss=self.loss, learning_rate=self.learning_rate,
coef_init=coef_init, intercept_init=intercept_init,
sample_weight=sample_weight)
class SGDClassifier(BaseSGDClassifier):
"""Linear classifiers (SVM, logistic regression, a.o.) with SGD training.
This estimator implements regularized linear models with stochastic
gradient descent (SGD) learning: the gradient of the loss is estimated
each sample at a time and the model is updated along the way with a
decreasing strength schedule (aka learning rate). SGD allows minibatch
(online/out-of-core) learning, see the partial_fit method.
For best results using the default learning rate schedule, the data should
have zero mean and unit variance.
This implementation works with data represented as dense or sparse arrays
of floating point values for the features. The model it fits can be
controlled with the loss parameter; by default, it fits a linear support
vector machine (SVM).
The regularizer is a penalty added to the loss function that shrinks model
parameters towards the zero vector using either the squared euclidean norm
L2 or the absolute norm L1 or a combination of both (Elastic Net). If the
parameter update crosses the 0.0 value because of the regularizer, the
update is truncated to 0.0 to allow for learning sparse models and achieve
online feature selection.
Read more in the :ref:`User Guide <sgd>`.
Parameters
----------
loss : str, default: 'hinge'
The loss function to be used. Defaults to 'hinge', which gives a
linear SVM.
The possible options are 'hinge', 'log', 'modified_huber',
'squared_hinge', 'perceptron', or a regression loss: 'squared_loss',
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'.
The 'log' loss gives logistic regression, a probabilistic classifier.
'modified_huber' is another smooth loss that brings tolerance to
outliers as well as probability estimates.
'squared_hinge' is like hinge but is quadratically penalized.
'perceptron' is the linear loss used by the perceptron algorithm.
The other losses are designed for regression but can be useful in
classification as well; see SGDRegressor for a description.
penalty : str, 'none', 'l2', 'l1', or 'elasticnet'
The penalty (aka regularization term) to be used. Defaults to 'l2'
which is the standard regularizer for linear SVM models. 'l1' and
'elasticnet' might bring sparsity to the model (feature selection)
not achievable with 'l2'.
alpha : float
Constant that multiplies the regularization term. Defaults to 0.0001.
Also used to compute learning_rate when set to 'optimal'.
l1_ratio : float
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
Defaults to 0.15.
fit_intercept : bool
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True.
max_iter : int, optional (default=1000)
The maximum number of passes over the training data (aka epochs).
It only impacts the behavior in the ``fit`` method, and not the
:meth:`partial_fit` method.
.. versionadded:: 0.19
tol : float or None, optional (default=1e-3)
The stopping criterion. If it is not None, the iterations will stop
when (loss > best_loss - tol) for ``n_iter_no_change`` consecutive
epochs.
.. versionadded:: 0.19
shuffle : bool, optional
Whether or not the training data should be shuffled after each epoch.
Defaults to True.
verbose : integer, default=0
The verbosity level
epsilon : float, default=0.1
Epsilon in the epsilon-insensitive loss functions; only if `loss` is
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'.
For 'huber', determines the threshold at which it becomes less
important to get the prediction exactly right.
For epsilon-insensitive, any differences between the current prediction
and the correct label are ignored if they are less than this threshold.
n_jobs : int or None, optional (default=None)
The number of CPUs to use to do the OVA (One Versus All, for
multi-class problems) computation.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details.
random_state : int, RandomState instance or None, optional (default=None)
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`.
learning_rate : string, optional
The learning rate schedule:
'constant':
eta = eta0
'optimal': [default]
eta = 1.0 / (alpha * (t + t0))
where t0 is chosen by a heuristic proposed by Leon Bottou.
'invscaling':
eta = eta0 / pow(t, power_t)
'adaptive':
eta = eta0, as long as the training keeps decreasing.
Each time n_iter_no_change consecutive epochs fail to decrease the
training loss by tol or fail to increase validation score by tol if
early_stopping is True, the current learning rate is divided by 5.
eta0 : double
The initial learning rate for the 'constant', 'invscaling' or
'adaptive' schedules. The default value is 0.0 as eta0 is not used by
the default schedule 'optimal'.
power_t : double
The exponent for inverse scaling learning rate [default 0.5].
early_stopping : bool, default=False
Whether to use early stopping to terminate training when validation
score is not improving. If set to True, it will automatically set aside
a stratified fraction of training data as validation and terminate
training when validation score is not improving by at least tol for
n_iter_no_change consecutive epochs.
.. versionadded:: 0.20
validation_fraction : float, default=0.1
The proportion of training data to set aside as validation set for
early stopping. Must be between 0 and 1.
Only used if early_stopping is True.
.. versionadded:: 0.20
n_iter_no_change : int, default=5
Number of iterations with no improvement to wait before early stopping.
.. versionadded:: 0.20
class_weight : dict, {class_label: weight} or "balanced" or None, optional
Preset for the class_weight fit parameter.
Weights associated with classes. If not given, all classes
are supposed to have weight one.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
warm_start : bool, default=False
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
See :term:`the Glossary <warm_start>`.
Repeatedly calling fit or partial_fit when warm_start is True can
result in a different solution than when calling fit a single time
because of the way the data is shuffled.
If a dynamic learning rate is used, the learning rate is adapted
depending on the number of samples already seen. Calling ``fit`` resets
this counter, while ``partial_fit`` will result in increasing the
existing counter.
average : bool or int, default=False
When set to True, computes the averaged SGD weights and stores the
result in the ``coef_`` attribute. If set to an int greater than 1,
averaging will begin once the total number of samples seen reaches
average. So ``average=10`` will begin averaging after seeing 10
samples.
Attributes
----------
coef_ : array, shape (1, n_features) if n_classes == 2 else (n_classes,\
n_features)
Weights assigned to the features.
intercept_ : array, shape (1,) if n_classes == 2 else (n_classes,)
Constants in decision function.
n_iter_ : int
The actual number of iterations to reach the stopping criterion.
For multiclass fits, it is the maximum over every binary fit.
loss_function_ : concrete ``LossFunction``
classes_ : array of shape (n_classes,)
t_ : int
Number of weight updates performed during training.
Same as ``(n_iter_ * n_samples)``.
Examples
--------
>>> import numpy as np
>>> from sklearn import linear_model
>>> X = np.array([[-1, -1], [-2, -1], [1, 1], [2, 1]])
>>> Y = np.array([1, 1, 2, 2])
>>> clf = linear_model.SGDClassifier(max_iter=1000, tol=1e-3)
>>> clf.fit(X, Y)
SGDClassifier()
>>> print(clf.predict([[-0.8, -1]]))
[1]
See also
--------
sklearn.svm.LinearSVC, LogisticRegression, Perceptron
"""
def __init__(self, loss="hinge", penalty='l2', alpha=0.0001, l1_ratio=0.15,
fit_intercept=True, max_iter=1000, tol=1e-3, shuffle=True,
verbose=0, epsilon=DEFAULT_EPSILON, n_jobs=None,
random_state=None, learning_rate="optimal", eta0=0.0,
power_t=0.5, early_stopping=False, validation_fraction=0.1,
n_iter_no_change=5, class_weight=None, warm_start=False,
average=False):
super().__init__(
loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,
shuffle=shuffle, verbose=verbose, epsilon=epsilon, n_jobs=n_jobs,
random_state=random_state, learning_rate=learning_rate, eta0=eta0,
power_t=power_t, early_stopping=early_stopping,
validation_fraction=validation_fraction,
n_iter_no_change=n_iter_no_change, class_weight=class_weight,
warm_start=warm_start, average=average)
def _check_proba(self):
if self.loss not in ("log", "modified_huber"):
raise AttributeError("probability estimates are not available for"
" loss=%r" % self.loss)
@property
def predict_proba(self):
"""Probability estimates.
This method is only available for log loss and modified Huber loss.
Multiclass probability estimates are derived from binary (one-vs.-rest)
estimates by simple normalization, as recommended by Zadrozny and
Elkan.
Binary probability estimates for loss="modified_huber" are given by
(clip(decision_function(X), -1, 1) + 1) / 2. For other loss functions
it is necessary to perform proper probability calibration by wrapping
the classifier with
:class:`sklearn.calibration.CalibratedClassifierCV` instead.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
-------
array, shape (n_samples, n_classes)
Returns the probability of the sample for each class in the model,
where classes are ordered as they are in `self.classes_`.
References
----------
Zadrozny and Elkan, "Transforming classifier scores into multiclass
probability estimates", SIGKDD'02,
http://www.research.ibm.com/people/z/zadrozny/kdd2002-Transf.pdf
The justification for the formula in the loss="modified_huber"
case is in the appendix B in:
http://jmlr.csail.mit.edu/papers/volume2/zhang02c/zhang02c.pdf
"""
self._check_proba()
return self._predict_proba
def _predict_proba(self, X):
check_is_fitted(self)
if self.loss == "log":
return self._predict_proba_lr(X)
elif self.loss == "modified_huber":
binary = (len(self.classes_) == 2)
scores = self.decision_function(X)
if binary:
prob2 = np.ones((scores.shape[0], 2))
prob = prob2[:, 1]
else:
prob = scores
np.clip(scores, -1, 1, prob)
prob += 1.
prob /= 2.
if binary:
prob2[:, 0] -= prob
prob = prob2
else:
# the above might assign zero to all classes, which doesn't
# normalize neatly; work around this to produce uniform
# probabilities
prob_sum = prob.sum(axis=1)
all_zero = (prob_sum == 0)
if np.any(all_zero):
prob[all_zero, :] = 1
prob_sum[all_zero] = len(self.classes_)
# normalize
prob /= prob_sum.reshape((prob.shape[0], -1))
return prob
else:
raise NotImplementedError("predict_(log_)proba only supported when"
" loss='log' or loss='modified_huber' "
"(%r given)" % self.loss)
@property
def predict_log_proba(self):
"""Log of probability estimates.
This method is only available for log loss and modified Huber loss.
When loss="modified_huber", probability estimates may be hard zeros
and ones, so taking the logarithm is not possible.
See ``predict_proba`` for details.
Parameters
----------
X : array-like, shape (n_samples, n_features)
Returns
-------
T : array-like, shape (n_samples, n_classes)
Returns the log-probability of the sample for each class in the
model, where classes are ordered as they are in
`self.classes_`.
"""
self._check_proba()
return self._predict_log_proba
def _predict_log_proba(self, X):
return np.log(self.predict_proba(X))
class BaseSGDRegressor(RegressorMixin, BaseSGD):
loss_functions = {
"squared_loss": (SquaredLoss, ),
"huber": (Huber, DEFAULT_EPSILON),
"epsilon_insensitive": (EpsilonInsensitive, DEFAULT_EPSILON),
"squared_epsilon_insensitive": (SquaredEpsilonInsensitive,
DEFAULT_EPSILON),
}
@abstractmethod
def __init__(self, loss="squared_loss", penalty="l2", alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=1e-3,
shuffle=True, verbose=0, epsilon=DEFAULT_EPSILON,
random_state=None, learning_rate="invscaling", eta0=0.01,
power_t=0.25, early_stopping=False, validation_fraction=0.1,
n_iter_no_change=5, warm_start=False, average=False):
super().__init__(
loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,
shuffle=shuffle, verbose=verbose, epsilon=epsilon,
random_state=random_state, learning_rate=learning_rate, eta0=eta0,
power_t=power_t, early_stopping=early_stopping,
validation_fraction=validation_fraction,
n_iter_no_change=n_iter_no_change, warm_start=warm_start,
average=average)
def _partial_fit(self, X, y, alpha, C, loss, learning_rate,
max_iter, sample_weight, coef_init, intercept_init):
X, y = check_X_y(X, y, "csr", copy=False, order='C', dtype=np.float64,
accept_large_sparse=False)
y = y.astype(np.float64, copy=False)
n_samples, n_features = X.shape
sample_weight = _check_sample_weight(sample_weight, X)
# Allocate datastructures from input arguments
if getattr(self, "coef_", None) is None:
self._allocate_parameter_mem(1, n_features, coef_init,
intercept_init)
elif n_features != self.coef_.shape[-1]:
raise ValueError("Number of features %d does not match previous "
"data %d." % (n_features, self.coef_.shape[-1]))
if self.average > 0 and getattr(self, "average_coef_", None) is None:
self.average_coef_ = np.zeros(n_features,
dtype=np.float64,
order="C")
self.average_intercept_ = np.zeros(1, dtype=np.float64, order="C")
self._fit_regressor(X, y, alpha, C, loss, learning_rate,
sample_weight, max_iter)
return self
def partial_fit(self, X, y, sample_weight=None):
"""Perform one epoch of stochastic gradient descent on given samples.
Internally, this method uses ``max_iter = 1``. Therefore, it is not
guaranteed that a minimum of the cost function is reached after calling
it once. Matters such as objective convergence and early stopping
should be handled by the user.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Subset of training data
y : numpy array of shape (n_samples,)
Subset of target values
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples.
If not provided, uniform weights are assumed.
Returns
-------
self : returns an instance of self.
"""
self._validate_params(for_partial_fit=True)
return self._partial_fit(X, y, self.alpha, C=1.0,
loss=self.loss,
learning_rate=self.learning_rate, max_iter=1,
sample_weight=sample_weight, coef_init=None,
intercept_init=None)
def _fit(self, X, y, alpha, C, loss, learning_rate, coef_init=None,
intercept_init=None, sample_weight=None):
self._validate_params()
if self.warm_start and getattr(self, "coef_", None) is not None:
if coef_init is None:
coef_init = self.coef_
if intercept_init is None:
intercept_init = self.intercept_
else:
self.coef_ = None
self.intercept_ = None
if self.average > 0:
self.standard_intercept_ = self.intercept_
self.standard_coef_ = self.coef_
self.average_coef_ = None
self.average_intercept_ = None
# Clear iteration count for multiple call to fit.
self.t_ = 1.0
self._partial_fit(X, y, alpha, C, loss, learning_rate,
self.max_iter, sample_weight, coef_init,
intercept_init)
if (self.tol is not None and self.tol > -np.inf
and self.n_iter_ == self.max_iter):
warnings.warn("Maximum number of iteration reached before "
"convergence. Consider increasing max_iter to "
"improve the fit.",
ConvergenceWarning)
return self
def fit(self, X, y, coef_init=None, intercept_init=None,
sample_weight=None):
"""Fit linear model with Stochastic Gradient Descent.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data
y : numpy array, shape (n_samples,)
Target values
coef_init : array, shape (n_features,)
The initial coefficients to warm-start the optimization.
intercept_init : array, shape (1,)
The initial intercept to warm-start the optimization.
sample_weight : array-like, shape (n_samples,), optional
Weights applied to individual samples (1. for unweighted).
Returns
-------
self : returns an instance of self.
"""
return self._fit(X, y, alpha=self.alpha, C=1.0,
loss=self.loss, learning_rate=self.learning_rate,
coef_init=coef_init,
intercept_init=intercept_init,
sample_weight=sample_weight)
def _decision_function(self, X):
"""Predict using the linear model
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
-------
array, shape (n_samples,)
Predicted target values per element in X.
"""
check_is_fitted(self)
X = check_array(X, accept_sparse='csr')
scores = safe_sparse_dot(X, self.coef_.T,
dense_output=True) + self.intercept_
return scores.ravel()
def predict(self, X):
"""Predict using the linear model
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Returns
-------
array, shape (n_samples,)
Predicted target values per element in X.
"""
return self._decision_function(X)
def _fit_regressor(self, X, y, alpha, C, loss, learning_rate,
sample_weight, max_iter):
dataset, intercept_decay = make_dataset(X, y, sample_weight)
loss_function = self._get_loss_function(loss)
penalty_type = self._get_penalty_type(self.penalty)
learning_rate_type = self._get_learning_rate_type(learning_rate)
if not hasattr(self, "t_"):
self.t_ = 1.0
validation_mask = self._make_validation_split(y)
validation_score_cb = self._make_validation_score_cb(
validation_mask, X, y, sample_weight)
random_state = check_random_state(self.random_state)
# numpy mtrand expects a C long which is a signed 32 bit integer under
# Windows
seed = random_state.randint(0, np.iinfo(np.int32).max)
tol = self.tol if self.tol is not None else -np.inf
if self.average > 0:
self.standard_coef_, self.standard_intercept_, \
self.average_coef_, self.average_intercept_, self.n_iter_ =\
average_sgd(self.standard_coef_,
self.standard_intercept_[0],
self.average_coef_,
self.average_intercept_[0],
loss_function,
penalty_type,
alpha, C,
self.l1_ratio,
dataset,
validation_mask, self.early_stopping,
validation_score_cb,
int(self.n_iter_no_change),
max_iter, tol,
int(self.fit_intercept),
int(self.verbose),
int(self.shuffle),
seed,
1.0, 1.0,
learning_rate_type,
self.eta0, self.power_t, self.t_,
intercept_decay, self.average)
self.average_intercept_ = np.atleast_1d(self.average_intercept_)
self.standard_intercept_ = np.atleast_1d(self.standard_intercept_)
self.t_ += self.n_iter_ * X.shape[0]
if self.average <= self.t_ - 1.0:
self.coef_ = self.average_coef_
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_
self.intercept_ = self.standard_intercept_
else:
self.coef_, self.intercept_, self.n_iter_ = \
plain_sgd(self.coef_,
self.intercept_[0],
loss_function,
penalty_type,
alpha, C,
self.l1_ratio,
dataset,
validation_mask, self.early_stopping,
validation_score_cb,
int(self.n_iter_no_change),
max_iter, tol,
int(self.fit_intercept),
int(self.verbose),
int(self.shuffle),
seed,
1.0, 1.0,
learning_rate_type,
self.eta0, self.power_t, self.t_,
intercept_decay)
self.t_ += self.n_iter_ * X.shape[0]
self.intercept_ = np.atleast_1d(self.intercept_)
class SGDRegressor(BaseSGDRegressor):
"""Linear model fitted by minimizing a regularized empirical loss with SGD
SGD stands for Stochastic Gradient Descent: the gradient of the loss is
estimated each sample at a time and the model is updated along the way with
a decreasing strength schedule (aka learning rate).
The regularizer is a penalty added to the loss function that shrinks model
parameters towards the zero vector using either the squared euclidean norm
L2 or the absolute norm L1 or a combination of both (Elastic Net). If the
parameter update crosses the 0.0 value because of the regularizer, the
update is truncated to 0.0 to allow for learning sparse models and achieve
online feature selection.
This implementation works with data represented as dense numpy arrays of
floating point values for the features.
Read more in the :ref:`User Guide <sgd>`.
Parameters
----------
loss : str, default: 'squared_loss'
The loss function to be used. The possible values are 'squared_loss',
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'
The 'squared_loss' refers to the ordinary least squares fit.
'huber' modifies 'squared_loss' to focus less on getting outliers
correct by switching from squared to linear loss past a distance of
epsilon. 'epsilon_insensitive' ignores errors less than epsilon and is
linear past that; this is the loss function used in SVR.
'squared_epsilon_insensitive' is the same but becomes squared loss past
a tolerance of epsilon.
penalty : str, 'none', 'l2', 'l1', or 'elasticnet'
The penalty (aka regularization term) to be used. Defaults to 'l2'
which is the standard regularizer for linear SVM models. 'l1' and
'elasticnet' might bring sparsity to the model (feature selection)
not achievable with 'l2'.
alpha : float
Constant that multiplies the regularization term. Defaults to 0.0001
Also used to compute learning_rate when set to 'optimal'.
l1_ratio : float
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1.
l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1.
Defaults to 0.15.
fit_intercept : bool
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True.
max_iter : int, optional (default=1000)
The maximum number of passes over the training data (aka epochs).
It only impacts the behavior in the ``fit`` method, and not the
:meth:`partial_fit` method.
.. versionadded:: 0.19
tol : float or None, optional (default=1e-3)
The stopping criterion. If it is not None, the iterations will stop
when (loss > best_loss - tol) for ``n_iter_no_change`` consecutive
epochs.
.. versionadded:: 0.19
shuffle : bool, optional
Whether or not the training data should be shuffled after each epoch.
Defaults to True.
verbose : integer, default=0
The verbosity level.
epsilon : float, default=0.1
Epsilon in the epsilon-insensitive loss functions; only if `loss` is
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'.
For 'huber', determines the threshold at which it becomes less
important to get the prediction exactly right.
For epsilon-insensitive, any differences between the current prediction
and the correct label are ignored if they are less than this threshold.
random_state : int, RandomState instance or None, optional (default=None)
The seed of the pseudo random number generator to use when shuffling
the data. If int, random_state is the seed used by the random number
generator; If RandomState instance, random_state is the random number
generator; If None, the random number generator is the RandomState
instance used by `np.random`.
learning_rate : string, optional
The learning rate schedule:
'constant':
eta = eta0
'optimal':
eta = 1.0 / (alpha * (t + t0))
where t0 is chosen by a heuristic proposed by Leon Bottou.
'invscaling': [default]
eta = eta0 / pow(t, power_t)
'adaptive':
eta = eta0, as long as the training keeps decreasing.
Each time n_iter_no_change consecutive epochs fail to decrease the
training loss by tol or fail to increase validation score by tol if
early_stopping is True, the current learning rate is divided by 5.
eta0 : double
The initial learning rate for the 'constant', 'invscaling' or
'adaptive' schedules. The default value is 0.01.
power_t : double
The exponent for inverse scaling learning rate [default 0.25].
early_stopping : bool, default=False
Whether to use early stopping to terminate training when validation
score is not improving. If set to True, it will automatically set aside
a fraction of training data as validation and terminate
training when validation score is not improving by at least tol for
n_iter_no_change consecutive epochs.
.. versionadded:: 0.20
validation_fraction : float, default=0.1
The proportion of training data to set aside as validation set for
early stopping. Must be between 0 and 1.
Only used if early_stopping is True.
.. versionadded:: 0.20
n_iter_no_change : int, default=5
Number of iterations with no improvement to wait before early stopping.
.. versionadded:: 0.20
warm_start : bool, default=False
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution.
See :term:`the Glossary <warm_start>`.
Repeatedly calling fit or partial_fit when warm_start is True can
result in a different solution than when calling fit a single time
because of the way the data is shuffled.
If a dynamic learning rate is used, the learning rate is adapted
depending on the number of samples already seen. Calling ``fit`` resets
this counter, while ``partial_fit`` will result in increasing the
existing counter.
average : bool or int, default=False
When set to True, computes the averaged SGD weights and stores the
result in the ``coef_`` attribute. If set to an int greater than 1,
averaging will begin once the total number of samples seen reaches
average. So ``average=10`` will begin averaging after seeing 10
samples.
Attributes
----------
coef_ : array, shape (n_features,)
Weights assigned to the features.
intercept_ : array, shape (1,)
The intercept term.
average_coef_ : array, shape (n_features,)
Averaged weights assigned to the features.
average_intercept_ : array, shape (1,)
The averaged intercept term.
n_iter_ : int
The actual number of iterations to reach the stopping criterion.
t_ : int
Number of weight updates performed during training.
Same as ``(n_iter_ * n_samples)``.
Examples
--------
>>> import numpy as np
>>> from sklearn import linear_model
>>> n_samples, n_features = 10, 5
>>> rng = np.random.RandomState(0)
>>> y = rng.randn(n_samples)
>>> X = rng.randn(n_samples, n_features)
>>> clf = linear_model.SGDRegressor(max_iter=1000, tol=1e-3)
>>> clf.fit(X, y)
SGDRegressor()
See also
--------
Ridge, ElasticNet, Lasso, sklearn.svm.SVR
"""
def __init__(self, loss="squared_loss", penalty="l2", alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=1e-3,
shuffle=True, verbose=0, epsilon=DEFAULT_EPSILON,
random_state=None, learning_rate="invscaling", eta0=0.01,
power_t=0.25, early_stopping=False, validation_fraction=0.1,
n_iter_no_change=5, warm_start=False, average=False):
super().__init__(
loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,
shuffle=shuffle, verbose=verbose, epsilon=epsilon,
random_state=random_state, learning_rate=learning_rate, eta0=eta0,
power_t=power_t, early_stopping=early_stopping,
validation_fraction=validation_fraction,
n_iter_no_change=n_iter_no_change, warm_start=warm_start,
average=average)
| 41.254748 | 79 | 0.604308 |
import numpy as np
import warnings
from abc import ABCMeta, abstractmethod
from joblib import Parallel, delayed
from ..base import clone, is_classifier
from ._base import LinearClassifierMixin, SparseCoefMixin
from ._base import make_dataset
from ..base import BaseEstimator, RegressorMixin
from ..utils import check_array, check_random_state, check_X_y
from ..utils.extmath import safe_sparse_dot
from ..utils.multiclass import _check_partial_fit_first_call
from ..utils.validation import check_is_fitted, _check_sample_weight
from ..exceptions import ConvergenceWarning
from ..model_selection import StratifiedShuffleSplit, ShuffleSplit
from ._sgd_fast import plain_sgd, average_sgd
from ..utils import compute_class_weight
from ._sgd_fast import Hinge
from ._sgd_fast import SquaredHinge
from ._sgd_fast import Log
from ._sgd_fast import ModifiedHuber
from ._sgd_fast import SquaredLoss
from ._sgd_fast import Huber
from ._sgd_fast import EpsilonInsensitive
from ._sgd_fast import SquaredEpsilonInsensitive
from ..utils.fixes import _joblib_parallel_args
LEARNING_RATE_TYPES = {"constant": 1, "optimal": 2, "invscaling": 3,
"adaptive": 4, "pa1": 5, "pa2": 6}
PENALTY_TYPES = {"none": 0, "l2": 2, "l1": 1, "elasticnet": 3}
DEFAULT_EPSILON = 0.1
MAX_INT = np.iinfo(np.int32).max
class _ValidationScoreCallback:
def __init__(self, estimator, X_val, y_val, sample_weight_val,
classes=None):
self.estimator = clone(estimator)
self.estimator.t_ = 1
if classes is not None:
self.estimator.classes_ = classes
self.X_val = X_val
self.y_val = y_val
self.sample_weight_val = sample_weight_val
def __call__(self, coef, intercept):
est = self.estimator
est.coef_ = coef.reshape(1, -1)
est.intercept_ = np.atleast_1d(intercept)
return est.score(self.X_val, self.y_val, self.sample_weight_val)
class BaseSGD(SparseCoefMixin, BaseEstimator, metaclass=ABCMeta):
def __init__(self, loss, penalty='l2', alpha=0.0001, C=1.0,
l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=1e-3,
shuffle=True, verbose=0, epsilon=0.1, random_state=None,
learning_rate="optimal", eta0=0.0, power_t=0.5,
early_stopping=False, validation_fraction=0.1,
n_iter_no_change=5, warm_start=False, average=False):
self.loss = loss
self.penalty = penalty
self.learning_rate = learning_rate
self.epsilon = epsilon
self.alpha = alpha
self.C = C
self.l1_ratio = l1_ratio
self.fit_intercept = fit_intercept
self.shuffle = shuffle
self.random_state = random_state
self.verbose = verbose
self.eta0 = eta0
self.power_t = power_t
self.early_stopping = early_stopping
self.validation_fraction = validation_fraction
self.n_iter_no_change = n_iter_no_change
self.warm_start = warm_start
self.average = average
self.max_iter = max_iter
self.tol = tol
self._validate_params()
def set_params(self, **kwargs):
super().set_params(**kwargs)
self._validate_params()
return self
@abstractmethod
def fit(self, X, y):
def _validate_params(self, for_partial_fit=False):
if not isinstance(self.shuffle, bool):
raise ValueError("shuffle must be either True or False")
if not isinstance(self.early_stopping, bool):
raise ValueError("early_stopping must be either True or False")
if self.early_stopping and for_partial_fit:
raise ValueError("early_stopping should be False with partial_fit")
if self.max_iter is not None and self.max_iter <= 0:
raise ValueError("max_iter must be > zero. Got %f" % self.max_iter)
if not (0.0 <= self.l1_ratio <= 1.0):
raise ValueError("l1_ratio must be in [0, 1]")
if self.alpha < 0.0:
raise ValueError("alpha must be >= 0")
if self.n_iter_no_change < 1:
raise ValueError("n_iter_no_change must be >= 1")
if not (0.0 < self.validation_fraction < 1.0):
raise ValueError("validation_fraction must be in range (0, 1)")
if self.learning_rate in ("constant", "invscaling", "adaptive"):
if self.eta0 <= 0.0:
raise ValueError("eta0 must be > 0")
if self.learning_rate == "optimal" and self.alpha == 0:
raise ValueError("alpha must be > 0 since "
"learning_rate is 'optimal'. alpha is used "
"to compute the optimal learning rate.")
self._get_penalty_type(self.penalty)
self._get_learning_rate_type(self.learning_rate)
if self.loss not in self.loss_functions:
raise ValueError("The loss %s is not supported. " % self.loss)
def _get_loss_function(self, loss):
try:
loss_ = self.loss_functions[loss]
loss_class, args = loss_[0], loss_[1:]
if loss in ('huber', 'epsilon_insensitive',
'squared_epsilon_insensitive'):
args = (self.epsilon, )
return loss_class(*args)
except KeyError:
raise ValueError("The loss %s is not supported. " % loss)
def _get_learning_rate_type(self, learning_rate):
try:
return LEARNING_RATE_TYPES[learning_rate]
except KeyError:
raise ValueError("learning rate %s "
"is not supported. " % learning_rate)
def _get_penalty_type(self, penalty):
penalty = str(penalty).lower()
try:
return PENALTY_TYPES[penalty]
except KeyError:
raise ValueError("Penalty %s is not supported. " % penalty)
def _allocate_parameter_mem(self, n_classes, n_features, coef_init=None,
intercept_init=None):
if n_classes > 2:
if coef_init is not None:
coef_init = np.asarray(coef_init, order="C")
if coef_init.shape != (n_classes, n_features):
raise ValueError("Provided ``coef_`` does not match "
"dataset. ")
self.coef_ = coef_init
else:
self.coef_ = np.zeros((n_classes, n_features),
dtype=np.float64, order="C")
if intercept_init is not None:
intercept_init = np.asarray(intercept_init, order="C")
if intercept_init.shape != (n_classes, ):
raise ValueError("Provided intercept_init "
"does not match dataset.")
self.intercept_ = intercept_init
else:
self.intercept_ = np.zeros(n_classes, dtype=np.float64,
order="C")
else:
if coef_init is not None:
coef_init = np.asarray(coef_init, dtype=np.float64,
order="C")
coef_init = coef_init.ravel()
if coef_init.shape != (n_features,):
raise ValueError("Provided coef_init does not "
"match dataset.")
self.coef_ = coef_init
else:
self.coef_ = np.zeros(n_features,
dtype=np.float64,
order="C")
if intercept_init is not None:
intercept_init = np.asarray(intercept_init, dtype=np.float64)
if intercept_init.shape != (1,) and intercept_init.shape != ():
raise ValueError("Provided intercept_init "
"does not match dataset.")
self.intercept_ = intercept_init.reshape(1,)
else:
self.intercept_ = np.zeros(1, dtype=np.float64, order="C")
if self.average > 0:
self.standard_coef_ = self.coef_
self.standard_intercept_ = self.intercept_
self.average_coef_ = np.zeros(self.coef_.shape,
dtype=np.float64,
order="C")
self.average_intercept_ = np.zeros(self.standard_intercept_.shape,
dtype=np.float64,
order="C")
def _make_validation_split(self, y):
n_samples = y.shape[0]
validation_mask = np.zeros(n_samples, dtype=np.uint8)
if not self.early_stopping:
return validation_mask
if is_classifier(self):
splitter_type = StratifiedShuffleSplit
else:
splitter_type = ShuffleSplit
cv = splitter_type(test_size=self.validation_fraction,
random_state=self.random_state)
idx_train, idx_val = next(cv.split(np.zeros(shape=(y.shape[0], 1)), y))
if idx_train.shape[0] == 0 or idx_val.shape[0] == 0:
raise ValueError(
"Splitting %d samples into a train set and a validation set "
"with validation_fraction=%r led to an empty set (%d and %d "
"samples). Please either change validation_fraction, increase "
"number of samples, or disable early_stopping."
% (n_samples, self.validation_fraction, idx_train.shape[0],
idx_val.shape[0]))
validation_mask[idx_val] = 1
return validation_mask
def _make_validation_score_cb(self, validation_mask, X, y, sample_weight,
classes=None):
if not self.early_stopping:
return None
return _ValidationScoreCallback(
self, X[validation_mask], y[validation_mask],
sample_weight[validation_mask], classes=classes)
def _prepare_fit_binary(est, y, i):
y_i = np.ones(y.shape, dtype=np.float64, order="C")
y_i[y != est.classes_[i]] = -1.0
average_intercept = 0
average_coef = None
if len(est.classes_) == 2:
if not est.average:
coef = est.coef_.ravel()
intercept = est.intercept_[0]
else:
coef = est.standard_coef_.ravel()
intercept = est.standard_intercept_[0]
average_coef = est.average_coef_.ravel()
average_intercept = est.average_intercept_[0]
else:
if not est.average:
coef = est.coef_[i]
intercept = est.intercept_[i]
else:
coef = est.standard_coef_[i]
intercept = est.standard_intercept_[i]
average_coef = est.average_coef_[i]
average_intercept = est.average_intercept_[i]
return y_i, coef, intercept, average_coef, average_intercept
def fit_binary(est, i, X, y, alpha, C, learning_rate, max_iter,
pos_weight, neg_weight, sample_weight, validation_mask=None,
random_state=None):
y_i, coef, intercept, average_coef, average_intercept = \
_prepare_fit_binary(est, y, i)
assert y_i.shape[0] == y.shape[0] == sample_weight.shape[0]
random_state = check_random_state(random_state)
dataset, intercept_decay = make_dataset(
X, y_i, sample_weight, random_state=random_state)
penalty_type = est._get_penalty_type(est.penalty)
learning_rate_type = est._get_learning_rate_type(learning_rate)
if validation_mask is None:
validation_mask = est._make_validation_split(y_i)
classes = np.array([-1, 1], dtype=y_i.dtype)
validation_score_cb = est._make_validation_score_cb(
validation_mask, X, y_i, sample_weight, classes=classes)
seed = random_state.randint(MAX_INT)
tol = est.tol if est.tol is not None else -np.inf
if not est.average:
result = plain_sgd(coef, intercept, est.loss_function_,
penalty_type, alpha, C, est.l1_ratio,
dataset, validation_mask, est.early_stopping,
validation_score_cb, int(est.n_iter_no_change),
max_iter, tol, int(est.fit_intercept),
int(est.verbose), int(est.shuffle), seed,
pos_weight, neg_weight,
learning_rate_type, est.eta0,
est.power_t, est.t_, intercept_decay)
else:
standard_coef, standard_intercept, average_coef, average_intercept, \
n_iter_ = average_sgd(coef, intercept, average_coef,
average_intercept, est.loss_function_,
penalty_type, alpha, C, est.l1_ratio,
dataset, validation_mask, est.early_stopping,
validation_score_cb,
int(est.n_iter_no_change), max_iter, tol,
int(est.fit_intercept), int(est.verbose),
int(est.shuffle), seed, pos_weight,
neg_weight, learning_rate_type, est.eta0,
est.power_t, est.t_, intercept_decay,
est.average)
if len(est.classes_) == 2:
est.average_intercept_[0] = average_intercept
else:
est.average_intercept_[i] = average_intercept
result = standard_coef, standard_intercept, n_iter_
return result
class BaseSGDClassifier(LinearClassifierMixin, BaseSGD, metaclass=ABCMeta):
loss_functions = {
"hinge": (Hinge, 1.0),
"squared_hinge": (SquaredHinge, 1.0),
"perceptron": (Hinge, 0.0),
"log": (Log, ),
"modified_huber": (ModifiedHuber, ),
"squared_loss": (SquaredLoss, ),
"huber": (Huber, DEFAULT_EPSILON),
"epsilon_insensitive": (EpsilonInsensitive, DEFAULT_EPSILON),
"squared_epsilon_insensitive": (SquaredEpsilonInsensitive,
DEFAULT_EPSILON),
}
@abstractmethod
def __init__(self, loss="hinge", penalty='l2', alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=1e-3,
shuffle=True, verbose=0, epsilon=DEFAULT_EPSILON, n_jobs=None,
random_state=None, learning_rate="optimal", eta0=0.0,
power_t=0.5, early_stopping=False,
validation_fraction=0.1, n_iter_no_change=5,
class_weight=None, warm_start=False, average=False):
super().__init__(
loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,
shuffle=shuffle, verbose=verbose, epsilon=epsilon,
random_state=random_state, learning_rate=learning_rate, eta0=eta0,
power_t=power_t, early_stopping=early_stopping,
validation_fraction=validation_fraction,
n_iter_no_change=n_iter_no_change, warm_start=warm_start,
average=average)
self.class_weight = class_weight
self.n_jobs = n_jobs
def _partial_fit(self, X, y, alpha, C,
loss, learning_rate, max_iter,
classes, sample_weight,
coef_init, intercept_init):
X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C",
accept_large_sparse=False)
n_samples, n_features = X.shape
_check_partial_fit_first_call(self, classes)
n_classes = self.classes_.shape[0]
self._expanded_class_weight = compute_class_weight(self.class_weight,
self.classes_, y)
sample_weight = _check_sample_weight(sample_weight, X)
if getattr(self, "coef_", None) is None or coef_init is not None:
self._allocate_parameter_mem(n_classes, n_features,
coef_init, intercept_init)
elif n_features != self.coef_.shape[-1]:
raise ValueError("Number of features %d does not match previous "
"data %d." % (n_features, self.coef_.shape[-1]))
self.loss_function_ = self._get_loss_function(loss)
if not hasattr(self, "t_"):
self.t_ = 1.0
if n_classes > 2:
self._fit_multiclass(X, y, alpha=alpha, C=C,
learning_rate=learning_rate,
sample_weight=sample_weight,
max_iter=max_iter)
elif n_classes == 2:
self._fit_binary(X, y, alpha=alpha, C=C,
learning_rate=learning_rate,
sample_weight=sample_weight,
max_iter=max_iter)
else:
raise ValueError(
"The number of classes has to be greater than one;"
" got %d class" % n_classes)
return self
def _fit(self, X, y, alpha, C, loss, learning_rate, coef_init=None,
intercept_init=None, sample_weight=None):
self._validate_params()
if hasattr(self, "classes_"):
self.classes_ = None
X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C",
accept_large_sparse=False)
classes = np.unique(y)
if self.warm_start and hasattr(self, "coef_"):
if coef_init is None:
coef_init = self.coef_
if intercept_init is None:
intercept_init = self.intercept_
else:
self.coef_ = None
self.intercept_ = None
if self.average > 0:
self.standard_coef_ = self.coef_
self.standard_intercept_ = self.intercept_
self.average_coef_ = None
self.average_intercept_ = None
self.t_ = 1.0
self._partial_fit(X, y, alpha, C, loss, learning_rate, self.max_iter,
classes, sample_weight, coef_init, intercept_init)
if (self.tol is not None and self.tol > -np.inf
and self.n_iter_ == self.max_iter):
warnings.warn("Maximum number of iteration reached before "
"convergence. Consider increasing max_iter to "
"improve the fit.",
ConvergenceWarning)
return self
def _fit_binary(self, X, y, alpha, C, sample_weight,
learning_rate, max_iter):
coef, intercept, n_iter_ = fit_binary(self, 1, X, y, alpha, C,
learning_rate, max_iter,
self._expanded_class_weight[1],
self._expanded_class_weight[0],
sample_weight,
random_state=self.random_state)
self.t_ += n_iter_ * X.shape[0]
self.n_iter_ = n_iter_
if self.average > 0:
if self.average <= self.t_ - 1:
self.coef_ = self.average_coef_.reshape(1, -1)
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_.reshape(1, -1)
self.standard_intercept_ = np.atleast_1d(intercept)
self.intercept_ = self.standard_intercept_
else:
self.coef_ = coef.reshape(1, -1)
self.intercept_ = np.atleast_1d(intercept)
def _fit_multiclass(self, X, y, alpha, C, learning_rate,
sample_weight, max_iter):
validation_mask = self._make_validation_split(y)
random_state = check_random_state(self.random_state)
seeds = random_state.randint(MAX_INT, size=len(self.classes_))
result = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,
**_joblib_parallel_args(require="sharedmem"))(
delayed(fit_binary)(self, i, X, y, alpha, C, learning_rate,
max_iter, self._expanded_class_weight[i],
1., sample_weight,
validation_mask=validation_mask,
random_state=seed)
for i, seed in enumerate(seeds))
n_iter_ = 0.
for i, (_, intercept, n_iter_i) in enumerate(result):
self.intercept_[i] = intercept
n_iter_ = max(n_iter_, n_iter_i)
self.t_ += n_iter_ * X.shape[0]
self.n_iter_ = n_iter_
if self.average > 0:
if self.average <= self.t_ - 1.0:
self.coef_ = self.average_coef_
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_
self.standard_intercept_ = np.atleast_1d(self.intercept_)
self.intercept_ = self.standard_intercept_
def partial_fit(self, X, y, classes=None, sample_weight=None):
self._validate_params(for_partial_fit=True)
if self.class_weight in ['balanced']:
raise ValueError("class_weight '{0}' is not supported for "
"partial_fit. In order to use 'balanced' weights,"
" use compute_class_weight('{0}', classes, y). "
"In place of y you can us a large enough sample "
"of the full training set target to properly "
"estimate the class frequency distributions. "
"Pass the resulting weights as the class_weight "
"parameter.".format(self.class_weight))
return self._partial_fit(X, y, alpha=self.alpha, C=1.0, loss=self.loss,
learning_rate=self.learning_rate, max_iter=1,
classes=classes, sample_weight=sample_weight,
coef_init=None, intercept_init=None)
def fit(self, X, y, coef_init=None, intercept_init=None,
sample_weight=None):
return self._fit(X, y, alpha=self.alpha, C=1.0,
loss=self.loss, learning_rate=self.learning_rate,
coef_init=coef_init, intercept_init=intercept_init,
sample_weight=sample_weight)
class SGDClassifier(BaseSGDClassifier):
def __init__(self, loss="hinge", penalty='l2', alpha=0.0001, l1_ratio=0.15,
fit_intercept=True, max_iter=1000, tol=1e-3, shuffle=True,
verbose=0, epsilon=DEFAULT_EPSILON, n_jobs=None,
random_state=None, learning_rate="optimal", eta0=0.0,
power_t=0.5, early_stopping=False, validation_fraction=0.1,
n_iter_no_change=5, class_weight=None, warm_start=False,
average=False):
super().__init__(
loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,
shuffle=shuffle, verbose=verbose, epsilon=epsilon, n_jobs=n_jobs,
random_state=random_state, learning_rate=learning_rate, eta0=eta0,
power_t=power_t, early_stopping=early_stopping,
validation_fraction=validation_fraction,
n_iter_no_change=n_iter_no_change, class_weight=class_weight,
warm_start=warm_start, average=average)
def _check_proba(self):
if self.loss not in ("log", "modified_huber"):
raise AttributeError("probability estimates are not available for"
" loss=%r" % self.loss)
@property
def predict_proba(self):
self._check_proba()
return self._predict_proba
def _predict_proba(self, X):
check_is_fitted(self)
if self.loss == "log":
return self._predict_proba_lr(X)
elif self.loss == "modified_huber":
binary = (len(self.classes_) == 2)
scores = self.decision_function(X)
if binary:
prob2 = np.ones((scores.shape[0], 2))
prob = prob2[:, 1]
else:
prob = scores
np.clip(scores, -1, 1, prob)
prob += 1.
prob /= 2.
if binary:
prob2[:, 0] -= prob
prob = prob2
else:
# normalize neatly; work around this to produce uniform
# probabilities
prob_sum = prob.sum(axis=1)
all_zero = (prob_sum == 0)
if np.any(all_zero):
prob[all_zero, :] = 1
prob_sum[all_zero] = len(self.classes_)
# normalize
prob /= prob_sum.reshape((prob.shape[0], -1))
return prob
else:
raise NotImplementedError("predict_(log_)proba only supported when"
" loss='log' or loss='modified_huber' "
"(%r given)" % self.loss)
@property
def predict_log_proba(self):
self._check_proba()
return self._predict_log_proba
def _predict_log_proba(self, X):
return np.log(self.predict_proba(X))
class BaseSGDRegressor(RegressorMixin, BaseSGD):
loss_functions = {
"squared_loss": (SquaredLoss, ),
"huber": (Huber, DEFAULT_EPSILON),
"epsilon_insensitive": (EpsilonInsensitive, DEFAULT_EPSILON),
"squared_epsilon_insensitive": (SquaredEpsilonInsensitive,
DEFAULT_EPSILON),
}
@abstractmethod
def __init__(self, loss="squared_loss", penalty="l2", alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=1e-3,
shuffle=True, verbose=0, epsilon=DEFAULT_EPSILON,
random_state=None, learning_rate="invscaling", eta0=0.01,
power_t=0.25, early_stopping=False, validation_fraction=0.1,
n_iter_no_change=5, warm_start=False, average=False):
super().__init__(
loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,
shuffle=shuffle, verbose=verbose, epsilon=epsilon,
random_state=random_state, learning_rate=learning_rate, eta0=eta0,
power_t=power_t, early_stopping=early_stopping,
validation_fraction=validation_fraction,
n_iter_no_change=n_iter_no_change, warm_start=warm_start,
average=average)
def _partial_fit(self, X, y, alpha, C, loss, learning_rate,
max_iter, sample_weight, coef_init, intercept_init):
X, y = check_X_y(X, y, "csr", copy=False, order='C', dtype=np.float64,
accept_large_sparse=False)
y = y.astype(np.float64, copy=False)
n_samples, n_features = X.shape
sample_weight = _check_sample_weight(sample_weight, X)
# Allocate datastructures from input arguments
if getattr(self, "coef_", None) is None:
self._allocate_parameter_mem(1, n_features, coef_init,
intercept_init)
elif n_features != self.coef_.shape[-1]:
raise ValueError("Number of features %d does not match previous "
"data %d." % (n_features, self.coef_.shape[-1]))
if self.average > 0 and getattr(self, "average_coef_", None) is None:
self.average_coef_ = np.zeros(n_features,
dtype=np.float64,
order="C")
self.average_intercept_ = np.zeros(1, dtype=np.float64, order="C")
self._fit_regressor(X, y, alpha, C, loss, learning_rate,
sample_weight, max_iter)
return self
def partial_fit(self, X, y, sample_weight=None):
self._validate_params(for_partial_fit=True)
return self._partial_fit(X, y, self.alpha, C=1.0,
loss=self.loss,
learning_rate=self.learning_rate, max_iter=1,
sample_weight=sample_weight, coef_init=None,
intercept_init=None)
def _fit(self, X, y, alpha, C, loss, learning_rate, coef_init=None,
intercept_init=None, sample_weight=None):
self._validate_params()
if self.warm_start and getattr(self, "coef_", None) is not None:
if coef_init is None:
coef_init = self.coef_
if intercept_init is None:
intercept_init = self.intercept_
else:
self.coef_ = None
self.intercept_ = None
if self.average > 0:
self.standard_intercept_ = self.intercept_
self.standard_coef_ = self.coef_
self.average_coef_ = None
self.average_intercept_ = None
# Clear iteration count for multiple call to fit.
self.t_ = 1.0
self._partial_fit(X, y, alpha, C, loss, learning_rate,
self.max_iter, sample_weight, coef_init,
intercept_init)
if (self.tol is not None and self.tol > -np.inf
and self.n_iter_ == self.max_iter):
warnings.warn("Maximum number of iteration reached before "
"convergence. Consider increasing max_iter to "
"improve the fit.",
ConvergenceWarning)
return self
def fit(self, X, y, coef_init=None, intercept_init=None,
sample_weight=None):
return self._fit(X, y, alpha=self.alpha, C=1.0,
loss=self.loss, learning_rate=self.learning_rate,
coef_init=coef_init,
intercept_init=intercept_init,
sample_weight=sample_weight)
def _decision_function(self, X):
check_is_fitted(self)
X = check_array(X, accept_sparse='csr')
scores = safe_sparse_dot(X, self.coef_.T,
dense_output=True) + self.intercept_
return scores.ravel()
def predict(self, X):
return self._decision_function(X)
def _fit_regressor(self, X, y, alpha, C, loss, learning_rate,
sample_weight, max_iter):
dataset, intercept_decay = make_dataset(X, y, sample_weight)
loss_function = self._get_loss_function(loss)
penalty_type = self._get_penalty_type(self.penalty)
learning_rate_type = self._get_learning_rate_type(learning_rate)
if not hasattr(self, "t_"):
self.t_ = 1.0
validation_mask = self._make_validation_split(y)
validation_score_cb = self._make_validation_score_cb(
validation_mask, X, y, sample_weight)
random_state = check_random_state(self.random_state)
# numpy mtrand expects a C long which is a signed 32 bit integer under
# Windows
seed = random_state.randint(0, np.iinfo(np.int32).max)
tol = self.tol if self.tol is not None else -np.inf
if self.average > 0:
self.standard_coef_, self.standard_intercept_, \
self.average_coef_, self.average_intercept_, self.n_iter_ =\
average_sgd(self.standard_coef_,
self.standard_intercept_[0],
self.average_coef_,
self.average_intercept_[0],
loss_function,
penalty_type,
alpha, C,
self.l1_ratio,
dataset,
validation_mask, self.early_stopping,
validation_score_cb,
int(self.n_iter_no_change),
max_iter, tol,
int(self.fit_intercept),
int(self.verbose),
int(self.shuffle),
seed,
1.0, 1.0,
learning_rate_type,
self.eta0, self.power_t, self.t_,
intercept_decay, self.average)
self.average_intercept_ = np.atleast_1d(self.average_intercept_)
self.standard_intercept_ = np.atleast_1d(self.standard_intercept_)
self.t_ += self.n_iter_ * X.shape[0]
if self.average <= self.t_ - 1.0:
self.coef_ = self.average_coef_
self.intercept_ = self.average_intercept_
else:
self.coef_ = self.standard_coef_
self.intercept_ = self.standard_intercept_
else:
self.coef_, self.intercept_, self.n_iter_ = \
plain_sgd(self.coef_,
self.intercept_[0],
loss_function,
penalty_type,
alpha, C,
self.l1_ratio,
dataset,
validation_mask, self.early_stopping,
validation_score_cb,
int(self.n_iter_no_change),
max_iter, tol,
int(self.fit_intercept),
int(self.verbose),
int(self.shuffle),
seed,
1.0, 1.0,
learning_rate_type,
self.eta0, self.power_t, self.t_,
intercept_decay)
self.t_ += self.n_iter_ * X.shape[0]
self.intercept_ = np.atleast_1d(self.intercept_)
class SGDRegressor(BaseSGDRegressor):
def __init__(self, loss="squared_loss", penalty="l2", alpha=0.0001,
l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=1e-3,
shuffle=True, verbose=0, epsilon=DEFAULT_EPSILON,
random_state=None, learning_rate="invscaling", eta0=0.01,
power_t=0.25, early_stopping=False, validation_fraction=0.1,
n_iter_no_change=5, warm_start=False, average=False):
super().__init__(
loss=loss, penalty=penalty, alpha=alpha, l1_ratio=l1_ratio,
fit_intercept=fit_intercept, max_iter=max_iter, tol=tol,
shuffle=shuffle, verbose=verbose, epsilon=epsilon,
random_state=random_state, learning_rate=learning_rate, eta0=eta0,
power_t=power_t, early_stopping=early_stopping,
validation_fraction=validation_fraction,
n_iter_no_change=n_iter_no_change, warm_start=warm_start,
average=average)
| true | true |
f7256edb1cd981f4d5a110f018a377b55aa4f7c7 | 1,246 | py | Python | Model prediction/app.py | choudhury722k/English-to-French-translator | e792ce92adbdd3100d73d9d8aebc109cc7c560d7 | [
"MIT"
] | null | null | null | Model prediction/app.py | choudhury722k/English-to-French-translator | e792ce92adbdd3100d73d9d8aebc109cc7c560d7 | [
"MIT"
] | null | null | null | Model prediction/app.py | choudhury722k/English-to-French-translator | e792ce92adbdd3100d73d9d8aebc109cc7c560d7 | [
"MIT"
] | null | null | null | from re import X
from flask import Flask,render_template,url_for,request
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras import models
import numpy as np
import pickle
french_tokenizer = pickle.load(open('french_tokenizer.pickle', 'rb'))
english_tokenizer = pickle.load(open('english_tokenizer.pickle', 'rb'))
model = models.load_model("translator_model.h5")
y_id_to_word = {value: key for key, value in french_tokenizer.word_index.items()}
y_id_to_word[0] = '<PAD>'
#y_id_to_word
app = Flask(__name__)
@app.route('/')
def hello_World():
return "Hello Soumya"
@app.route('/translator', methods = ['GET', 'POST'])
def eng_to_french():
message = request.args.get("message")
sentence = [english_tokenizer.word_index[word] for word in message.split()]
#sentence
sentence = pad_sequences([sentence], maxlen=15, padding='post')
sentences = np.array([sentence[0]])
predictions = model.predict(sentences, len(sentences))
x = ' '.join([y_id_to_word[np.argmax(x)] for x in predictions[0]])
if '<PAD>' in x:
x=x.replace('<PAD>','')
print(x)
return x
if __name__ == '__main__':
app.run(debug=True)
| 31.948718 | 81 | 0.719904 | from re import X
from flask import Flask,render_template,url_for,request
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras import models
import numpy as np
import pickle
french_tokenizer = pickle.load(open('french_tokenizer.pickle', 'rb'))
english_tokenizer = pickle.load(open('english_tokenizer.pickle', 'rb'))
model = models.load_model("translator_model.h5")
y_id_to_word = {value: key for key, value in french_tokenizer.word_index.items()}
y_id_to_word[0] = '<PAD>'
app = Flask(__name__)
@app.route('/')
def hello_World():
return "Hello Soumya"
@app.route('/translator', methods = ['GET', 'POST'])
def eng_to_french():
message = request.args.get("message")
sentence = [english_tokenizer.word_index[word] for word in message.split()]
sentence = pad_sequences([sentence], maxlen=15, padding='post')
sentences = np.array([sentence[0]])
predictions = model.predict(sentences, len(sentences))
x = ' '.join([y_id_to_word[np.argmax(x)] for x in predictions[0]])
if '<PAD>' in x:
x=x.replace('<PAD>','')
print(x)
return x
if __name__ == '__main__':
app.run(debug=True)
| true | true |
f7256eedcf3a758fc0b86618617827425e34c972 | 438 | py | Python | carpyncho2/carpyncho/steps/prepare_pawprint_to_sync.py | carpyncho/yeolde_carpyncho | fba72ebf9d4a3e4e4ea18160310058c6812a0457 | [
"BSD-3-Clause"
] | null | null | null | carpyncho2/carpyncho/steps/prepare_pawprint_to_sync.py | carpyncho/yeolde_carpyncho | fba72ebf9d4a3e4e4ea18160310058c6812a0457 | [
"BSD-3-Clause"
] | 2 | 2020-06-05T19:37:26.000Z | 2020-06-05T19:40:38.000Z | carpyncho2/carpyncho/steps/prepare_pawprint_to_sync.py | carpyncho/yeolde_carpyncho | fba72ebf9d4a3e4e4ea18160310058c6812a0457 | [
"BSD-3-Clause"
] | null | null | null | #!/usr/bin/env python
# -*- coding: utf-8 -*-
from corral import run
from ..models import PawprintXTile
class PreparePawprintToSync(run.Step):
model = PawprintXTile
conditions = [
PawprintXTile.status == "raw",
PawprintXTile.tile.has(status="loaded"),
PawprintXTile.pawprint.has(status="loaded")
]
limit = 500
groups = ["match"]
def process(self, pxt):
pxt.status = "pending"
| 19.909091 | 51 | 0.630137 |
from corral import run
from ..models import PawprintXTile
class PreparePawprintToSync(run.Step):
model = PawprintXTile
conditions = [
PawprintXTile.status == "raw",
PawprintXTile.tile.has(status="loaded"),
PawprintXTile.pawprint.has(status="loaded")
]
limit = 500
groups = ["match"]
def process(self, pxt):
pxt.status = "pending"
| true | true |
f7256fad4dd4f8677f2d6bac3cf8110e20ecf681 | 423 | py | Python | mysite/mysite/development_settings.py | timmahrt/gamecorpus | 6ce170f3d590475a320410c9d937039555207ee9 | [
"MIT"
] | null | null | null | mysite/mysite/development_settings.py | timmahrt/gamecorpus | 6ce170f3d590475a320410c9d937039555207ee9 | [
"MIT"
] | null | null | null | mysite/mysite/development_settings.py | timmahrt/gamecorpus | 6ce170f3d590475a320410c9d937039555207ee9 | [
"MIT"
] | null | null | null | from mysite.common_settings import *
SECRET_KEY = "aje#lg$7!t!tc5*i%ittn(to%5#5%vjvi*oc=ib25wx%+##_b+"
DEBUG = True
ALLOWED_HOSTS = ["*"]
# Database
# https://docs.djangoproject.com/en/3.1/ref/settings/#databases
DATABASES = {
"default": {
"ENGINE": "django.db.backends.mysql",
"NAME": "game_corpus_db",
"HOST": "127.0.0.1",
"USER": "tmahrt",
"PASSWORD": "12345678",
}
} | 21.15 | 65 | 0.591017 | from mysite.common_settings import *
SECRET_KEY = "aje#lg$7!t!tc5*i%ittn(to%5#5%vjvi*oc=ib25wx%+##_b+"
DEBUG = True
ALLOWED_HOSTS = ["*"]
S = {
"default": {
"ENGINE": "django.db.backends.mysql",
"NAME": "game_corpus_db",
"HOST": "127.0.0.1",
"USER": "tmahrt",
"PASSWORD": "12345678",
}
} | true | true |
f725707d1175e051257c22b78d93284af9b6061b | 4,819 | py | Python | catalyst/engines/tests/test_parallel.py | alxmamaev/catalyst | d05120c68fbc5174ff74297d29c0fc00d7e94924 | [
"Apache-2.0"
] | 1 | 2021-03-02T12:06:32.000Z | 2021-03-02T12:06:32.000Z | catalyst/engines/tests/test_parallel.py | alxmamaev/catalyst | d05120c68fbc5174ff74297d29c0fc00d7e94924 | [
"Apache-2.0"
] | null | null | null | catalyst/engines/tests/test_parallel.py | alxmamaev/catalyst | d05120c68fbc5174ff74297d29c0fc00d7e94924 | [
"Apache-2.0"
] | null | null | null | # flake8: noqa
from typing import Any, Dict, List
import logging
from tempfile import TemporaryDirectory
from pytest import mark
import torch
from torch.utils.data import DataLoader
from catalyst.callbacks import CheckpointCallback, CriterionCallback, OptimizerCallback
from catalyst.core.runner import IRunner
from catalyst.engines import DataParallelEngine
from catalyst.engines.torch import DeviceEngine
from catalyst.loggers import ConsoleLogger, CSVLogger
from catalyst.runners.config import SupervisedConfigRunner
from catalyst.settings import IS_CUDA_AVAILABLE, NUM_CUDA_DEVICES
from .misc import DataParallelTypeChecker, DummyDataset, DummyModel, LossMinimizationCallback
logger = logging.getLogger(__name__)
class CustomRunner(IRunner):
def __init__(self, logdir):
super().__init__()
self._logdir = logdir
def get_engine(self):
return DataParallelEngine()
def get_callbacks(self, stage: str):
return {
"criterion": CriterionCallback(
metric_key="loss", input_key="logits", target_key="targets"
),
"optimizer": OptimizerCallback(metric_key="loss"),
# "scheduler": dl.SchedulerCallback(loader_key="valid", metric_key="loss"),
"checkpoint": CheckpointCallback(
self._logdir, loader_key="valid", metric_key="loss", minimize=True, save_n_best=3
),
"test_nn_parallel_data_parallel": DataParallelTypeChecker(),
"test_loss_minimization": LossMinimizationCallback("loss", logger=logger),
}
@property
def stages(self) -> "Iterable[str]":
return ["train"]
def get_stage_len(self, stage: str) -> int:
return 10
def get_loaders(self, stage: str) -> "OrderedDict[str, DataLoader]":
dataset = DummyDataset(6)
loader = DataLoader(dataset, batch_size=4)
return {"train": loader, "valid": loader}
def get_model(self, stage: str):
return DummyModel(4, 2)
def get_criterion(self, stage: str):
return torch.nn.MSELoss()
def get_optimizer(self, model, stage: str):
return torch.optim.Adam(model.parameters())
def get_scheduler(self, optimizer, stage: str):
return None
def get_trial(self):
return None
def get_loggers(self):
return {"console": ConsoleLogger(), "csv": CSVLogger(logdir=self._logdir)}
def handle_batch(self, batch):
x, y = batch
logits = self.model(x)
self.batch = {"features": x, "targets": y, "logits": logits}
def train_from_runner():
with TemporaryDirectory() as logdir:
runner = CustomRunner(logdir)
runner.run()
def train_from_config():
with TemporaryDirectory() as logdir:
dataset = DummyDataset(6)
runner = SupervisedConfigRunner(
config={
"args": {"logdir": logdir},
"model": {"_target_": "DummyModel", "in_features": 4, "out_features": 2},
"engine": {"_target_": "DataParallelEngine"},
"args": {"logdir": logdir},
"stages": {
"stage1": {
"num_epochs": 10,
"loaders": {"batch_size": 4, "num_workers": 0},
"criterion": {"_target_": "MSELoss"},
"optimizer": {"_target_": "Adam", "lr": 1e-3},
"callbacks": {
"criterion": {
"_target_": "CriterionCallback",
"metric_key": "loss",
"input_key": "logits",
"target_key": "targets",
},
"optimizer": {"_target_": "OptimizerCallback", "metric_key": "loss"},
"test_nn_parallel_data_parallel": {
"_target_": "DataParallelTypeChecker"
},
"test_loss_minimization": {
"_target_": "LossMinimizationCallback",
"key": "loss",
},
},
},
},
}
)
runner.get_datasets = lambda *args, **kwargs: {
"train": dataset,
"valid": dataset,
}
runner.run()
@mark.skipif(not IS_CUDA_AVAILABLE, reason="CUDA device is not available")
def test_experiment_parallel_engine_with_cuda():
train_from_runner()
# @mark.skip("Config experiment is in development phase!")
@mark.skipif(not IS_CUDA_AVAILABLE, reason="CUDA device is not available")
def test_config_experiment_engine_with_cuda():
train_from_config()
| 34.421429 | 97 | 0.573563 |
from typing import Any, Dict, List
import logging
from tempfile import TemporaryDirectory
from pytest import mark
import torch
from torch.utils.data import DataLoader
from catalyst.callbacks import CheckpointCallback, CriterionCallback, OptimizerCallback
from catalyst.core.runner import IRunner
from catalyst.engines import DataParallelEngine
from catalyst.engines.torch import DeviceEngine
from catalyst.loggers import ConsoleLogger, CSVLogger
from catalyst.runners.config import SupervisedConfigRunner
from catalyst.settings import IS_CUDA_AVAILABLE, NUM_CUDA_DEVICES
from .misc import DataParallelTypeChecker, DummyDataset, DummyModel, LossMinimizationCallback
logger = logging.getLogger(__name__)
class CustomRunner(IRunner):
def __init__(self, logdir):
super().__init__()
self._logdir = logdir
def get_engine(self):
return DataParallelEngine()
def get_callbacks(self, stage: str):
return {
"criterion": CriterionCallback(
metric_key="loss", input_key="logits", target_key="targets"
),
"optimizer": OptimizerCallback(metric_key="loss"),
"checkpoint": CheckpointCallback(
self._logdir, loader_key="valid", metric_key="loss", minimize=True, save_n_best=3
),
"test_nn_parallel_data_parallel": DataParallelTypeChecker(),
"test_loss_minimization": LossMinimizationCallback("loss", logger=logger),
}
@property
def stages(self) -> "Iterable[str]":
return ["train"]
def get_stage_len(self, stage: str) -> int:
return 10
def get_loaders(self, stage: str) -> "OrderedDict[str, DataLoader]":
dataset = DummyDataset(6)
loader = DataLoader(dataset, batch_size=4)
return {"train": loader, "valid": loader}
def get_model(self, stage: str):
return DummyModel(4, 2)
def get_criterion(self, stage: str):
return torch.nn.MSELoss()
def get_optimizer(self, model, stage: str):
return torch.optim.Adam(model.parameters())
def get_scheduler(self, optimizer, stage: str):
return None
def get_trial(self):
return None
def get_loggers(self):
return {"console": ConsoleLogger(), "csv": CSVLogger(logdir=self._logdir)}
def handle_batch(self, batch):
x, y = batch
logits = self.model(x)
self.batch = {"features": x, "targets": y, "logits": logits}
def train_from_runner():
with TemporaryDirectory() as logdir:
runner = CustomRunner(logdir)
runner.run()
def train_from_config():
with TemporaryDirectory() as logdir:
dataset = DummyDataset(6)
runner = SupervisedConfigRunner(
config={
"args": {"logdir": logdir},
"model": {"_target_": "DummyModel", "in_features": 4, "out_features": 2},
"engine": {"_target_": "DataParallelEngine"},
"args": {"logdir": logdir},
"stages": {
"stage1": {
"num_epochs": 10,
"loaders": {"batch_size": 4, "num_workers": 0},
"criterion": {"_target_": "MSELoss"},
"optimizer": {"_target_": "Adam", "lr": 1e-3},
"callbacks": {
"criterion": {
"_target_": "CriterionCallback",
"metric_key": "loss",
"input_key": "logits",
"target_key": "targets",
},
"optimizer": {"_target_": "OptimizerCallback", "metric_key": "loss"},
"test_nn_parallel_data_parallel": {
"_target_": "DataParallelTypeChecker"
},
"test_loss_minimization": {
"_target_": "LossMinimizationCallback",
"key": "loss",
},
},
},
},
}
)
runner.get_datasets = lambda *args, **kwargs: {
"train": dataset,
"valid": dataset,
}
runner.run()
@mark.skipif(not IS_CUDA_AVAILABLE, reason="CUDA device is not available")
def test_experiment_parallel_engine_with_cuda():
train_from_runner()
@mark.skipif(not IS_CUDA_AVAILABLE, reason="CUDA device is not available")
def test_config_experiment_engine_with_cuda():
train_from_config()
| true | true |
f72572a5ded8e8384c8775f3509841aff1d8e01a | 1,939 | py | Python | tests/test_issues/test_issue_25.py | cmungall/PyShEx | 43026c4b0393362e770b868794c5d9071e691d6f | [
"CC0-1.0"
] | 25 | 2018-01-11T10:59:16.000Z | 2021-07-02T03:44:02.000Z | tests/test_issues/test_issue_25.py | cmungall/PyShEx | 43026c4b0393362e770b868794c5d9071e691d6f | [
"CC0-1.0"
] | 66 | 2018-03-12T01:12:02.000Z | 2022-03-18T07:56:31.000Z | tests/test_issues/test_issue_25.py | cmungall/PyShEx | 43026c4b0393362e770b868794c5d9071e691d6f | [
"CC0-1.0"
] | 12 | 2018-04-06T11:29:40.000Z | 2021-12-17T22:48:07.000Z | import os
import unittest
from contextlib import redirect_stdout, redirect_stderr
from io import StringIO
from pyshex.shex_evaluator import evaluate_cli
data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data'))
validation_dir = os.path.join(data_dir, 'validation')
rdffile = os.path.join(validation_dir, 'simple.ttl')
shexfile = os.path.join(validation_dir, 'simple.shex')
class Issue25TestCase(unittest.TestCase):
def test_nostart(self):
outf = StringIO()
with(redirect_stdout(outf)):
evaluate_cli(f"{rdffile} {shexfile} -A".split())
self.assertEqual("""Errors:
Focus: None
Start: None
Reason: START node is not specified""", outf.getvalue().strip())
def test_all_nodes(self):
outf = StringIO()
with(redirect_stderr(outf)):
evaluate_cli(f"{rdffile} {shexfile} -s http://example.org/shapes/S".split())
self.assertEqual('Error: You must specify one or more graph focus nodes, supply a SPARQL query, '
'or use the "-A" option',
outf.getvalue().strip())
outf = StringIO()
with(redirect_stdout(outf)):
evaluate_cli(f"{rdffile} {shexfile} -A -s http://example.org/shapes/S".split())
self.assertEqual("""Errors:
Focus: http://a.example/s1
Start: http://example.org/shapes/S
Reason: Testing :s1 against shape http://example.org/shapes/S
No matching triples found for predicate :s4
Focus: http://a.example/s2
Start: http://example.org/shapes/S
Reason: Testing :s2 against shape http://example.org/shapes/S
No matching triples found for predicate :s4
Focus: http://a.example/s3
Start: http://example.org/shapes/S
Reason: Testing :s3 against shape http://example.org/shapes/S
No matching triples found for predicate :s4""", outf.getvalue().strip())
if __name__ == '__main__':
unittest.main()
| 35.254545 | 105 | 0.66426 | import os
import unittest
from contextlib import redirect_stdout, redirect_stderr
from io import StringIO
from pyshex.shex_evaluator import evaluate_cli
data_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'data'))
validation_dir = os.path.join(data_dir, 'validation')
rdffile = os.path.join(validation_dir, 'simple.ttl')
shexfile = os.path.join(validation_dir, 'simple.shex')
class Issue25TestCase(unittest.TestCase):
def test_nostart(self):
outf = StringIO()
with(redirect_stdout(outf)):
evaluate_cli(f"{rdffile} {shexfile} -A".split())
self.assertEqual("""Errors:
Focus: None
Start: None
Reason: START node is not specified""", outf.getvalue().strip())
def test_all_nodes(self):
outf = StringIO()
with(redirect_stderr(outf)):
evaluate_cli(f"{rdffile} {shexfile} -s http://example.org/shapes/S".split())
self.assertEqual('Error: You must specify one or more graph focus nodes, supply a SPARQL query, '
'or use the "-A" option',
outf.getvalue().strip())
outf = StringIO()
with(redirect_stdout(outf)):
evaluate_cli(f"{rdffile} {shexfile} -A -s http://example.org/shapes/S".split())
self.assertEqual("""Errors:
Focus: http://a.example/s1
Start: http://example.org/shapes/S
Reason: Testing :s1 against shape http://example.org/shapes/S
No matching triples found for predicate :s4
Focus: http://a.example/s2
Start: http://example.org/shapes/S
Reason: Testing :s2 against shape http://example.org/shapes/S
No matching triples found for predicate :s4
Focus: http://a.example/s3
Start: http://example.org/shapes/S
Reason: Testing :s3 against shape http://example.org/shapes/S
No matching triples found for predicate :s4""", outf.getvalue().strip())
if __name__ == '__main__':
unittest.main()
| true | true |
f725742c34d7363a4d55d7cc29936dc1953858f7 | 3,975 | py | Python | cpp/build-support/lint_cpp_cli.py | LuoZijun/arrow | 8219a8b878d9344fe73e07def34a18a71a8f85a8 | [
"CC-BY-3.0",
"Apache-2.0",
"CC0-1.0"
] | 1 | 2020-09-15T16:47:08.000Z | 2020-09-15T16:47:08.000Z | cpp/build-support/lint_cpp_cli.py | LuoZijun/arrow | 8219a8b878d9344fe73e07def34a18a71a8f85a8 | [
"CC-BY-3.0",
"Apache-2.0",
"CC0-1.0"
] | 3 | 2018-10-25T13:52:14.000Z | 2018-10-27T08:44:27.000Z | cpp/build-support/lint_cpp_cli.py | LuoZijun/arrow | 8219a8b878d9344fe73e07def34a18a71a8f85a8 | [
"CC-BY-3.0",
"Apache-2.0",
"CC0-1.0"
] | 10 | 2019-03-18T08:19:16.000Z | 2020-09-15T09:05:39.000Z | #!/usr/bin/env python
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import argparse
import re
import os
parser = argparse.ArgumentParser(
description="Check for illegal headers for C++/CLI applications")
parser.add_argument("source_path",
help="Path to source code")
arguments = parser.parse_args()
_STRIP_COMMENT_REGEX = re.compile('(.+)?(?=//)')
_NULLPTR_REGEX = re.compile(r'.*\bnullptr\b.*')
_RETURN_NOT_OK_REGEX = re.compile(r'.*\sRETURN_NOT_OK.*')
_ASSIGN_OR_RAISE_REGEX = re.compile(r'.*\sASSIGN_OR_RAISE.*')
def _paths(paths):
return [p.strip().replace('/', os.path.sep) for p in paths.splitlines()]
def _strip_comments(line):
m = _STRIP_COMMENT_REGEX.match(line)
if not m:
return line
else:
return m.group(0)
def lint_file(path):
fail_rules = [
# rule, error message, rule-specific exclusions list
(lambda x: '<mutex>' in x, 'Uses <mutex>', []),
(lambda x: '<iostream>' in x, 'Uses <iostream>', []),
(lambda x: re.match(_NULLPTR_REGEX, x), 'Uses nullptr', []),
(lambda x: re.match(_RETURN_NOT_OK_REGEX, x),
'Use ARROW_RETURN_NOT_OK in header files', _paths('''\
arrow/status.h
test
arrow/util/hash.h
arrow/python/util''')),
(lambda x: re.match(_ASSIGN_OR_RAISE_REGEX, x),
'Use ARROW_ASSIGN_OR_RAISE in header files', _paths('''\
arrow/result_internal.h
test
'''))
]
with open(path) as f:
for i, line in enumerate(f):
stripped_line = _strip_comments(line)
for rule, why, rule_exclusions in fail_rules:
if any([True for excl in rule_exclusions if excl in path]):
continue
if rule(stripped_line):
yield path, why, i, line
EXCLUSIONS = _paths('''\
arrow/python/iterators.h
arrow/util/hashing.h
arrow/util/macros.h
arrow/util/parallel.h
arrow/vendored
arrow/visitor_inline.h
gandiva/cache.h
gandiva/jni
jni/
test
internal
_generated''')
def lint_files():
for dirpath, _, filenames in os.walk(arguments.source_path):
for filename in filenames:
full_path = os.path.join(dirpath, filename)
exclude = False
for exclusion in EXCLUSIONS:
if exclusion in full_path:
exclude = True
break
if exclude:
continue
# Lint file name, except for pkgconfig templates
if not filename.endswith('.pc.in'):
if '-' in filename:
why = ("Please user underscores, not hyphens, "
"in source file names")
yield full_path, why, 0, full_path
# Only run on header files
if filename.endswith('.h'):
for _ in lint_file(full_path):
yield _
if __name__ == '__main__':
failures = list(lint_files())
for path, why, i, line in failures:
print('File {0} failed C++/CLI lint check: {1}\n'
'Line {2}: {3}'.format(path, why, i + 1, line))
if failures:
exit(1)
| 31.054688 | 76 | 0.610063 |
import argparse
import re
import os
parser = argparse.ArgumentParser(
description="Check for illegal headers for C++/CLI applications")
parser.add_argument("source_path",
help="Path to source code")
arguments = parser.parse_args()
_STRIP_COMMENT_REGEX = re.compile('(.+)?(?=//)')
_NULLPTR_REGEX = re.compile(r'.*\bnullptr\b.*')
_RETURN_NOT_OK_REGEX = re.compile(r'.*\sRETURN_NOT_OK.*')
_ASSIGN_OR_RAISE_REGEX = re.compile(r'.*\sASSIGN_OR_RAISE.*')
def _paths(paths):
return [p.strip().replace('/', os.path.sep) for p in paths.splitlines()]
def _strip_comments(line):
m = _STRIP_COMMENT_REGEX.match(line)
if not m:
return line
else:
return m.group(0)
def lint_file(path):
fail_rules = [
(lambda x: '<mutex>' in x, 'Uses <mutex>', []),
(lambda x: '<iostream>' in x, 'Uses <iostream>', []),
(lambda x: re.match(_NULLPTR_REGEX, x), 'Uses nullptr', []),
(lambda x: re.match(_RETURN_NOT_OK_REGEX, x),
'Use ARROW_RETURN_NOT_OK in header files', _paths('''\
arrow/status.h
test
arrow/util/hash.h
arrow/python/util''')),
(lambda x: re.match(_ASSIGN_OR_RAISE_REGEX, x),
'Use ARROW_ASSIGN_OR_RAISE in header files', _paths('''\
arrow/result_internal.h
test
'''))
]
with open(path) as f:
for i, line in enumerate(f):
stripped_line = _strip_comments(line)
for rule, why, rule_exclusions in fail_rules:
if any([True for excl in rule_exclusions if excl in path]):
continue
if rule(stripped_line):
yield path, why, i, line
EXCLUSIONS = _paths('''\
arrow/python/iterators.h
arrow/util/hashing.h
arrow/util/macros.h
arrow/util/parallel.h
arrow/vendored
arrow/visitor_inline.h
gandiva/cache.h
gandiva/jni
jni/
test
internal
_generated''')
def lint_files():
for dirpath, _, filenames in os.walk(arguments.source_path):
for filename in filenames:
full_path = os.path.join(dirpath, filename)
exclude = False
for exclusion in EXCLUSIONS:
if exclusion in full_path:
exclude = True
break
if exclude:
continue
if not filename.endswith('.pc.in'):
if '-' in filename:
why = ("Please user underscores, not hyphens, "
"in source file names")
yield full_path, why, 0, full_path
if filename.endswith('.h'):
for _ in lint_file(full_path):
yield _
if __name__ == '__main__':
failures = list(lint_files())
for path, why, i, line in failures:
print('File {0} failed C++/CLI lint check: {1}\n'
'Line {2}: {3}'.format(path, why, i + 1, line))
if failures:
exit(1)
| true | true |
f72574687e5a2e271c479e18ac1e881bdf5eae75 | 578 | py | Python | azure-mgmt-consumption/azure/mgmt/consumption/models/consumption_management_client_enums.py | v-Ajnava/azure-sdk-for-python | a1f6f80eb5869c5b710e8bfb66146546697e2a6f | [
"MIT"
] | 4 | 2016-06-17T23:25:29.000Z | 2022-03-30T22:37:45.000Z | azure-mgmt-consumption/azure/mgmt/consumption/models/consumption_management_client_enums.py | v-Ajnava/azure-sdk-for-python | a1f6f80eb5869c5b710e8bfb66146546697e2a6f | [
"MIT"
] | 2 | 2016-09-30T21:40:24.000Z | 2017-11-10T18:16:18.000Z | azure-mgmt-consumption/azure/mgmt/consumption/models/consumption_management_client_enums.py | v-Ajnava/azure-sdk-for-python | a1f6f80eb5869c5b710e8bfb66146546697e2a6f | [
"MIT"
] | 3 | 2016-05-03T20:49:46.000Z | 2017-10-05T21:05:27.000Z | # coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from enum import Enum
class Datagrain(Enum):
daily_grain = "daily"
monthly_grain = "monthly"
| 30.421053 | 76 | 0.550173 |
from enum import Enum
class Datagrain(Enum):
daily_grain = "daily"
monthly_grain = "monthly"
| true | true |
f725747bf99d90df726266e21ecdd3c5dc730fc5 | 16,227 | py | Python | pyfarm/models/core/mixins.py | guidow/pyfarm-master | d41c8f1eb5bfefb8400d400bcecadf197bcfb80a | [
"Apache-2.0"
] | null | null | null | pyfarm/models/core/mixins.py | guidow/pyfarm-master | d41c8f1eb5bfefb8400d400bcecadf197bcfb80a | [
"Apache-2.0"
] | null | null | null | pyfarm/models/core/mixins.py | guidow/pyfarm-master | d41c8f1eb5bfefb8400d400bcecadf197bcfb80a | [
"Apache-2.0"
] | null | null | null | # No shebang line, this module is meant to be imported
#
# Copyright 2013 Oliver Palmer
#
# 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.
"""
Mixin Classes
=============
Module containing mixins which can be used by multiple models.
"""
from datetime import datetime
from collections import namedtuple
try:
from httplib import INTERNAL_SERVER_ERROR
except ImportError:
from http.client import INTERNAL_SERVER_ERROR
from sqlalchemy.orm import validates, class_mapper
from pyfarm.core.enums import _WorkState, Values, PY2
from pyfarm.core.logger import getLogger
from pyfarm.models.core.types import IPAddress
from pyfarm.master.config import config
logger = getLogger("models.mixin")
# stores information about a model's columns
# and relationships
ModelTypes = namedtuple(
"ModelTypes",
("primary_keys", "autoincrementing", "columns", "required",
"relationships", "mappings"))
class ValidatePriorityMixin(object):
"""
Mixin that adds a `state` column and uses a class
level `STATE_ENUM` attribute to assist in validation.
"""
MIN_PRIORITY = config.get("queue_min_priority")
MAX_PRIORITY = config.get("queue_max_priority")
if MAX_PRIORITY <= MIN_PRIORITY:
raise AssertionError(
"`queue_min_priority` must be <= `queue_max_priority`")
@validates("priority")
def validate_priority(self, key, value):
"""ensures the value provided to priority is valid"""
if value is None or self.MIN_PRIORITY <= value <= self.MAX_PRIORITY:
return value
err_args = (key, self.MIN_PRIORITY, self.MAX_PRIORITY, value)
raise ValueError(
"%s must be between %s and %s, got %s instead" % err_args)
@validates("attempts")
def validate_attempts(self, key, value):
"""ensures the number of attempts provided is valid"""
if value is None or value >= 0:
return value
raise ValueError("%s cannot be less than zero" % key)
class ValidateWorkStateMixin(object):
STATE_ENUM = NotImplemented
def validate_state(self, key, value):
"""Ensures that ``value`` is a member of ``STATE_ENUM``"""
assert self.STATE_ENUM is not NotImplemented
if value not in self.STATE_ENUM:
raise ValueError("`%s` is not a valid state" % value)
return value
@validates("state")
def validate_state_column(self, key, value):
"""validates the state column"""
return self.validate_state(key, value)
class WorkStateChangedMixin(object):
"""
Mixin which adds a static method to be used when the model
state changes
"""
@staticmethod
def state_changed(target, new_value, old_value, initiator):
"""update the datetime objects depending on the new value"""
if (new_value == _WorkState.RUNNING and
(old_value not in [_WorkState.RUNNING, _WorkState.PAUSED] or
target.time_started == None)):
target.time_started = datetime.utcnow()
target.time_finished = None
elif new_value in (_WorkState.DONE, _WorkState.FAILED):
target.time_finished = datetime.utcnow()
class UtilityMixins(object):
"""
Mixins which can be used to produce dictionaries
of existing data.
:const dict DICT_CONVERT_COLUMN:
A dictionary containing key value pairs of attribute names
and a function to retrieve the attribute. The function should
take a single input and return the value itself. Optionally,
you can also use the ``NotImplemented`` object to exclude
some columns from the results.
"""
DICT_CONVERT_COLUMN = {}
def _to_dict_column(self, name):
"""
Default method used by :meth:`.to_dict` to convert a column to
a standard value.
"""
value = getattr(self, name)
if isinstance(value, Values):
return value.str
elif isinstance(value, IPAddress):
return str(value)
else:
return value
def _to_dict_relationship(self, name):
"""
Default method used by :meth:`.to_dict` to convert a relationship
to a standard value. In the event this method does not know
how to unpack a relationship it will raise a ``NotImplementedError``
"""
relation = getattr(self.__class__, name)
relation_object = getattr(self, name)
if relation_object is None:
return
if relation.property.uselist:
out = []
for relationship in relation_object:
if name == "tags":
out.append(relationship.tag)
elif name == "projects":
out.append(relationship.name)
elif name == "software":
out.append(relationship.name)
elif name == "versions":
out.append({"id": relationship.id,
"version": relationship.version,
"rank": relationship.rank})
elif name == "software_versions":
out.append({"id": relationship.id,
"software": relationship.software.software,
"version": relationship.version,
"rank": relationship.rank})
elif name in ("jobs", "agents"):
out.append(relationship.id)
elif name == "software_requirements":
out.append({"software_id": relationship.software_id,
"software": relationship.software.software,
"min_version_id": relationship.min_version_id,
"min_version":
(relationship.min_version.version
if relationship.min_version else None),
"max_version_id": relationship.max_version_id,
"max_version":
(relationship.max_version.version
if relationship.max_version else None)})
elif name in ("tasks", "tasks_queued", "tasks_done",
"tasks_failed"):
out.append({"id": relationship.id,
"frame": relationship.frame,
"state": str(relationship.state)})
elif name == "notified_users":
out.append({"id": relationship.user_id,
"username": relationship.user.username,
"email": relationship.user.email,
"on_success": relationship.on_success,
"on_failure": relationship.on_failure,
"on_deletion": relationship.on_deletion})
elif name == "parents":
out.append({"id": relationship.id,
"title": relationship.title})
elif name == "children":
out.append({"id": relationship.id,
"title": relationship.title})
elif name == "tag_requirements":
out.append({"tag": relationship.tag.tag,
"negate": relationship.negate})
elif name == "gpus":
out.append({"fullname": relationship.fullname})
elif name == "disks":
out.append({"mountpoint": relationship.mountpoint,
"size": relationship.size,
"free": relationship.free})
else:
raise NotImplementedError(
"don't know how to unpack relationships for `%s`" % name)
else:
if name == "software":
out = {"software": relation_object.software,
"id": relation_object.id}
elif name == "jobtype_version":
out = {"version": relation_object.version,
"jobtype": relation_object.jobtype.name}
elif name in ("min_version", "max_version"):
out = {"id": relation_object.id,
"version": relation_object.version}
elif name == "job":
out = {"id": relation_object.id,
"title": relation_object.title}
elif name == "agent":
out = {"id": relation_object.id,
"hostname": relation_object.hostname,
"remote_ip": str(relation_object.remote_ip),
"port": relation_object.port}
elif name == "parent":
out = {"id": relation_object.id,
"name": relation_object.name,
"priority": relation_object.priority,
"weight": relation_object.weight,
"maximum_agents": relation_object.maximum_agents,
"minimum_agents": relation_object.minimum_agents}
elif name == "user":
out = relation_object.username
elif name == "main_jobtype":
out = relation_object.name
else:
raise NotImplementedError(
"don't know how to unpack relationships for `%s`" % name)
return out
def to_dict(self, unpack_relationships=True):
"""
Produce a dictionary of existing data in the table
:type unpack_relationships: list, tuple, set, bool
:param unpack_relationships:
If ``True`` then unpack all relationships. If
``unpack_relationships`` is an iterable such as a list or
tuple object then only unpack those relationships.
"""
if not isinstance(self.DICT_CONVERT_COLUMN, dict):
raise TypeError(
"expected %s.DICT_CONVERT_COLUMN to "
"be a dictionary" % self.__class__.__name__)
results = {}
types = self.types()
# first convert all the non-relationship columns
for name in types.columns:
converter = self.DICT_CONVERT_COLUMN.get(
name, self._to_dict_column)
if converter is NotImplemented:
continue
elif not callable(converter):
raise TypeError(
"converter function for %s was not callable" % name)
else:
results[name] = converter(name)
# unpack all relationships
if unpack_relationships is True:
relationships = types.relationships
# unpack the intersection of the requested relationships
# and the real relationships
elif isinstance(unpack_relationships, (list, set, tuple)):
relationships = set(unpack_relationships) & types.relationships
else:
relationships = set()
for name in relationships:
converter = self.DICT_CONVERT_COLUMN.get(
name, self._to_dict_relationship)
if converter is NotImplemented:
continue
elif not callable(converter):
raise TypeError(
"converter function for %s was not callable" % name)
else:
results[name] = converter(name)
return results
@classmethod
def to_schema(cls):
"""
Produce a dictionary which represents the
table's schema in a basic format
"""
result = {}
for name in cls.types().columns:
column = cls.__table__.c[name]
try:
column.type.python_type
except NotImplementedError:
result[name] = column.type.__class__.__name__
else:
result[name] = str(column.type)
return result
@classmethod
def types(cls):
"""
A classmethod that constructs a ``namedtuple`` object with four
attributes:
* primary_keys - set of all primary key(s) names
* autoincrementing - set of all columns which have autoincrement set
* columns - set of all column names
* required - set of all required columns (non-nullable wo/defaults)
* relationships - not columns themselves but do store relationships
* mappings - contains a dictionary with each field mapping to a
Python type
"""
mapper = class_mapper(cls)
primary_keys = set()
autoincrementing = set()
columns = set()
required = set()
relationships = set(
name for name, column in mapper.relationships.items())
# TODO: it's possible though unlikely, based on our current tables,
# that a relationship this could be some other than a list
type_mapping = dict((name, list) for name in relationships)
# create sets for all true columns, primary keys,
# and required columns
for name, column in mapper.c.items():
columns.add(name)
if column.primary_key:
primary_keys.add(name)
if column.autoincrement:
autoincrementing.add(name)
if column.primary_key and not column.autoincrement:
required.add(name)
if not column.nullable and column.default is None:
required.add(name)
# get the Python type(s)
try:
python_types = column.type.python_type
except NotImplementedError: # custom type object
python_types = column.type.json_types
# if we're using Python 2.x be sure that we include
# a couple of extra types that could potentially
# come in with a request
if PY2 and python_types is str:
# pylint: disable=undefined-variable
python_types = (python_types, unicode)
elif PY2 and python_types is int:
# pylint: disable=undefined-variable
python_types = (python_types, long)
type_mapping[name] = python_types
return ModelTypes(
primary_keys=primary_keys,
autoincrementing=autoincrementing,
columns=columns,
required=required,
relationships=relationships,
mappings=type_mapping)
class ReprMixin(object):
"""
Mixin which allows model classes to to convert columns into a more
easily read object format.
:cvar tuple REPR_COLUMNS:
the columns to convert
:cvar dict REPR_CONVERT_COLUMN:
optional dictionary containing columns names and functions
for converting to a more readable string format
"""
REPR_COLUMNS = NotImplemented
REPR_CONVERT_COLUMN = {}
def __repr__(self):
if self.REPR_COLUMNS is NotImplemented:
return super(ReprMixin, self).__repr__()
column_data = []
for name in self.REPR_COLUMNS:
convert = self.REPR_CONVERT_COLUMN.get(name, repr)
try:
column_data.append(
"%s=%s" % (name, convert(getattr(self, name))))
except AttributeError:
logger.warning("%s has no such column %s" % (
self.__class__.__name__, repr(name)))
return "%s(%s)" % (self.__class__.__name__, ", ".join(column_data))
| 37.21789 | 81 | 0.570038 |
from datetime import datetime
from collections import namedtuple
try:
from httplib import INTERNAL_SERVER_ERROR
except ImportError:
from http.client import INTERNAL_SERVER_ERROR
from sqlalchemy.orm import validates, class_mapper
from pyfarm.core.enums import _WorkState, Values, PY2
from pyfarm.core.logger import getLogger
from pyfarm.models.core.types import IPAddress
from pyfarm.master.config import config
logger = getLogger("models.mixin")
# and relationships
ModelTypes = namedtuple(
"ModelTypes",
("primary_keys", "autoincrementing", "columns", "required",
"relationships", "mappings"))
class ValidatePriorityMixin(object):
MIN_PRIORITY = config.get("queue_min_priority")
MAX_PRIORITY = config.get("queue_max_priority")
if MAX_PRIORITY <= MIN_PRIORITY:
raise AssertionError(
"`queue_min_priority` must be <= `queue_max_priority`")
@validates("priority")
def validate_priority(self, key, value):
if value is None or self.MIN_PRIORITY <= value <= self.MAX_PRIORITY:
return value
err_args = (key, self.MIN_PRIORITY, self.MAX_PRIORITY, value)
raise ValueError(
"%s must be between %s and %s, got %s instead" % err_args)
@validates("attempts")
def validate_attempts(self, key, value):
if value is None or value >= 0:
return value
raise ValueError("%s cannot be less than zero" % key)
class ValidateWorkStateMixin(object):
STATE_ENUM = NotImplemented
def validate_state(self, key, value):
assert self.STATE_ENUM is not NotImplemented
if value not in self.STATE_ENUM:
raise ValueError("`%s` is not a valid state" % value)
return value
@validates("state")
def validate_state_column(self, key, value):
return self.validate_state(key, value)
class WorkStateChangedMixin(object):
@staticmethod
def state_changed(target, new_value, old_value, initiator):
if (new_value == _WorkState.RUNNING and
(old_value not in [_WorkState.RUNNING, _WorkState.PAUSED] or
target.time_started == None)):
target.time_started = datetime.utcnow()
target.time_finished = None
elif new_value in (_WorkState.DONE, _WorkState.FAILED):
target.time_finished = datetime.utcnow()
class UtilityMixins(object):
DICT_CONVERT_COLUMN = {}
def _to_dict_column(self, name):
value = getattr(self, name)
if isinstance(value, Values):
return value.str
elif isinstance(value, IPAddress):
return str(value)
else:
return value
def _to_dict_relationship(self, name):
relation = getattr(self.__class__, name)
relation_object = getattr(self, name)
if relation_object is None:
return
if relation.property.uselist:
out = []
for relationship in relation_object:
if name == "tags":
out.append(relationship.tag)
elif name == "projects":
out.append(relationship.name)
elif name == "software":
out.append(relationship.name)
elif name == "versions":
out.append({"id": relationship.id,
"version": relationship.version,
"rank": relationship.rank})
elif name == "software_versions":
out.append({"id": relationship.id,
"software": relationship.software.software,
"version": relationship.version,
"rank": relationship.rank})
elif name in ("jobs", "agents"):
out.append(relationship.id)
elif name == "software_requirements":
out.append({"software_id": relationship.software_id,
"software": relationship.software.software,
"min_version_id": relationship.min_version_id,
"min_version":
(relationship.min_version.version
if relationship.min_version else None),
"max_version_id": relationship.max_version_id,
"max_version":
(relationship.max_version.version
if relationship.max_version else None)})
elif name in ("tasks", "tasks_queued", "tasks_done",
"tasks_failed"):
out.append({"id": relationship.id,
"frame": relationship.frame,
"state": str(relationship.state)})
elif name == "notified_users":
out.append({"id": relationship.user_id,
"username": relationship.user.username,
"email": relationship.user.email,
"on_success": relationship.on_success,
"on_failure": relationship.on_failure,
"on_deletion": relationship.on_deletion})
elif name == "parents":
out.append({"id": relationship.id,
"title": relationship.title})
elif name == "children":
out.append({"id": relationship.id,
"title": relationship.title})
elif name == "tag_requirements":
out.append({"tag": relationship.tag.tag,
"negate": relationship.negate})
elif name == "gpus":
out.append({"fullname": relationship.fullname})
elif name == "disks":
out.append({"mountpoint": relationship.mountpoint,
"size": relationship.size,
"free": relationship.free})
else:
raise NotImplementedError(
"don't know how to unpack relationships for `%s`" % name)
else:
if name == "software":
out = {"software": relation_object.software,
"id": relation_object.id}
elif name == "jobtype_version":
out = {"version": relation_object.version,
"jobtype": relation_object.jobtype.name}
elif name in ("min_version", "max_version"):
out = {"id": relation_object.id,
"version": relation_object.version}
elif name == "job":
out = {"id": relation_object.id,
"title": relation_object.title}
elif name == "agent":
out = {"id": relation_object.id,
"hostname": relation_object.hostname,
"remote_ip": str(relation_object.remote_ip),
"port": relation_object.port}
elif name == "parent":
out = {"id": relation_object.id,
"name": relation_object.name,
"priority": relation_object.priority,
"weight": relation_object.weight,
"maximum_agents": relation_object.maximum_agents,
"minimum_agents": relation_object.minimum_agents}
elif name == "user":
out = relation_object.username
elif name == "main_jobtype":
out = relation_object.name
else:
raise NotImplementedError(
"don't know how to unpack relationships for `%s`" % name)
return out
def to_dict(self, unpack_relationships=True):
if not isinstance(self.DICT_CONVERT_COLUMN, dict):
raise TypeError(
"expected %s.DICT_CONVERT_COLUMN to "
"be a dictionary" % self.__class__.__name__)
results = {}
types = self.types()
# first convert all the non-relationship columns
for name in types.columns:
converter = self.DICT_CONVERT_COLUMN.get(
name, self._to_dict_column)
if converter is NotImplemented:
continue
elif not callable(converter):
raise TypeError(
"converter function for %s was not callable" % name)
else:
results[name] = converter(name)
# unpack all relationships
if unpack_relationships is True:
relationships = types.relationships
# unpack the intersection of the requested relationships
# and the real relationships
elif isinstance(unpack_relationships, (list, set, tuple)):
relationships = set(unpack_relationships) & types.relationships
else:
relationships = set()
for name in relationships:
converter = self.DICT_CONVERT_COLUMN.get(
name, self._to_dict_relationship)
if converter is NotImplemented:
continue
elif not callable(converter):
raise TypeError(
"converter function for %s was not callable" % name)
else:
results[name] = converter(name)
return results
@classmethod
def to_schema(cls):
result = {}
for name in cls.types().columns:
column = cls.__table__.c[name]
try:
column.type.python_type
except NotImplementedError:
result[name] = column.type.__class__.__name__
else:
result[name] = str(column.type)
return result
@classmethod
def types(cls):
mapper = class_mapper(cls)
primary_keys = set()
autoincrementing = set()
columns = set()
required = set()
relationships = set(
name for name, column in mapper.relationships.items())
# TODO: it's possible though unlikely, based on our current tables,
type_mapping = dict((name, list) for name in relationships)
for name, column in mapper.c.items():
columns.add(name)
if column.primary_key:
primary_keys.add(name)
if column.autoincrement:
autoincrementing.add(name)
if column.primary_key and not column.autoincrement:
required.add(name)
if not column.nullable and column.default is None:
required.add(name)
try:
python_types = column.type.python_type
except NotImplementedError:
python_types = column.type.json_types
# a couple of extra types that could potentially
# come in with a request
if PY2 and python_types is str:
# pylint: disable=undefined-variable
python_types = (python_types, unicode)
elif PY2 and python_types is int:
# pylint: disable=undefined-variable
python_types = (python_types, long)
type_mapping[name] = python_types
return ModelTypes(
primary_keys=primary_keys,
autoincrementing=autoincrementing,
columns=columns,
required=required,
relationships=relationships,
mappings=type_mapping)
class ReprMixin(object):
REPR_COLUMNS = NotImplemented
REPR_CONVERT_COLUMN = {}
def __repr__(self):
if self.REPR_COLUMNS is NotImplemented:
return super(ReprMixin, self).__repr__()
column_data = []
for name in self.REPR_COLUMNS:
convert = self.REPR_CONVERT_COLUMN.get(name, repr)
try:
column_data.append(
"%s=%s" % (name, convert(getattr(self, name))))
except AttributeError:
logger.warning("%s has no such column %s" % (
self.__class__.__name__, repr(name)))
return "%s(%s)" % (self.__class__.__name__, ", ".join(column_data))
| true | true |
f7257517d39b2f8675741bc3a48ae828e67d5468 | 495 | py | Python | src/commercetools/services/abstract.py | labd/commercetools-python-sdk | d8ec285f08d56ede2e4cad45c74833f5b609ab5c | [
"MIT"
] | 15 | 2018-11-02T14:35:52.000Z | 2022-03-16T07:51:44.000Z | src/commercetools/services/abstract.py | lime-green/commercetools-python-sdk | 63b77f6e5abe43e2b3ebbf3cdbbe00c7cf80dca6 | [
"MIT"
] | 84 | 2018-11-02T12:50:32.000Z | 2022-03-22T01:25:54.000Z | src/commercetools/services/abstract.py | lime-green/commercetools-python-sdk | 63b77f6e5abe43e2b3ebbf3cdbbe00c7cf80dca6 | [
"MIT"
] | 13 | 2019-01-03T09:16:50.000Z | 2022-02-15T18:37:19.000Z | import typing
from marshmallow.base import SchemaABC
if typing.TYPE_CHECKING:
from commercetools.client import Client
class AbstractService:
def __init__(self, client: "Client") -> None:
self._client = client
self._schemas: typing.Dict[str, SchemaABC] = {}
def _serialize_params(self, params, schema) -> typing.Dict[str, str]:
if schema not in self._schemas:
self._schemas[schema] = schema()
return self._schemas[schema].dump(params)
| 27.5 | 73 | 0.684848 | import typing
from marshmallow.base import SchemaABC
if typing.TYPE_CHECKING:
from commercetools.client import Client
class AbstractService:
def __init__(self, client: "Client") -> None:
self._client = client
self._schemas: typing.Dict[str, SchemaABC] = {}
def _serialize_params(self, params, schema) -> typing.Dict[str, str]:
if schema not in self._schemas:
self._schemas[schema] = schema()
return self._schemas[schema].dump(params)
| true | true |
f72576ba10ecc45859e387a54131b2e173076e5e | 13,744 | py | Python | pinpayments/tests/models.py | branchup/django-pinpayments | e342970c6309facf35b804de9994d326abaa094f | [
"Unlicense"
] | null | null | null | pinpayments/tests/models.py | branchup/django-pinpayments | e342970c6309facf35b804de9994d326abaa094f | [
"Unlicense"
] | null | null | null | pinpayments/tests/models.py | branchup/django-pinpayments | e342970c6309facf35b804de9994d326abaa094f | [
"Unlicense"
] | null | null | null | """ Ensure that the models work as intended """
import json
from django.conf import settings
from django.contrib.auth.models import User
from django.test import TestCase
from django.test.utils import override_settings
from mock import patch
from pinpayments.models import (
ConfigError,
CustomerToken,
PinError,
PinTransaction
)
from requests import Response
ENV_MISSING_SECRET = {
'test': {
'key': 'key1',
'host': 'test-api.pin.net.au',
},
}
ENV_MISSING_HOST = {
'test': {
'key': 'key1',
'secret': 'secret1',
},
}
class FakeResponse(Response):
def __init__(self, status_code, content):
super(FakeResponse, self).__init__()
self.status_code = status_code
self._content = content
class CustomerTokenTests(TestCase):
# Need to override the setting so we can delete it, not sure why.
@override_settings(PIN_DEFAULT_ENVIRONMENT=None)
def test_default_environment(self):
"""
Unset PIN_DEFAULT_ENVIRONMENT to test that the environment defaults
to 'test'.
"""
del settings.PIN_DEFAULT_ENVIRONMENT
token = CustomerToken()
token.user = User.objects.create()
token.environment = None
token.save()
self.assertEqual(token.environment, 'test')
class CreateFromCardTokenTests(TestCase):
""" Test the creation of customer tokens from card tokens """
def setUp(self):
""" Common setup for methods """
super(CreateFromCardTokenTests, self).setUp()
self.user = User.objects.create()
self.response_data = json.dumps({
'response': {
'token': '1234',
'email': 'test@example.com',
'created_at': '2012-06-22T06:27:33Z',
'card': {
'token': '54321',
'display_number': 'XXXX-XXXX-XXXX-0000',
'scheme': 'master',
'expiry_month': 6,
'expiry_year': 2017,
'name': 'Roland Robot',
'address_line1': '42 Sevenoaks St',
'address_line2': None,
'address_city': 'Lathlain',
'address_postcode': '6454',
'address_state': 'WA',
'address_country': 'Australia',
'primary': None,
}
}
})
self.response_error = json.dumps({
'error': 'invalid_resource',
'error_description':
'One or more parameters were missing or invalid.'
})
@patch('requests.post')
def test_default_environment(self, mock_request):
""" return a default environment """
mock_request.return_value = FakeResponse(200, self.response_data)
token = CustomerToken.create_from_card_token('1234', self.user)
self.assertEqual(token.environment, 'test')
@override_settings(PIN_ENVIRONMENTS={})
@patch('requests.post')
def test_valid_environment(self, mock_request):
""" Check errors are raised with no environments """
mock_request.return_value = FakeResponse(200, self.response_data)
with self.assertRaises(ConfigError):
CustomerToken.create_from_card_token(
'1234', self.user, environment='test'
)
@override_settings(PIN_ENVIRONMENTS=ENV_MISSING_SECRET)
@patch('requests.post')
def test_secret_set(self, mock_request):
""" Check errors are raised when the secret is not set """
mock_request.return_value = FakeResponse(200, self.response_data)
with self.assertRaises(ConfigError):
CustomerToken.create_from_card_token(
'1234', self.user, environment='test'
)
@override_settings(PIN_ENVIRONMENTS=ENV_MISSING_HOST)
@patch('requests.post')
def test_host_set(self, mock_request):
""" Check errors are raised when the host is not set """
mock_request.return_value = FakeResponse(200, self.response_data)
with self.assertRaises(ConfigError):
CustomerToken.create_from_card_token(
'1234', self.user, environment='test'
)
@patch('requests.post')
def test_response_not_json(self, mock_request):
""" Validate non-json response """
mock_request.return_value = FakeResponse(200, '')
with self.assertRaises(PinError):
CustomerToken.create_from_card_token(
'1234', self.user, environment='test'
)
@patch('requests.post')
def test_response_error(self, mock_request):
""" Validate generic error response """
mock_request.return_value = FakeResponse(200, self.response_error)
with self.assertRaises(PinError):
CustomerToken.create_from_card_token(
'1234', self.user, environment='test'
)
@patch('requests.post')
def test_response_success(self, mock_request):
""" Validate successful response """
mock_request.return_value = FakeResponse(200, self.response_data)
customer = CustomerToken.create_from_card_token(
'1234', self.user, environment='test'
)
self.assertIsInstance(customer, CustomerToken)
self.assertEqual(customer.user, self.user)
self.assertEqual(customer.token, '1234')
self.assertEqual(customer.environment, 'test')
self.assertEqual(customer.card_number, 'XXXX-XXXX-XXXX-0000')
self.assertEqual(customer.card_type, 'master')
class PinTransactionTests(TestCase):
""" Transaction construction/init related tests """
def setUp(self):
""" Common setup for methods """
super(PinTransactionTests, self).setUp()
self.transaction = PinTransaction()
self.transaction.card_token = '12345'
self.transaction.ip_address = '127.0.0.1'
self.transaction.amount = 500
self.transaction.currency = 'AUD'
self.transaction.email_address = 'test@example.com'
self.transaction.environment = 'test'
# Need to override the setting so we can delete it, not sure why.
@override_settings(PIN_DEFAULT_ENVIRONMENT=None)
def test_save_defaults(self):
"""
Unset PIN_DEFAULT_ENVIRONMENT to test that the environment defaults
to 'test'.
"""
del settings.PIN_DEFAULT_ENVIRONMENT
self.transaction.environment = None
self.transaction.save()
self.assertEqual(self.transaction.environment, 'test')
self.assertTrue(self.transaction.date)
def test_save_notokens(self):
"""
Check that an error is thrown if neither card nor customer token
are provided to the transaction
"""
self.transaction.card_token = None
self.transaction.customer_token = None
self.assertRaises(PinError, self.transaction.save)
def test_valid_environment(self):
"""
Check that errors are thrown when a fake environment is requested
"""
self.transaction.environment = 'this should not exist'
self.assertRaises(PinError, self.transaction.save)
class ProcessTransactionsTests(TestCase):
""" Transaction processing related tests """
def setUp(self):
""" Common setup for methods """
super(ProcessTransactionsTests, self).setUp()
self.transaction = PinTransaction()
self.transaction.card_token = '12345'
self.transaction.ip_address = '127.0.0.1'
self.transaction.amount = 500
self.transaction.currency = 'AUD'
self.transaction.email_address = 'test@example.com'
self.transaction.environment = 'test'
self.transaction.save()
self.response_data = json.dumps({
'response': {
'token': '12345',
'success': True,
'amount': 500,
'total_fees': 500,
'currency': 'AUD',
'description': 'test charge',
'email': 'test@example.com',
'ip_address': '127.0.0.1',
'created_at': '2012-06-20T03:10:49Z',
'status_message': 'Success!',
'error_message': None,
'card': {
'token': 'card_nytGw7koRg23EEp9NTmz9w',
'display_number': 'XXXX-XXXX-XXXX-0000',
'scheme': 'master',
'expiry_month': 6,
'expiry_year': 2017,
'name': 'Roland Robot',
'address_line1': '42 Sevenoaks St',
'address_line2': None,
'address_city': 'Lathlain',
'address_postcode': '6454',
'address_state': 'WA',
'address_country': 'Australia',
'primary': None,
},
'transfer': None
}
})
self.response_error = json.dumps({
'error': 'invalid_resource',
'error_description':
'One or more parameters were missing or invalid.',
# Should there really be a charge token?
'charge_token': '1234',
'messages': [{
'code': 'description_invalid',
'message': 'Description can\'t be blank',
'param': 'description'
}]
})
self.response_error_no_messages = json.dumps({
'error': 'invalid_resource',
'error_description':
'One or more parameters were missing or invalid.',
# Should there really be a charge token?
'charge_token': '1234'
})
@patch('requests.post')
def test_only_process_once(self, mock_request):
""" Check that transactions are processed exactly once """
mock_request.return_value = FakeResponse(200, self.response_data)
# Shouldn't be marked as processed before process_transaction is called
# for the first time.
self.assertFalse(self.transaction.processed)
# Should be marked after the first call.
result = self.transaction.process_transaction()
self.assertTrue(self.transaction.processed)
# Shouldn't process anything the second time
result = self.transaction.process_transaction()
self.assertIsNone(result)
@override_settings(PIN_ENVIRONMENTS={})
@patch('requests.post')
def test_valid_environment(self, mock_request):
""" Check that an error is thrown with no environment """
mock_request.return_value = FakeResponse(200, self.response_data)
self.assertRaises(PinError, self.transaction.process_transaction)
@override_settings(PIN_ENVIRONMENTS=ENV_MISSING_SECRET)
@patch('requests.post')
def test_secret_set(self, mock_request):
""" Check that an error is thrown with no secret """
mock_request.return_value = FakeResponse(200, self.response_data)
self.assertRaises(ConfigError, self.transaction.process_transaction)
@override_settings(PIN_ENVIRONMENTS=ENV_MISSING_HOST)
@patch('requests.post')
def test_host_set(self, mock_request):
""" Check that an error is thrown with no host """
mock_request.return_value = FakeResponse(200, self.response_data)
self.assertRaises(ConfigError, self.transaction.process_transaction)
@patch('requests.post')
def test_response_not_json(self, mock_request):
""" Check that failure is returned for non-JSON responses """
mock_request.return_value = FakeResponse(200, '')
response = self.transaction.process_transaction()
self.assertEqual(response, 'Failure.')
@patch('requests.post')
def test_response_badparam(self, mock_request):
""" Check that a specific error is thrown for invalid parameters """
mock_request.return_value = FakeResponse(200, self.response_error)
response = self.transaction.process_transaction()
self.assertEqual(response, 'Failure: Description can\'t be blank')
@patch('requests.post')
def test_response_noparam(self, mock_request):
""" Check that a specific error is thrown for missing parameters """
mock_request.return_value = FakeResponse(
200, self.response_error_no_messages
)
response = self.transaction.process_transaction()
self.assertEqual(
response,
'Failure: One or more parameters were missing or invalid.'
)
@patch('requests.post')
def test_response_success(self, mock_request):
""" Check that the success response is correctly processed """
mock_request.return_value = FakeResponse(200, self.response_data)
response = self.transaction.process_transaction()
self.assertEqual(response, 'Success!')
self.assertTrue(self.transaction.succeeded)
self.assertEqual(self.transaction.transaction_token, '12345')
self.assertEqual(self.transaction.fees, 5.0)
self.assertEqual(self.transaction.pin_response, 'Success!')
self.assertEqual(self.transaction.card_address1, '42 Sevenoaks St')
self.assertIsNone(self.transaction.card_address2)
self.assertEqual(self.transaction.card_city, 'Lathlain')
self.assertEqual(self.transaction.card_state, 'WA')
self.assertEqual(self.transaction.card_postcode, '6454')
self.assertEqual(self.transaction.card_country, 'Australia')
self.assertEqual(self.transaction.card_number, 'XXXX-XXXX-XXXX-0000')
self.assertEqual(self.transaction.card_type, 'master')
| 39.608069 | 79 | 0.621799 | import json
from django.conf import settings
from django.contrib.auth.models import User
from django.test import TestCase
from django.test.utils import override_settings
from mock import patch
from pinpayments.models import (
ConfigError,
CustomerToken,
PinError,
PinTransaction
)
from requests import Response
ENV_MISSING_SECRET = {
'test': {
'key': 'key1',
'host': 'test-api.pin.net.au',
},
}
ENV_MISSING_HOST = {
'test': {
'key': 'key1',
'secret': 'secret1',
},
}
class FakeResponse(Response):
def __init__(self, status_code, content):
super(FakeResponse, self).__init__()
self.status_code = status_code
self._content = content
class CustomerTokenTests(TestCase):
@override_settings(PIN_DEFAULT_ENVIRONMENT=None)
def test_default_environment(self):
del settings.PIN_DEFAULT_ENVIRONMENT
token = CustomerToken()
token.user = User.objects.create()
token.environment = None
token.save()
self.assertEqual(token.environment, 'test')
class CreateFromCardTokenTests(TestCase):
def setUp(self):
super(CreateFromCardTokenTests, self).setUp()
self.user = User.objects.create()
self.response_data = json.dumps({
'response': {
'token': '1234',
'email': 'test@example.com',
'created_at': '2012-06-22T06:27:33Z',
'card': {
'token': '54321',
'display_number': 'XXXX-XXXX-XXXX-0000',
'scheme': 'master',
'expiry_month': 6,
'expiry_year': 2017,
'name': 'Roland Robot',
'address_line1': '42 Sevenoaks St',
'address_line2': None,
'address_city': 'Lathlain',
'address_postcode': '6454',
'address_state': 'WA',
'address_country': 'Australia',
'primary': None,
}
}
})
self.response_error = json.dumps({
'error': 'invalid_resource',
'error_description':
'One or more parameters were missing or invalid.'
})
@patch('requests.post')
def test_default_environment(self, mock_request):
mock_request.return_value = FakeResponse(200, self.response_data)
token = CustomerToken.create_from_card_token('1234', self.user)
self.assertEqual(token.environment, 'test')
@override_settings(PIN_ENVIRONMENTS={})
@patch('requests.post')
def test_valid_environment(self, mock_request):
mock_request.return_value = FakeResponse(200, self.response_data)
with self.assertRaises(ConfigError):
CustomerToken.create_from_card_token(
'1234', self.user, environment='test'
)
@override_settings(PIN_ENVIRONMENTS=ENV_MISSING_SECRET)
@patch('requests.post')
def test_secret_set(self, mock_request):
mock_request.return_value = FakeResponse(200, self.response_data)
with self.assertRaises(ConfigError):
CustomerToken.create_from_card_token(
'1234', self.user, environment='test'
)
@override_settings(PIN_ENVIRONMENTS=ENV_MISSING_HOST)
@patch('requests.post')
def test_host_set(self, mock_request):
mock_request.return_value = FakeResponse(200, self.response_data)
with self.assertRaises(ConfigError):
CustomerToken.create_from_card_token(
'1234', self.user, environment='test'
)
@patch('requests.post')
def test_response_not_json(self, mock_request):
mock_request.return_value = FakeResponse(200, '')
with self.assertRaises(PinError):
CustomerToken.create_from_card_token(
'1234', self.user, environment='test'
)
@patch('requests.post')
def test_response_error(self, mock_request):
mock_request.return_value = FakeResponse(200, self.response_error)
with self.assertRaises(PinError):
CustomerToken.create_from_card_token(
'1234', self.user, environment='test'
)
@patch('requests.post')
def test_response_success(self, mock_request):
mock_request.return_value = FakeResponse(200, self.response_data)
customer = CustomerToken.create_from_card_token(
'1234', self.user, environment='test'
)
self.assertIsInstance(customer, CustomerToken)
self.assertEqual(customer.user, self.user)
self.assertEqual(customer.token, '1234')
self.assertEqual(customer.environment, 'test')
self.assertEqual(customer.card_number, 'XXXX-XXXX-XXXX-0000')
self.assertEqual(customer.card_type, 'master')
class PinTransactionTests(TestCase):
def setUp(self):
super(PinTransactionTests, self).setUp()
self.transaction = PinTransaction()
self.transaction.card_token = '12345'
self.transaction.ip_address = '127.0.0.1'
self.transaction.amount = 500
self.transaction.currency = 'AUD'
self.transaction.email_address = 'test@example.com'
self.transaction.environment = 'test'
@override_settings(PIN_DEFAULT_ENVIRONMENT=None)
def test_save_defaults(self):
del settings.PIN_DEFAULT_ENVIRONMENT
self.transaction.environment = None
self.transaction.save()
self.assertEqual(self.transaction.environment, 'test')
self.assertTrue(self.transaction.date)
def test_save_notokens(self):
self.transaction.card_token = None
self.transaction.customer_token = None
self.assertRaises(PinError, self.transaction.save)
def test_valid_environment(self):
self.transaction.environment = 'this should not exist'
self.assertRaises(PinError, self.transaction.save)
class ProcessTransactionsTests(TestCase):
def setUp(self):
super(ProcessTransactionsTests, self).setUp()
self.transaction = PinTransaction()
self.transaction.card_token = '12345'
self.transaction.ip_address = '127.0.0.1'
self.transaction.amount = 500
self.transaction.currency = 'AUD'
self.transaction.email_address = 'test@example.com'
self.transaction.environment = 'test'
self.transaction.save()
self.response_data = json.dumps({
'response': {
'token': '12345',
'success': True,
'amount': 500,
'total_fees': 500,
'currency': 'AUD',
'description': 'test charge',
'email': 'test@example.com',
'ip_address': '127.0.0.1',
'created_at': '2012-06-20T03:10:49Z',
'status_message': 'Success!',
'error_message': None,
'card': {
'token': 'card_nytGw7koRg23EEp9NTmz9w',
'display_number': 'XXXX-XXXX-XXXX-0000',
'scheme': 'master',
'expiry_month': 6,
'expiry_year': 2017,
'name': 'Roland Robot',
'address_line1': '42 Sevenoaks St',
'address_line2': None,
'address_city': 'Lathlain',
'address_postcode': '6454',
'address_state': 'WA',
'address_country': 'Australia',
'primary': None,
},
'transfer': None
}
})
self.response_error = json.dumps({
'error': 'invalid_resource',
'error_description':
'One or more parameters were missing or invalid.',
'charge_token': '1234',
'messages': [{
'code': 'description_invalid',
'message': 'Description can\'t be blank',
'param': 'description'
}]
})
self.response_error_no_messages = json.dumps({
'error': 'invalid_resource',
'error_description':
'One or more parameters were missing or invalid.',
# Should there really be a charge token?
'charge_token': '1234'
})
@patch('requests.post')
def test_only_process_once(self, mock_request):
mock_request.return_value = FakeResponse(200, self.response_data)
# Shouldn't be marked as processed before process_transaction is called
self.assertFalse(self.transaction.processed)
result = self.transaction.process_transaction()
self.assertTrue(self.transaction.processed)
result = self.transaction.process_transaction()
self.assertIsNone(result)
@override_settings(PIN_ENVIRONMENTS={})
@patch('requests.post')
def test_valid_environment(self, mock_request):
mock_request.return_value = FakeResponse(200, self.response_data)
self.assertRaises(PinError, self.transaction.process_transaction)
@override_settings(PIN_ENVIRONMENTS=ENV_MISSING_SECRET)
@patch('requests.post')
def test_secret_set(self, mock_request):
mock_request.return_value = FakeResponse(200, self.response_data)
self.assertRaises(ConfigError, self.transaction.process_transaction)
@override_settings(PIN_ENVIRONMENTS=ENV_MISSING_HOST)
@patch('requests.post')
def test_host_set(self, mock_request):
mock_request.return_value = FakeResponse(200, self.response_data)
self.assertRaises(ConfigError, self.transaction.process_transaction)
@patch('requests.post')
def test_response_not_json(self, mock_request):
mock_request.return_value = FakeResponse(200, '')
response = self.transaction.process_transaction()
self.assertEqual(response, 'Failure.')
@patch('requests.post')
def test_response_badparam(self, mock_request):
mock_request.return_value = FakeResponse(200, self.response_error)
response = self.transaction.process_transaction()
self.assertEqual(response, 'Failure: Description can\'t be blank')
@patch('requests.post')
def test_response_noparam(self, mock_request):
mock_request.return_value = FakeResponse(
200, self.response_error_no_messages
)
response = self.transaction.process_transaction()
self.assertEqual(
response,
'Failure: One or more parameters were missing or invalid.'
)
@patch('requests.post')
def test_response_success(self, mock_request):
mock_request.return_value = FakeResponse(200, self.response_data)
response = self.transaction.process_transaction()
self.assertEqual(response, 'Success!')
self.assertTrue(self.transaction.succeeded)
self.assertEqual(self.transaction.transaction_token, '12345')
self.assertEqual(self.transaction.fees, 5.0)
self.assertEqual(self.transaction.pin_response, 'Success!')
self.assertEqual(self.transaction.card_address1, '42 Sevenoaks St')
self.assertIsNone(self.transaction.card_address2)
self.assertEqual(self.transaction.card_city, 'Lathlain')
self.assertEqual(self.transaction.card_state, 'WA')
self.assertEqual(self.transaction.card_postcode, '6454')
self.assertEqual(self.transaction.card_country, 'Australia')
self.assertEqual(self.transaction.card_number, 'XXXX-XXXX-XXXX-0000')
self.assertEqual(self.transaction.card_type, 'master')
| true | true |
f72576c2d1acd4d4c339871d110e259e22d8e73b | 13,114 | py | Python | sdk/python/pulumi_azure_native/network/v20200501/express_route_circuit_authorization.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_azure_native/network/v20200501/express_route_circuit_authorization.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | null | null | null | sdk/python/pulumi_azure_native/network/v20200501/express_route_circuit_authorization.py | pulumi-bot/pulumi-azure-native | f7b9490b5211544318e455e5cceafe47b628e12c | [
"Apache-2.0"
] | null | null | null | # coding=utf-8
# *** WARNING: this file was generated by the Pulumi SDK Generator. ***
# *** Do not edit by hand unless you're certain you know what you are doing! ***
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
from ._enums import *
__all__ = ['ExpressRouteCircuitAuthorization']
class ExpressRouteCircuitAuthorization(pulumi.CustomResource):
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
authorization_key: Optional[pulumi.Input[str]] = None,
authorization_name: Optional[pulumi.Input[str]] = None,
authorization_use_status: Optional[pulumi.Input[Union[str, 'AuthorizationUseStatus']]] = None,
circuit_name: Optional[pulumi.Input[str]] = None,
id: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
__props__=None,
__name__=None,
__opts__=None):
"""
Authorization in an ExpressRouteCircuit resource.
:param str resource_name: The name of the resource.
:param pulumi.ResourceOptions opts: Options for the resource.
:param pulumi.Input[str] authorization_key: The authorization key.
:param pulumi.Input[str] authorization_name: The name of the authorization.
:param pulumi.Input[Union[str, 'AuthorizationUseStatus']] authorization_use_status: The authorization use status.
:param pulumi.Input[str] circuit_name: The name of the express route circuit.
:param pulumi.Input[str] id: Resource ID.
:param pulumi.Input[str] name: The name of the resource that is unique within a resource group. This name can be used to access the resource.
:param pulumi.Input[str] resource_group_name: The name of the resource group.
"""
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['authorization_key'] = authorization_key
__props__['authorization_name'] = authorization_name
__props__['authorization_use_status'] = authorization_use_status
if circuit_name is None and not opts.urn:
raise TypeError("Missing required property 'circuit_name'")
__props__['circuit_name'] = circuit_name
__props__['id'] = id
__props__['name'] = name
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__['resource_group_name'] = resource_group_name
__props__['etag'] = None
__props__['provisioning_state'] = None
__props__['type'] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:network/v20200501:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/latest:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/latest:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20150501preview:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20150501preview:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20150615:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20150615:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20160330:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20160330:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20160601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20160601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20160901:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20160901:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20161201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20161201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20170301:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20170301:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20170601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20170601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20170801:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20170801:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20170901:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20170901:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20171001:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20171001:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20171101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20171101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20180101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20180101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20180201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20180201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20180401:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20180401:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20180601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20180601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20180701:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20180701:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20180801:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20180801:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20181001:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20181001:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20181101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20181101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20181201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20181201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20190201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20190201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20190401:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20190401:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20190601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20190601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20190701:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20190701:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20190801:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20190801:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20190901:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20190901:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20191101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20191101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20191201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20191201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20200301:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20200301:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20200401:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20200401:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20200601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20200601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20200701:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20200701:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20200801:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20200801:ExpressRouteCircuitAuthorization")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(ExpressRouteCircuitAuthorization, __self__).__init__(
'azure-native:network/v20200501:ExpressRouteCircuitAuthorization',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'ExpressRouteCircuitAuthorization':
"""
Get an existing ExpressRouteCircuitAuthorization resource's state with the given name, id, and optional extra
properties used to qualify the lookup.
:param str resource_name: The unique name of the resulting resource.
:param pulumi.Input[str] id: The unique provider ID of the resource to lookup.
:param pulumi.ResourceOptions opts: Options for the resource.
"""
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["authorization_key"] = None
__props__["authorization_use_status"] = None
__props__["etag"] = None
__props__["name"] = None
__props__["provisioning_state"] = None
__props__["type"] = None
return ExpressRouteCircuitAuthorization(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="authorizationKey")
def authorization_key(self) -> pulumi.Output[Optional[str]]:
"""
The authorization key.
"""
return pulumi.get(self, "authorization_key")
@property
@pulumi.getter(name="authorizationUseStatus")
def authorization_use_status(self) -> pulumi.Output[Optional[str]]:
"""
The authorization use status.
"""
return pulumi.get(self, "authorization_use_status")
@property
@pulumi.getter
def etag(self) -> pulumi.Output[str]:
"""
A unique read-only string that changes whenever the resource is updated.
"""
return pulumi.get(self, "etag")
@property
@pulumi.getter
def name(self) -> pulumi.Output[Optional[str]]:
"""
The name of the resource that is unique within a resource group. This name can be used to access the resource.
"""
return pulumi.get(self, "name")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> pulumi.Output[str]:
"""
The provisioning state of the authorization resource.
"""
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
"""
Type of the resource.
"""
return pulumi.get(self, "type")
def translate_output_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
| 82.477987 | 6,429 | 0.753317 |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
from ._enums import *
__all__ = ['ExpressRouteCircuitAuthorization']
class ExpressRouteCircuitAuthorization(pulumi.CustomResource):
def __init__(__self__,
resource_name: str,
opts: Optional[pulumi.ResourceOptions] = None,
authorization_key: Optional[pulumi.Input[str]] = None,
authorization_name: Optional[pulumi.Input[str]] = None,
authorization_use_status: Optional[pulumi.Input[Union[str, 'AuthorizationUseStatus']]] = None,
circuit_name: Optional[pulumi.Input[str]] = None,
id: Optional[pulumi.Input[str]] = None,
name: Optional[pulumi.Input[str]] = None,
resource_group_name: Optional[pulumi.Input[str]] = None,
__props__=None,
__name__=None,
__opts__=None):
if __name__ is not None:
warnings.warn("explicit use of __name__ is deprecated", DeprecationWarning)
resource_name = __name__
if __opts__ is not None:
warnings.warn("explicit use of __opts__ is deprecated, use 'opts' instead", DeprecationWarning)
opts = __opts__
if opts is None:
opts = pulumi.ResourceOptions()
if not isinstance(opts, pulumi.ResourceOptions):
raise TypeError('Expected resource options to be a ResourceOptions instance')
if opts.version is None:
opts.version = _utilities.get_version()
if opts.id is None:
if __props__ is not None:
raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource')
__props__ = dict()
__props__['authorization_key'] = authorization_key
__props__['authorization_name'] = authorization_name
__props__['authorization_use_status'] = authorization_use_status
if circuit_name is None and not opts.urn:
raise TypeError("Missing required property 'circuit_name'")
__props__['circuit_name'] = circuit_name
__props__['id'] = id
__props__['name'] = name
if resource_group_name is None and not opts.urn:
raise TypeError("Missing required property 'resource_group_name'")
__props__['resource_group_name'] = resource_group_name
__props__['etag'] = None
__props__['provisioning_state'] = None
__props__['type'] = None
alias_opts = pulumi.ResourceOptions(aliases=[pulumi.Alias(type_="azure-nextgen:network/v20200501:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/latest:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/latest:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20150501preview:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20150501preview:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20150615:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20150615:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20160330:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20160330:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20160601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20160601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20160901:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20160901:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20161201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20161201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20170301:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20170301:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20170601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20170601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20170801:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20170801:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20170901:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20170901:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20171001:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20171001:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20171101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20171101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20180101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20180101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20180201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20180201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20180401:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20180401:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20180601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20180601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20180701:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20180701:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20180801:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20180801:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20181001:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20181001:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20181101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20181101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20181201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20181201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20190201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20190201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20190401:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20190401:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20190601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20190601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20190701:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20190701:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20190801:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20190801:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20190901:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20190901:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20191101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20191101:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20191201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20191201:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20200301:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20200301:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20200401:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20200401:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20200601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20200601:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20200701:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20200701:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-native:network/v20200801:ExpressRouteCircuitAuthorization"), pulumi.Alias(type_="azure-nextgen:network/v20200801:ExpressRouteCircuitAuthorization")])
opts = pulumi.ResourceOptions.merge(opts, alias_opts)
super(ExpressRouteCircuitAuthorization, __self__).__init__(
'azure-native:network/v20200501:ExpressRouteCircuitAuthorization',
resource_name,
__props__,
opts)
@staticmethod
def get(resource_name: str,
id: pulumi.Input[str],
opts: Optional[pulumi.ResourceOptions] = None) -> 'ExpressRouteCircuitAuthorization':
opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id))
__props__ = dict()
__props__["authorization_key"] = None
__props__["authorization_use_status"] = None
__props__["etag"] = None
__props__["name"] = None
__props__["provisioning_state"] = None
__props__["type"] = None
return ExpressRouteCircuitAuthorization(resource_name, opts=opts, __props__=__props__)
@property
@pulumi.getter(name="authorizationKey")
def authorization_key(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "authorization_key")
@property
@pulumi.getter(name="authorizationUseStatus")
def authorization_use_status(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "authorization_use_status")
@property
@pulumi.getter
def etag(self) -> pulumi.Output[str]:
return pulumi.get(self, "etag")
@property
@pulumi.getter
def name(self) -> pulumi.Output[Optional[str]]:
return pulumi.get(self, "name")
@property
@pulumi.getter(name="provisioningState")
def provisioning_state(self) -> pulumi.Output[str]:
return pulumi.get(self, "provisioning_state")
@property
@pulumi.getter
def type(self) -> pulumi.Output[str]:
return pulumi.get(self, "type")
def translate_output_property(self, prop):
return _tables.CAMEL_TO_SNAKE_CASE_TABLE.get(prop) or prop
def translate_input_property(self, prop):
return _tables.SNAKE_TO_CAMEL_CASE_TABLE.get(prop) or prop
| true | true |
f725772c43f53c9873559775e2e56aabd5ec8fda | 13,185 | py | Python | aps/loader/simu.py | LvHang/aps | 3e9c8b247e0526481970c28e8af1a6a93cc7f2cc | [
"Apache-2.0"
] | 5 | 2021-07-05T12:21:44.000Z | 2021-11-23T08:09:45.000Z | aps/loader/simu.py | LvHang/aps | 3e9c8b247e0526481970c28e8af1a6a93cc7f2cc | [
"Apache-2.0"
] | null | null | null | aps/loader/simu.py | LvHang/aps | 3e9c8b247e0526481970c28e8af1a6a93cc7f2cc | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# Copyright 2020 Jian Wu
# License: Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
"""
Adopt from my another project: https://github.com/funcwj/setk
See https://github.com/funcwj/setk/tree/master/doc/data_simu for command line usage
"""
import argparse
import numpy as np
from aps.loader.audio import read_audio, add_room_response
from aps.opts import StrToBoolAction
from aps.const import EPSILON
def coeff_snr(sig_pow, ref_pow, snr):
"""
For
mix = Sa + alpha*Sb
Given
SNR = 10*log10[Pa/(Pb * alpha^2)]
we got
alpha = Pa/[Pb*10^(SNR/10)]^0.5
"""
return (ref_pow / (sig_pow * 10**(snr / 10) + EPSILON))**0.5
def add_speaker(mix_nsamps,
src_spk,
src_begin,
sdr,
src_rir=None,
channel=-1,
sr=16000):
"""
Mix source speakers
"""
spk_image, spk_power = [], []
for i, spk in enumerate(src_spk):
if src_rir is None:
src = spk[None, ...] if spk.ndim == 1 else spk
spk_image.append(src)
spk_power.append(np.mean(src[0]**2))
else:
rir = src_rir[i]
if rir.ndim == 1:
rir = rir[None, ...]
if channel >= 0:
if rir.ndim == 2:
rir = rir[channel:channel + 1]
revb, p = add_room_response(spk, rir, sr=sr)
spk_image.append(revb)
spk_power.append(p)
# make mix
N, _ = spk_image[0].shape
mix = [np.zeros([N, mix_nsamps], dtype=np.float32) for _ in src_spk]
# start mixing
ref_power = spk_power[0]
for i, image in enumerate(spk_image):
dur = image.shape[-1]
beg = src_begin[i]
coeff = 1 if i == 0 else coeff_snr(spk_power[i], ref_power, sdr[i])
mix[i][..., beg:beg + dur] += coeff * image
return mix
def add_point_noise(mix_nsamps,
ref_power,
noise,
noise_begin,
snr,
noise_rir=None,
channel=-1,
repeat=False,
sr=16000):
"""
Add pointsource noises
"""
image = []
image_power = []
for i, noise in enumerate(noise):
beg = noise_begin[i]
if not repeat:
dur = min(noise.shape[-1], mix_nsamps - beg)
else:
dur = mix_nsamps - beg
# if short, then padding
if noise.shape[-1] < dur:
noise = np.pad(noise, (0, dur - noise.shape[-1]), mode="wrap")
if noise_rir is None:
src = noise[None, ...] if noise.ndim == 1 else noise
image.append(src)
image_power.append(np.mean(src[0, :dur]**2) if dur > 0 else 0)
else:
rir = noise_rir[i]
if rir.ndim == 1:
rir = rir[None, ...]
if channel >= 0:
if rir.ndim == 2:
rir = rir[channel:channel + 1]
revb, revb_power = add_room_response(noise[:dur], rir, sr=sr)
image.append(revb)
image_power.append(revb_power)
# make noise mix
N, _ = image[0].shape
mix = np.zeros([N, mix_nsamps], dtype=np.float32)
# start mixing
for i, img in enumerate(image):
beg = noise_begin[i]
coeff = coeff_snr(image_power[i], ref_power, snr[i])
mix[..., beg:beg + dur] += coeff * img[..., :dur]
return mix
def load_audio(src_args, beg=None, end=None, sr=16000):
"""
Load audio from args.xxx
"""
if src_args:
src_path = src_args.split(",")
beg_int = [None for _ in src_path]
end_int = [None for _ in src_path]
if beg:
beg_int = [int(v) for v in beg.split(",")]
if end:
end_int = [int(v) for v in end.split(",")]
return [
read_audio(s, sr=sr, beg=b, end=e)
for s, b, e in zip(src_path, beg_int, end_int)
]
else:
return None
def run_simu(args):
def arg_float(src_args):
return [float(s) for s in src_args.split(",")] if src_args else None
src_spk = load_audio(args.src_spk, sr=args.sr)
src_rir = load_audio(args.src_rir, sr=args.sr)
if src_rir:
if len(src_rir) != len(src_spk):
raise RuntimeError(
f"Number of --src-rir={args.src_rir} do not match with " +
f"--src-spk={args.src_spk} option")
sdr = arg_float(args.src_sdr)
if len(src_spk) > 1 and not sdr:
raise RuntimeError("--src-sdr need to be assigned for " +
f"--src-spk={args.src_spk}")
if sdr:
if len(src_spk) - 1 != len(sdr):
raise RuntimeError("Number of --src-snr - 1 do not match with " +
"--src-snr option")
sdr = [0] + sdr
src_begin = arg_float(args.src_begin)
if src_begin:
src_begin = [int(v) for v in src_begin]
else:
src_begin = [0 for _ in src_spk]
# number samples of the mixture
mix_nsamps = max([b + s.size for b, s in zip(src_begin, src_spk)])
point_noise_rir = load_audio(args.point_noise_rir, sr=args.sr)
point_noise_end = [
str(int(v) + mix_nsamps) for v in args.point_noise_offset.split()
]
point_noise = load_audio(args.point_noise,
beg=args.point_noise_offset,
end=",".join(point_noise_end),
sr=args.sr)
if args.point_noise:
if point_noise_rir:
if len(point_noise) != len(point_noise_rir):
raise RuntimeError(
f"Number of --point-noise-rir={args.point_noise_rir} do not match with "
+ f"--point-noise={args.point_noise} option")
point_snr = arg_float(args.point_noise_snr)
if not point_snr:
raise RuntimeError("--point-noise-snr need to be assigned for " +
f"--point-noise={args.point_noise}")
if len(point_noise) != len(point_snr):
raise RuntimeError(
f"Number of --point-noise-snr={args.point_noise_snr} do not match with "
+ f"--point-noise={args.point_noise} option")
point_begin = arg_float(args.point_noise_begin)
if point_begin:
point_begin = [int(v) for v in point_begin]
else:
point_begin = [0 for _ in point_noise]
isotropic_noise = load_audio(args.isotropic_noise,
beg=str(args.isotropic_noise_offset),
end=str(args.isotropic_noise_offset +
mix_nsamps),
sr=args.sr)
if isotropic_noise:
isotropic_noise = isotropic_noise[0]
isotropic_snr = arg_float(args.isotropic_noise_snr)
if not isotropic_snr:
raise RuntimeError(
"--isotropic-snr need to be assigned for " +
f"--isotropic-noise={args.isotropic_noise} option")
isotropic_snr = isotropic_snr[0]
else:
isotropic_snr = None
# add speakers
spk = add_speaker(mix_nsamps,
src_spk,
src_begin,
sdr,
src_rir=src_rir,
channel=args.dump_channel,
sr=args.sr)
spk_utt = sum(spk)
mix = spk_utt.copy()
spk_power = np.mean(spk_utt[0]**2)
if point_noise:
noise = add_point_noise(mix_nsamps,
spk_power,
point_noise,
point_begin,
point_snr,
noise_rir=point_noise_rir,
channel=args.dump_channel,
repeat=args.point_noise_repeat,
sr=args.sr)
num_channels = spk_utt.shape[0]
if num_channels != noise.shape[0]:
if num_channels == 1:
noise = noise[0:1]
else:
raise RuntimeError("Channel mismatch between source speaker " +
"configuration and pointsource noise's, " +
f"{num_channels} vs {noise.shape[0]}")
mix = spk_utt + noise
else:
noise = None
ch = args.dump_channel
if isotropic_noise is not None:
N, _ = spk_utt.shape
if N == 1:
if isotropic_noise.ndim == 1:
isotropic_noise = isotropic_noise[None, ...]
else:
if ch >= 0:
isotropic_noise = isotropic_noise[ch:ch + 1]
else:
raise RuntimeError(
"Single channel mixture vs multi-channel "
"isotropic noise")
else:
if isotropic_noise.shape[0] != N:
raise RuntimeError(
"Channel number mismatch between mixture and isotropic noise, "
+ f"{N} vs {isotropic_noise.shape[0]}")
dur = min(mix_nsamps, isotropic_noise.shape[-1])
isotropic_chunk = isotropic_noise[0, :dur]
power = np.mean(isotropic_chunk**2)
coeff = coeff_snr(power, spk_power, isotropic_snr)
mix[..., :dur] += coeff * isotropic_chunk
if noise is None:
noise = coeff * isotropic_chunk
else:
noise[..., :dur] += coeff * isotropic_chunk
factor = args.norm_factor / (np.max(np.abs(mix)) + EPSILON)
mix = mix.squeeze() * factor
spk = [s[0] * factor for s in spk]
if noise is None:
return mix, spk, None
else:
return mix, spk, noise[0] * factor
def make_argparse():
parser = argparse.ArgumentParser(
description="Command to do audio data simulation",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--src-spk",
type=str,
required=True,
help="Source speakers, e.g., spk1.wav,spk2.wav")
parser.add_argument("--src-rir",
type=str,
default="",
help="RIRs for each source speakers")
parser.add_argument("--src-sdr",
type=str,
default="",
help="SDR for each speakers (if needed)")
parser.add_argument("--src-begin",
type=str,
default="",
help="Begining samples on the mixture utterances")
parser.add_argument("--point-noise",
type=str,
default="",
help="Add pointsource noises")
parser.add_argument("--point-noise-rir",
type=str,
default="",
help="RIRs of the pointsource noises (if needed)")
parser.add_argument("--point-noise-snr",
type=str,
default="",
help="SNR of the pointsource noises")
parser.add_argument("--point-noise-begin",
type=str,
default="",
help="Begining samples of the "
"pointsource noises on the mixture "
"utterances (if needed)")
parser.add_argument("--point-noise-offset",
type=str,
default="",
help="Add from the offset position "
"of the pointsource noise")
parser.add_argument("--point-noise-repeat",
action=StrToBoolAction,
default=False,
help="Repeat the pointsource noise or not")
parser.add_argument("--isotropic-noise",
type=str,
default="",
help="Add isotropic noises")
parser.add_argument("--isotropic-noise-snr",
type=str,
default="",
help="SNR of the isotropic noises")
parser.add_argument("--isotropic-noise-offset",
type=int,
default=0,
help="Add noise from the offset position "
"of the isotropic noise")
parser.add_argument("--dump-channel",
type=int,
default=-1,
help="Index of the channel to dump out (-1 means all)")
parser.add_argument('--norm-factor',
type=float,
default=0.9,
help="Normalization factor of the final output")
parser.add_argument("--sr",
type=int,
default=16000,
help="Value of the sample rate")
return parser
| 36.222527 | 92 | 0.498597 |
import argparse
import numpy as np
from aps.loader.audio import read_audio, add_room_response
from aps.opts import StrToBoolAction
from aps.const import EPSILON
def coeff_snr(sig_pow, ref_pow, snr):
return (ref_pow / (sig_pow * 10**(snr / 10) + EPSILON))**0.5
def add_speaker(mix_nsamps,
src_spk,
src_begin,
sdr,
src_rir=None,
channel=-1,
sr=16000):
spk_image, spk_power = [], []
for i, spk in enumerate(src_spk):
if src_rir is None:
src = spk[None, ...] if spk.ndim == 1 else spk
spk_image.append(src)
spk_power.append(np.mean(src[0]**2))
else:
rir = src_rir[i]
if rir.ndim == 1:
rir = rir[None, ...]
if channel >= 0:
if rir.ndim == 2:
rir = rir[channel:channel + 1]
revb, p = add_room_response(spk, rir, sr=sr)
spk_image.append(revb)
spk_power.append(p)
N, _ = spk_image[0].shape
mix = [np.zeros([N, mix_nsamps], dtype=np.float32) for _ in src_spk]
ref_power = spk_power[0]
for i, image in enumerate(spk_image):
dur = image.shape[-1]
beg = src_begin[i]
coeff = 1 if i == 0 else coeff_snr(spk_power[i], ref_power, sdr[i])
mix[i][..., beg:beg + dur] += coeff * image
return mix
def add_point_noise(mix_nsamps,
ref_power,
noise,
noise_begin,
snr,
noise_rir=None,
channel=-1,
repeat=False,
sr=16000):
image = []
image_power = []
for i, noise in enumerate(noise):
beg = noise_begin[i]
if not repeat:
dur = min(noise.shape[-1], mix_nsamps - beg)
else:
dur = mix_nsamps - beg
if noise.shape[-1] < dur:
noise = np.pad(noise, (0, dur - noise.shape[-1]), mode="wrap")
if noise_rir is None:
src = noise[None, ...] if noise.ndim == 1 else noise
image.append(src)
image_power.append(np.mean(src[0, :dur]**2) if dur > 0 else 0)
else:
rir = noise_rir[i]
if rir.ndim == 1:
rir = rir[None, ...]
if channel >= 0:
if rir.ndim == 2:
rir = rir[channel:channel + 1]
revb, revb_power = add_room_response(noise[:dur], rir, sr=sr)
image.append(revb)
image_power.append(revb_power)
N, _ = image[0].shape
mix = np.zeros([N, mix_nsamps], dtype=np.float32)
for i, img in enumerate(image):
beg = noise_begin[i]
coeff = coeff_snr(image_power[i], ref_power, snr[i])
mix[..., beg:beg + dur] += coeff * img[..., :dur]
return mix
def load_audio(src_args, beg=None, end=None, sr=16000):
if src_args:
src_path = src_args.split(",")
beg_int = [None for _ in src_path]
end_int = [None for _ in src_path]
if beg:
beg_int = [int(v) for v in beg.split(",")]
if end:
end_int = [int(v) for v in end.split(",")]
return [
read_audio(s, sr=sr, beg=b, end=e)
for s, b, e in zip(src_path, beg_int, end_int)
]
else:
return None
def run_simu(args):
def arg_float(src_args):
return [float(s) for s in src_args.split(",")] if src_args else None
src_spk = load_audio(args.src_spk, sr=args.sr)
src_rir = load_audio(args.src_rir, sr=args.sr)
if src_rir:
if len(src_rir) != len(src_spk):
raise RuntimeError(
f"Number of --src-rir={args.src_rir} do not match with " +
f"--src-spk={args.src_spk} option")
sdr = arg_float(args.src_sdr)
if len(src_spk) > 1 and not sdr:
raise RuntimeError("--src-sdr need to be assigned for " +
f"--src-spk={args.src_spk}")
if sdr:
if len(src_spk) - 1 != len(sdr):
raise RuntimeError("Number of --src-snr - 1 do not match with " +
"--src-snr option")
sdr = [0] + sdr
src_begin = arg_float(args.src_begin)
if src_begin:
src_begin = [int(v) for v in src_begin]
else:
src_begin = [0 for _ in src_spk]
mix_nsamps = max([b + s.size for b, s in zip(src_begin, src_spk)])
point_noise_rir = load_audio(args.point_noise_rir, sr=args.sr)
point_noise_end = [
str(int(v) + mix_nsamps) for v in args.point_noise_offset.split()
]
point_noise = load_audio(args.point_noise,
beg=args.point_noise_offset,
end=",".join(point_noise_end),
sr=args.sr)
if args.point_noise:
if point_noise_rir:
if len(point_noise) != len(point_noise_rir):
raise RuntimeError(
f"Number of --point-noise-rir={args.point_noise_rir} do not match with "
+ f"--point-noise={args.point_noise} option")
point_snr = arg_float(args.point_noise_snr)
if not point_snr:
raise RuntimeError("--point-noise-snr need to be assigned for " +
f"--point-noise={args.point_noise}")
if len(point_noise) != len(point_snr):
raise RuntimeError(
f"Number of --point-noise-snr={args.point_noise_snr} do not match with "
+ f"--point-noise={args.point_noise} option")
point_begin = arg_float(args.point_noise_begin)
if point_begin:
point_begin = [int(v) for v in point_begin]
else:
point_begin = [0 for _ in point_noise]
isotropic_noise = load_audio(args.isotropic_noise,
beg=str(args.isotropic_noise_offset),
end=str(args.isotropic_noise_offset +
mix_nsamps),
sr=args.sr)
if isotropic_noise:
isotropic_noise = isotropic_noise[0]
isotropic_snr = arg_float(args.isotropic_noise_snr)
if not isotropic_snr:
raise RuntimeError(
"--isotropic-snr need to be assigned for " +
f"--isotropic-noise={args.isotropic_noise} option")
isotropic_snr = isotropic_snr[0]
else:
isotropic_snr = None
spk = add_speaker(mix_nsamps,
src_spk,
src_begin,
sdr,
src_rir=src_rir,
channel=args.dump_channel,
sr=args.sr)
spk_utt = sum(spk)
mix = spk_utt.copy()
spk_power = np.mean(spk_utt[0]**2)
if point_noise:
noise = add_point_noise(mix_nsamps,
spk_power,
point_noise,
point_begin,
point_snr,
noise_rir=point_noise_rir,
channel=args.dump_channel,
repeat=args.point_noise_repeat,
sr=args.sr)
num_channels = spk_utt.shape[0]
if num_channels != noise.shape[0]:
if num_channels == 1:
noise = noise[0:1]
else:
raise RuntimeError("Channel mismatch between source speaker " +
"configuration and pointsource noise's, " +
f"{num_channels} vs {noise.shape[0]}")
mix = spk_utt + noise
else:
noise = None
ch = args.dump_channel
if isotropic_noise is not None:
N, _ = spk_utt.shape
if N == 1:
if isotropic_noise.ndim == 1:
isotropic_noise = isotropic_noise[None, ...]
else:
if ch >= 0:
isotropic_noise = isotropic_noise[ch:ch + 1]
else:
raise RuntimeError(
"Single channel mixture vs multi-channel "
"isotropic noise")
else:
if isotropic_noise.shape[0] != N:
raise RuntimeError(
"Channel number mismatch between mixture and isotropic noise, "
+ f"{N} vs {isotropic_noise.shape[0]}")
dur = min(mix_nsamps, isotropic_noise.shape[-1])
isotropic_chunk = isotropic_noise[0, :dur]
power = np.mean(isotropic_chunk**2)
coeff = coeff_snr(power, spk_power, isotropic_snr)
mix[..., :dur] += coeff * isotropic_chunk
if noise is None:
noise = coeff * isotropic_chunk
else:
noise[..., :dur] += coeff * isotropic_chunk
factor = args.norm_factor / (np.max(np.abs(mix)) + EPSILON)
mix = mix.squeeze() * factor
spk = [s[0] * factor for s in spk]
if noise is None:
return mix, spk, None
else:
return mix, spk, noise[0] * factor
def make_argparse():
parser = argparse.ArgumentParser(
description="Command to do audio data simulation",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--src-spk",
type=str,
required=True,
help="Source speakers, e.g., spk1.wav,spk2.wav")
parser.add_argument("--src-rir",
type=str,
default="",
help="RIRs for each source speakers")
parser.add_argument("--src-sdr",
type=str,
default="",
help="SDR for each speakers (if needed)")
parser.add_argument("--src-begin",
type=str,
default="",
help="Begining samples on the mixture utterances")
parser.add_argument("--point-noise",
type=str,
default="",
help="Add pointsource noises")
parser.add_argument("--point-noise-rir",
type=str,
default="",
help="RIRs of the pointsource noises (if needed)")
parser.add_argument("--point-noise-snr",
type=str,
default="",
help="SNR of the pointsource noises")
parser.add_argument("--point-noise-begin",
type=str,
default="",
help="Begining samples of the "
"pointsource noises on the mixture "
"utterances (if needed)")
parser.add_argument("--point-noise-offset",
type=str,
default="",
help="Add from the offset position "
"of the pointsource noise")
parser.add_argument("--point-noise-repeat",
action=StrToBoolAction,
default=False,
help="Repeat the pointsource noise or not")
parser.add_argument("--isotropic-noise",
type=str,
default="",
help="Add isotropic noises")
parser.add_argument("--isotropic-noise-snr",
type=str,
default="",
help="SNR of the isotropic noises")
parser.add_argument("--isotropic-noise-offset",
type=int,
default=0,
help="Add noise from the offset position "
"of the isotropic noise")
parser.add_argument("--dump-channel",
type=int,
default=-1,
help="Index of the channel to dump out (-1 means all)")
parser.add_argument('--norm-factor',
type=float,
default=0.9,
help="Normalization factor of the final output")
parser.add_argument("--sr",
type=int,
default=16000,
help="Value of the sample rate")
return parser
| true | true |
f7257753ce0834da08cef9848377fb031be44fc6 | 2,553 | py | Python | tests/test_parametric_printer_coverage.py | ka3bhy/wexpect | 14a4279579a740ce15743db44228f3b0cf4ee8f4 | [
"MIT"
] | 52 | 2019-04-24T14:38:43.000Z | 2022-03-08T22:03:11.000Z | tests/test_parametric_printer_coverage.py | ka3bhy/wexpect | 14a4279579a740ce15743db44228f3b0cf4ee8f4 | [
"MIT"
] | 51 | 2019-05-13T12:15:09.000Z | 2021-12-15T14:00:15.000Z | tests/test_parametric_printer_coverage.py | ka3bhy/wexpect | 14a4279579a740ce15743db44228f3b0cf4ee8f4 | [
"MIT"
] | 20 | 2019-07-15T15:48:31.000Z | 2022-03-27T08:55:17.000Z | import wexpect
import unittest
import sys
import os
import time
from tests import PexpectTestCase
@unittest.skipIf(wexpect.spawn_class_name == 'legacy_wexpect', "legacy unsupported")
class TestCaseParametricPrinter(PexpectTestCase.PexpectTestCase):
def test_all_line_length (self):
here = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, here)
# With quotes (C:\Program Files\Python37\python.exe needs quotes)
python_executable = '"' + sys.executable + '" '
child_script = here + '\\parametric_printer.py'
self.prompt = '> '
# Start the child process
self.p = wexpect.spawn(python_executable + ' ' + child_script, coverage_console_reader=True)
# Wait for prompt
self.p.expect(self.prompt)
self._test(['a'], range(1,200), [1], [0])
self.p.terminate()
def test_long_console(self):
here = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, here)
# With quotes (C:\Program Files\Python37\python.exe needs quotes)
python_executable = '"' + sys.executable + '" '
child_script = here + '\\parametric_printer.py'
self.prompt = '> '
# Start the child process
self.p = wexpect.spawn(python_executable + ' ' + child_script, coverage_console_reader=True)
# Wait for prompt
self.p.expect(self.prompt)
self._test(['a', 'b', 'c', 'd', 'e', 'f'], [8, 16, 32, 64], [64, 128, 256], [-1, 0])
self.p.terminate()
def _test(self, character_list, character_count_list, line_count_list, speed_ms_list):
# print(f'character_list: {character_list} character_count_list: {character_count_list} line_count_list: {line_count_list} speed_ms_list: {speed_ms_list}')
for character in character_list:
for character_count in character_count_list:
for line_count in line_count_list:
for speed_ms in speed_ms_list:
command = f'{character},{character_count},{line_count},{speed_ms}'
self.p.sendline(command)
self.p.expect(self.prompt)
expected = [character*character_count] * line_count
try:
self.assertEqual(self.p.before.splitlines()[1:-1], expected)
except:
raise
if __name__ == '__main__':
unittest.main()
suite = unittest.makeSuite(TestCaseParametricPrinter,'test')
| 36.471429 | 166 | 0.613396 | import wexpect
import unittest
import sys
import os
import time
from tests import PexpectTestCase
@unittest.skipIf(wexpect.spawn_class_name == 'legacy_wexpect', "legacy unsupported")
class TestCaseParametricPrinter(PexpectTestCase.PexpectTestCase):
def test_all_line_length (self):
here = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, here)
python_executable = '"' + sys.executable + '" '
child_script = here + '\\parametric_printer.py'
self.prompt = '> '
self.p = wexpect.spawn(python_executable + ' ' + child_script, coverage_console_reader=True)
self.p.expect(self.prompt)
self._test(['a'], range(1,200), [1], [0])
self.p.terminate()
def test_long_console(self):
here = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, here)
python_executable = '"' + sys.executable + '" '
child_script = here + '\\parametric_printer.py'
self.prompt = '> '
self.p = wexpect.spawn(python_executable + ' ' + child_script, coverage_console_reader=True)
self.p.expect(self.prompt)
self._test(['a', 'b', 'c', 'd', 'e', 'f'], [8, 16, 32, 64], [64, 128, 256], [-1, 0])
self.p.terminate()
def _test(self, character_list, character_count_list, line_count_list, speed_ms_list):
for character in character_list:
for character_count in character_count_list:
for line_count in line_count_list:
for speed_ms in speed_ms_list:
command = f'{character},{character_count},{line_count},{speed_ms}'
self.p.sendline(command)
self.p.expect(self.prompt)
expected = [character*character_count] * line_count
try:
self.assertEqual(self.p.before.splitlines()[1:-1], expected)
except:
raise
if __name__ == '__main__':
unittest.main()
suite = unittest.makeSuite(TestCaseParametricPrinter,'test')
| true | true |
f725787c40eac809defe6a07fbbbbed9170067b9 | 3,731 | py | Python | contrib/macdeploy/custom_dsstore.py | BufferUnderwhelm/asspennies | 919be76d6d4be42fea02af1194df2875b91c85dc | [
"MIT"
] | null | null | null | contrib/macdeploy/custom_dsstore.py | BufferUnderwhelm/asspennies | 919be76d6d4be42fea02af1194df2875b91c85dc | [
"MIT"
] | null | null | null | contrib/macdeploy/custom_dsstore.py | BufferUnderwhelm/asspennies | 919be76d6d4be42fea02af1194df2875b91c85dc | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
# Copyright (c) 2013-2018 The Bitcoin Core developers
# Distributed under the MIT software license, see the accompanying
# file COPYING or http://www.opensource.org/licenses/mit-license.php.
import biplist
from ds_store import DSStore
from mac_alias import Alias
import sys
output_file = sys.argv[1]
package_name_ns = sys.argv[2]
ds = DSStore.open(output_file, 'w+')
ds['.']['bwsp'] = {
'ShowStatusBar': False,
'WindowBounds': '{{300, 280}, {500, 343}}',
'ContainerShowSidebar': False,
'SidebarWidth': 0,
'ShowTabView': False,
'PreviewPaneVisibility': False,
'ShowToolbar': False,
'ShowSidebar': False,
'ShowPathbar': True
}
icvp = {
'gridOffsetX': 0.0,
'textSize': 12.0,
'viewOptionsVersion': 1,
'backgroundImageAlias': b'\x00\x00\x00\x00\x02\x1e\x00\x02\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xd1\x94\\\xb0H+\x00\x05\x00\x00\x00\x98\x0fbackground.tiff\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x99\xd19\xb0\xf8\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\r\x02\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x0b.background\x00\x00\x10\x00\x08\x00\x00\xd1\x94\\\xb0\x00\x00\x00\x11\x00\x08\x00\x00\xd19\xb0\xf8\x00\x00\x00\x01\x00\x04\x00\x00\x00\x98\x00\x0e\x00 \x00\x0f\x00b\x00a\x00c\x00k\x00g\x00r\x00o\x00u\x00n\x00d\x00.\x00t\x00i\x00f\x00f\x00\x0f\x00\x02\x00\x00\x00\x12\x00\x1c/.background/background.tiff\x00\x14\x01\x06\x00\x00\x00\x00\x01\x06\x00\x02\x00\x00\x0cMacintosh HD\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xce\x97\xab\xc3H+\x00\x00\x01\x88[\x88\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x02u\xab\x8d\xd1\x94\\\xb0devrddsk\xff\xff\xff\xff\x00\x00\t \x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x07bitcoin\x00\x00\x10\x00\x08\x00\x00\xce\x97\xab\xc3\x00\x00\x00\x11\x00\x08\x00\x00\xd1\x94\\\xb0\x00\x00\x00\x01\x00\x14\x01\x88[\x88\x00\x16\xa9\t\x00\x08\xfaR\x00\x08\xfaQ\x00\x02d\x8e\x00\x0e\x00\x02\x00\x00\x00\x0f\x00\x1a\x00\x0c\x00M\x00a\x00c\x00i\x00n\x00t\x00o\x00s\x00h\x00 \x00H\x00D\x00\x13\x00\x01/\x00\x00\x15\x00\x02\x00\x14\xff\xff\x00\x00\xff\xff\x00\x00',
'backgroundColorBlue': 1.0,
'iconSize': 96.0,
'backgroundColorGreen': 1.0,
'arrangeBy': 'none',
'showIconPreview': True,
'gridSpacing': 100.0,
'gridOffsetY': 0.0,
'showItemInfo': False,
'labelOnBottom': True,
'backgroundType': 2,
'backgroundColorRed': 1.0
}
alias = Alias.from_bytes(icvp['backgroundImageAlias'])
alias.volume.name = package_name_ns
alias.volume.posix_path = '/Volumes/' + package_name_ns
alias.volume.disk_image_alias.target.filename = package_name_ns + '.temp.dmg'
alias.volume.disk_image_alias.target.carbon_path = 'Macintosh HD:Users:\x00asspenniesuser:\x00Documents:\x00asspennies:\x00asspennies:\x00' + package_name_ns + '.temp.dmg'
alias.volume.disk_image_alias.target.posix_path = 'Users/asspenniesuser/Documents/asspennies/asspennies/' + package_name_ns + '.temp.dmg'
alias.target.carbon_path = package_name_ns + ':.background:\x00background.tiff'
icvp['backgroundImageAlias'] = biplist.Data(alias.to_bytes())
ds['.']['icvp'] = icvp
ds['.']['vSrn'] = ('long', 1)
ds['Applications']['Iloc'] = (370, 156)
ds['AssPennies-Qt.app']['Iloc'] = (128, 156)
ds.flush()
ds.close()
| 62.183333 | 1,817 | 0.725811 |
import biplist
from ds_store import DSStore
from mac_alias import Alias
import sys
output_file = sys.argv[1]
package_name_ns = sys.argv[2]
ds = DSStore.open(output_file, 'w+')
ds['.']['bwsp'] = {
'ShowStatusBar': False,
'WindowBounds': '{{300, 280}, {500, 343}}',
'ContainerShowSidebar': False,
'SidebarWidth': 0,
'ShowTabView': False,
'PreviewPaneVisibility': False,
'ShowToolbar': False,
'ShowSidebar': False,
'ShowPathbar': True
}
icvp = {
'gridOffsetX': 0.0,
'textSize': 12.0,
'viewOptionsVersion': 1,
'backgroundImageAlias': b'\x00\x00\x00\x00\x02\x1e\x00\x02\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xd1\x94\\\xb0H+\x00\x05\x00\x00\x00\x98\x0fbackground.tiff\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x99\xd19\xb0\xf8\x00\x00\x00\x00\x00\x00\x00\x00\xff\xff\xff\xff\x00\x00\r\x02\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x0b.background\x00\x00\x10\x00\x08\x00\x00\xd1\x94\\\xb0\x00\x00\x00\x11\x00\x08\x00\x00\xd19\xb0\xf8\x00\x00\x00\x01\x00\x04\x00\x00\x00\x98\x00\x0e\x00 \x00\x0f\x00b\x00a\x00c\x00k\x00g\x00r\x00o\x00u\x00n\x00d\x00.\x00t\x00i\x00f\x00f\x00\x0f\x00\x02\x00\x00\x00\x12\x00\x1c/.background/background.tiff\x00\x14\x01\x06\x00\x00\x00\x00\x01\x06\x00\x02\x00\x00\x0cMacintosh HD\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\xce\x97\xab\xc3H+\x00\x00\x01\x88[\x88\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x02u\xab\x8d\xd1\x94\\\xb0devrddsk\xff\xff\xff\xff\x00\x00\t \x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x07bitcoin\x00\x00\x10\x00\x08\x00\x00\xce\x97\xab\xc3\x00\x00\x00\x11\x00\x08\x00\x00\xd1\x94\\\xb0\x00\x00\x00\x01\x00\x14\x01\x88[\x88\x00\x16\xa9\t\x00\x08\xfaR\x00\x08\xfaQ\x00\x02d\x8e\x00\x0e\x00\x02\x00\x00\x00\x0f\x00\x1a\x00\x0c\x00M\x00a\x00c\x00i\x00n\x00t\x00o\x00s\x00h\x00 \x00H\x00D\x00\x13\x00\x01/\x00\x00\x15\x00\x02\x00\x14\xff\xff\x00\x00\xff\xff\x00\x00',
'backgroundColorBlue': 1.0,
'iconSize': 96.0,
'backgroundColorGreen': 1.0,
'arrangeBy': 'none',
'showIconPreview': True,
'gridSpacing': 100.0,
'gridOffsetY': 0.0,
'showItemInfo': False,
'labelOnBottom': True,
'backgroundType': 2,
'backgroundColorRed': 1.0
}
alias = Alias.from_bytes(icvp['backgroundImageAlias'])
alias.volume.name = package_name_ns
alias.volume.posix_path = '/Volumes/' + package_name_ns
alias.volume.disk_image_alias.target.filename = package_name_ns + '.temp.dmg'
alias.volume.disk_image_alias.target.carbon_path = 'Macintosh HD:Users:\x00asspenniesuser:\x00Documents:\x00asspennies:\x00asspennies:\x00' + package_name_ns + '.temp.dmg'
alias.volume.disk_image_alias.target.posix_path = 'Users/asspenniesuser/Documents/asspennies/asspennies/' + package_name_ns + '.temp.dmg'
alias.target.carbon_path = package_name_ns + ':.background:\x00background.tiff'
icvp['backgroundImageAlias'] = biplist.Data(alias.to_bytes())
ds['.']['icvp'] = icvp
ds['.']['vSrn'] = ('long', 1)
ds['Applications']['Iloc'] = (370, 156)
ds['AssPennies-Qt.app']['Iloc'] = (128, 156)
ds.flush()
ds.close()
| true | true |
f72579efb9020b0aeda91f6744f1f71d26ad9971 | 380 | py | Python | yandex_checkout/domain/models/payment_data/payment_data_factory.py | pavel52rus/yandex-checkout-sdk-python | 10c8b0ce12712bca675254f2a230f9fc0e1cb9b4 | [
"MIT"
] | null | null | null | yandex_checkout/domain/models/payment_data/payment_data_factory.py | pavel52rus/yandex-checkout-sdk-python | 10c8b0ce12712bca675254f2a230f9fc0e1cb9b4 | [
"MIT"
] | null | null | null | yandex_checkout/domain/models/payment_data/payment_data_factory.py | pavel52rus/yandex-checkout-sdk-python | 10c8b0ce12712bca675254f2a230f9fc0e1cb9b4 | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
from yandex_checkout.domain.common.type_factory import TypeFactory
from yandex_checkout.domain.models.payment_data.payment_data_class_map import PaymentDataClassMap
class PaymentDataFactory(TypeFactory):
"""
Factory for payment data objects
"""
def __init__(self):
super(PaymentDataFactory, self).__init__(PaymentDataClassMap())
| 29.230769 | 97 | 0.765789 |
from yandex_checkout.domain.common.type_factory import TypeFactory
from yandex_checkout.domain.models.payment_data.payment_data_class_map import PaymentDataClassMap
class PaymentDataFactory(TypeFactory):
def __init__(self):
super(PaymentDataFactory, self).__init__(PaymentDataClassMap())
| true | true |
f7257ab79e200ce2c0c75e0ae6d7b38cf586e521 | 4,649 | py | Python | ee/clickhouse/queries/paths/path_event_query.py | thinhnguyenuit/posthog | 4758e66790485587d29a617174158d07341342f8 | [
"MIT"
] | null | null | null | ee/clickhouse/queries/paths/path_event_query.py | thinhnguyenuit/posthog | 4758e66790485587d29a617174158d07341342f8 | [
"MIT"
] | null | null | null | ee/clickhouse/queries/paths/path_event_query.py | thinhnguyenuit/posthog | 4758e66790485587d29a617174158d07341342f8 | [
"MIT"
] | null | null | null | from typing import Any, Dict, Tuple
from ee.clickhouse.models.property import get_property_string_expr
from ee.clickhouse.queries.event_query import ClickhouseEventQuery
from posthog.constants import AUTOCAPTURE_EVENT, PAGEVIEW_EVENT, SCREEN_EVENT
from posthog.models.filters.path_filter import PathFilter
class PathEventQuery(ClickhouseEventQuery):
FUNNEL_PERSONS_ALIAS = "funnel_persons"
_filter: PathFilter
def __init__(
self,
filter: PathFilter,
team_id: int,
round_interval=False,
should_join_distinct_ids=False,
should_join_persons=False,
**kwargs,
) -> None:
super().__init__(filter, team_id, round_interval, should_join_distinct_ids, should_join_persons, **kwargs)
def get_query(self) -> Tuple[str, Dict[str, Any]]:
# TODO: ColumnOptimizer with options like self._filter.include_pageviews, self._filter.include_screenviews,
funnel_paths_timestamp = ""
funnel_paths_join = ""
funnel_paths_filter = ""
if self._filter.funnel_paths:
funnel_paths_timestamp = f"{self.FUNNEL_PERSONS_ALIAS}.timestamp as min_timestamp"
funnel_paths_join = f"JOIN {self.FUNNEL_PERSONS_ALIAS} ON {self.FUNNEL_PERSONS_ALIAS}.person_id = {self.DISTINCT_ID_TABLE_ALIAS}.person_id"
funnel_paths_filter = f"AND {self.EVENT_TABLE_ALIAS}.timestamp >= min_timestamp"
_fields = [
f"{self.EVENT_TABLE_ALIAS}.timestamp AS timestamp",
(
f"if(event = '{SCREEN_EVENT}', {self._get_screen_name_parsing()}, "
f"if({self.EVENT_TABLE_ALIAS}.event = '{PAGEVIEW_EVENT}', {self._get_current_url_parsing()}, "
f"if({self.EVENT_TABLE_ALIAS}.event = '{AUTOCAPTURE_EVENT}', concat('autocapture:', {self.EVENT_TABLE_ALIAS}.elements_chain), "
f"{self.EVENT_TABLE_ALIAS}.event))) AS path_item"
),
f"{self.DISTINCT_ID_TABLE_ALIAS}.person_id as person_id" if self._should_join_distinct_ids else "",
funnel_paths_timestamp,
]
_fields = list(filter(None, _fields))
date_query, date_params = self._get_date_filter()
self.params.update(date_params)
prop_filters = self._filter.properties
prop_query, prop_params = self._get_props(prop_filters)
self.params.update(prop_params)
event_query, event_params = self._get_event_query()
self.params.update(event_params)
query = f"""
SELECT {','.join(_fields)} FROM events {self.EVENT_TABLE_ALIAS}
{self._get_disintct_id_query()}
{self._get_person_query()}
{funnel_paths_join}
WHERE team_id = %(team_id)s
{event_query}
{date_query}
{prop_query}
{funnel_paths_filter}
ORDER BY {self.DISTINCT_ID_TABLE_ALIAS}.person_id, {self.EVENT_TABLE_ALIAS}.timestamp
"""
return query, self.params
def _determine_should_join_distinct_ids(self) -> None:
self._should_join_distinct_ids = True
def _get_current_url_parsing(self):
path_type, _ = get_property_string_expr("events", "$current_url", "'$current_url'", "properties")
return f"if(length({path_type}) > 1, trim( TRAILING '/' FROM {path_type}), {path_type})"
def _get_screen_name_parsing(self):
path_type, _ = get_property_string_expr("events", "$screen_name", "'$screen_name'", "properties")
return path_type
def _get_event_query(self) -> Tuple[str, Dict[str, Any]]:
params: Dict[str, Any] = {}
conditions = []
or_conditions = []
if self._filter.include_pageviews:
or_conditions.append(f"event = '{PAGEVIEW_EVENT}'")
if self._filter.include_screenviews:
or_conditions.append(f"event = '{SCREEN_EVENT}'")
if self._filter.include_autocaptures:
or_conditions.append(f"event = '{AUTOCAPTURE_EVENT}'")
if self._filter.include_all_custom_events:
or_conditions.append(f"NOT event LIKE '$%%'")
if self._filter.custom_events:
or_conditions.append(f"event IN %(custom_events)s")
params["custom_events"] = self._filter.custom_events
if or_conditions:
conditions.append(f"({' OR '.join(or_conditions)})")
if self._filter.exclude_events:
conditions.append(f"NOT event IN %(exclude_events)s")
params["exclude_events"] = self._filter.exclude_events
if conditions:
return f" AND {' AND '.join(conditions)}", params
return "", {}
| 39.735043 | 151 | 0.655195 | from typing import Any, Dict, Tuple
from ee.clickhouse.models.property import get_property_string_expr
from ee.clickhouse.queries.event_query import ClickhouseEventQuery
from posthog.constants import AUTOCAPTURE_EVENT, PAGEVIEW_EVENT, SCREEN_EVENT
from posthog.models.filters.path_filter import PathFilter
class PathEventQuery(ClickhouseEventQuery):
FUNNEL_PERSONS_ALIAS = "funnel_persons"
_filter: PathFilter
def __init__(
self,
filter: PathFilter,
team_id: int,
round_interval=False,
should_join_distinct_ids=False,
should_join_persons=False,
**kwargs,
) -> None:
super().__init__(filter, team_id, round_interval, should_join_distinct_ids, should_join_persons, **kwargs)
def get_query(self) -> Tuple[str, Dict[str, Any]]:
funnel_paths_timestamp = ""
funnel_paths_join = ""
funnel_paths_filter = ""
if self._filter.funnel_paths:
funnel_paths_timestamp = f"{self.FUNNEL_PERSONS_ALIAS}.timestamp as min_timestamp"
funnel_paths_join = f"JOIN {self.FUNNEL_PERSONS_ALIAS} ON {self.FUNNEL_PERSONS_ALIAS}.person_id = {self.DISTINCT_ID_TABLE_ALIAS}.person_id"
funnel_paths_filter = f"AND {self.EVENT_TABLE_ALIAS}.timestamp >= min_timestamp"
_fields = [
f"{self.EVENT_TABLE_ALIAS}.timestamp AS timestamp",
(
f"if(event = '{SCREEN_EVENT}', {self._get_screen_name_parsing()}, "
f"if({self.EVENT_TABLE_ALIAS}.event = '{PAGEVIEW_EVENT}', {self._get_current_url_parsing()}, "
f"if({self.EVENT_TABLE_ALIAS}.event = '{AUTOCAPTURE_EVENT}', concat('autocapture:', {self.EVENT_TABLE_ALIAS}.elements_chain), "
f"{self.EVENT_TABLE_ALIAS}.event))) AS path_item"
),
f"{self.DISTINCT_ID_TABLE_ALIAS}.person_id as person_id" if self._should_join_distinct_ids else "",
funnel_paths_timestamp,
]
_fields = list(filter(None, _fields))
date_query, date_params = self._get_date_filter()
self.params.update(date_params)
prop_filters = self._filter.properties
prop_query, prop_params = self._get_props(prop_filters)
self.params.update(prop_params)
event_query, event_params = self._get_event_query()
self.params.update(event_params)
query = f"""
SELECT {','.join(_fields)} FROM events {self.EVENT_TABLE_ALIAS}
{self._get_disintct_id_query()}
{self._get_person_query()}
{funnel_paths_join}
WHERE team_id = %(team_id)s
{event_query}
{date_query}
{prop_query}
{funnel_paths_filter}
ORDER BY {self.DISTINCT_ID_TABLE_ALIAS}.person_id, {self.EVENT_TABLE_ALIAS}.timestamp
"""
return query, self.params
def _determine_should_join_distinct_ids(self) -> None:
self._should_join_distinct_ids = True
def _get_current_url_parsing(self):
path_type, _ = get_property_string_expr("events", "$current_url", "'$current_url'", "properties")
return f"if(length({path_type}) > 1, trim( TRAILING '/' FROM {path_type}), {path_type})"
def _get_screen_name_parsing(self):
path_type, _ = get_property_string_expr("events", "$screen_name", "'$screen_name'", "properties")
return path_type
def _get_event_query(self) -> Tuple[str, Dict[str, Any]]:
params: Dict[str, Any] = {}
conditions = []
or_conditions = []
if self._filter.include_pageviews:
or_conditions.append(f"event = '{PAGEVIEW_EVENT}'")
if self._filter.include_screenviews:
or_conditions.append(f"event = '{SCREEN_EVENT}'")
if self._filter.include_autocaptures:
or_conditions.append(f"event = '{AUTOCAPTURE_EVENT}'")
if self._filter.include_all_custom_events:
or_conditions.append(f"NOT event LIKE '$%%'")
if self._filter.custom_events:
or_conditions.append(f"event IN %(custom_events)s")
params["custom_events"] = self._filter.custom_events
if or_conditions:
conditions.append(f"({' OR '.join(or_conditions)})")
if self._filter.exclude_events:
conditions.append(f"NOT event IN %(exclude_events)s")
params["exclude_events"] = self._filter.exclude_events
if conditions:
return f" AND {' AND '.join(conditions)}", params
return "", {}
| true | true |
f7257ab8f76526d2fc5780943a2451a7b5e04d54 | 4,946 | py | Python | example_scenes/basic.py | Pow3r5/manim | 2972a64342aa5ae72977b444f653b05250ab1f8f | [
"MIT"
] | 2 | 2022-03-31T08:31:00.000Z | 2022-03-31T08:31:43.000Z | example_scenes/basic.py | Pow3r5/manim | 2972a64342aa5ae72977b444f653b05250ab1f8f | [
"MIT"
] | null | null | null | example_scenes/basic.py | Pow3r5/manim | 2972a64342aa5ae72977b444f653b05250ab1f8f | [
"MIT"
] | null | null | null | #!/usr/bin/env python
from manim import *
# To watch one of these scenes, run the following:
# python --quality m manim -p example_scenes.py SquareToCircle
#
# Use the flag --quality l for a faster rendering at a lower quality.
# Use -s to skip to the end and just save the final frame
# Use the -p to have preview of the animation (or image, if -s was
# used) pop up once done.
# Use -n <number> to skip ahead to the nth animation of a scene.
# Use -r <number> to specify a resolution (for example, -r 1920,1080
# for a 1920x1080 video)
class OpeningManim(Scene):
def construct(self):
title = Tex(r"This is some \LaTeX")
basel = MathTex(r"\sum_{n=1}^\infty \frac{1}{n^2} = \frac{\pi^2}{6}")
VGroup(title, basel).arrange(DOWN)
self.play(
Write(title),
FadeIn(basel, shift=DOWN),
)
self.wait()
transform_title = Tex("That was a transform")
transform_title.to_corner(UP + LEFT)
self.play(
Transform(title, transform_title),
LaggedStart(*(FadeOut(obj, shift=DOWN) for obj in basel)),
)
self.wait()
grid = NumberPlane()
grid_title = Tex("This is a grid", font_size=72)
grid_title.move_to(transform_title)
self.add(grid, grid_title) # Make sure title is on top of grid
self.play(
FadeOut(title),
FadeIn(grid_title, shift=UP),
Create(grid, run_time=3, lag_ratio=0.1),
)
self.wait()
grid_transform_title = Tex(
r"That was a non-linear function \\ applied to the grid",
)
grid_transform_title.move_to(grid_title, UL)
grid.prepare_for_nonlinear_transform()
self.play(
grid.animate.apply_function(
lambda p: p
+ np.array(
[
np.sin(p[1]),
np.sin(p[0]),
0,
],
),
),
run_time=3,
)
self.wait()
self.play(Transform(grid_title, grid_transform_title))
self.wait()
class SquareToCircle(Scene):
def construct(self):
circle = Circle()
square = Square()
square.flip(RIGHT)
square.rotate(-3 * TAU / 8)
circle.set_fill(PINK, opacity=0.5)
self.play(Create(square))
self.play(Transform(square, circle))
self.play(FadeOut(square))
class WarpSquare(Scene):
def construct(self):
square = Square()
self.play(
ApplyPointwiseFunction(
lambda point: complex_to_R3(np.exp(R3_to_complex(point))),
square,
),
)
self.wait()
class WriteStuff(Scene):
def construct(self):
example_text = Tex("This is a some text", tex_to_color_map={"text": YELLOW})
example_tex = MathTex(
"\\sum_{k=1}^\\infty {1 \\over k^2} = {\\pi^2 \\over 6}",
)
group = VGroup(example_text, example_tex)
group.arrange(DOWN)
group.width = config["frame_width"] - 2 * LARGE_BUFF
self.play(Write(example_text))
self.play(Write(example_tex))
self.wait()
class UpdatersExample(Scene):
def construct(self):
decimal = DecimalNumber(
0,
show_ellipsis=True,
num_decimal_places=3,
include_sign=True,
)
square = Square().to_edge(UP)
decimal.add_updater(lambda d: d.next_to(square, RIGHT))
decimal.add_updater(lambda d: d.set_value(square.get_center()[1]))
self.add(square, decimal)
self.play(
square.animate.to_edge(DOWN),
rate_func=there_and_back,
run_time=5,
)
self.wait()
class SpiralInExample(Scene):
def construct(self):
logo_green = "#81b29a"
logo_blue = "#454866"
logo_red = "#e07a5f"
font_color = "#ece6e2"
pi = MathTex(r"\pi").scale(7).set_color(font_color)
pi.shift(2.25 * LEFT + 1.5 * UP)
circle = Circle(color=logo_green, fill_opacity=0.7, stroke_width=0).shift(LEFT)
square = Square(color=logo_blue, fill_opacity=0.8, stroke_width=0).shift(UP)
triangle = Triangle(color=logo_red, fill_opacity=0.9, stroke_width=0).shift(
RIGHT
)
pentagon = Polygon(
*[
[np.cos(2 * np.pi / 5 * i), np.sin(2 * np.pi / 5 * i), 0]
for i in range(5)
],
color=PURPLE_B,
fill_opacity=1,
stroke_width=0
).shift(UP + 2 * RIGHT)
shapes = VGroup(triangle, square, circle, pentagon, pi)
self.play(SpiralIn(shapes, fade_in_fraction=0.9))
self.wait()
self.play(FadeOut(shapes))
# See many more examples at https://docs.manim.community/en/stable/examples.html
| 29.975758 | 87 | 0.558229 |
from manim import *
class OpeningManim(Scene):
def construct(self):
title = Tex(r"This is some \LaTeX")
basel = MathTex(r"\sum_{n=1}^\infty \frac{1}{n^2} = \frac{\pi^2}{6}")
VGroup(title, basel).arrange(DOWN)
self.play(
Write(title),
FadeIn(basel, shift=DOWN),
)
self.wait()
transform_title = Tex("That was a transform")
transform_title.to_corner(UP + LEFT)
self.play(
Transform(title, transform_title),
LaggedStart(*(FadeOut(obj, shift=DOWN) for obj in basel)),
)
self.wait()
grid = NumberPlane()
grid_title = Tex("This is a grid", font_size=72)
grid_title.move_to(transform_title)
self.add(grid, grid_title)
self.play(
FadeOut(title),
FadeIn(grid_title, shift=UP),
Create(grid, run_time=3, lag_ratio=0.1),
)
self.wait()
grid_transform_title = Tex(
r"That was a non-linear function \\ applied to the grid",
)
grid_transform_title.move_to(grid_title, UL)
grid.prepare_for_nonlinear_transform()
self.play(
grid.animate.apply_function(
lambda p: p
+ np.array(
[
np.sin(p[1]),
np.sin(p[0]),
0,
],
),
),
run_time=3,
)
self.wait()
self.play(Transform(grid_title, grid_transform_title))
self.wait()
class SquareToCircle(Scene):
def construct(self):
circle = Circle()
square = Square()
square.flip(RIGHT)
square.rotate(-3 * TAU / 8)
circle.set_fill(PINK, opacity=0.5)
self.play(Create(square))
self.play(Transform(square, circle))
self.play(FadeOut(square))
class WarpSquare(Scene):
def construct(self):
square = Square()
self.play(
ApplyPointwiseFunction(
lambda point: complex_to_R3(np.exp(R3_to_complex(point))),
square,
),
)
self.wait()
class WriteStuff(Scene):
def construct(self):
example_text = Tex("This is a some text", tex_to_color_map={"text": YELLOW})
example_tex = MathTex(
"\\sum_{k=1}^\\infty {1 \\over k^2} = {\\pi^2 \\over 6}",
)
group = VGroup(example_text, example_tex)
group.arrange(DOWN)
group.width = config["frame_width"] - 2 * LARGE_BUFF
self.play(Write(example_text))
self.play(Write(example_tex))
self.wait()
class UpdatersExample(Scene):
def construct(self):
decimal = DecimalNumber(
0,
show_ellipsis=True,
num_decimal_places=3,
include_sign=True,
)
square = Square().to_edge(UP)
decimal.add_updater(lambda d: d.next_to(square, RIGHT))
decimal.add_updater(lambda d: d.set_value(square.get_center()[1]))
self.add(square, decimal)
self.play(
square.animate.to_edge(DOWN),
rate_func=there_and_back,
run_time=5,
)
self.wait()
class SpiralInExample(Scene):
def construct(self):
logo_green = "#81b29a"
logo_blue = "#454866"
logo_red = "#e07a5f"
font_color = "#ece6e2"
pi = MathTex(r"\pi").scale(7).set_color(font_color)
pi.shift(2.25 * LEFT + 1.5 * UP)
circle = Circle(color=logo_green, fill_opacity=0.7, stroke_width=0).shift(LEFT)
square = Square(color=logo_blue, fill_opacity=0.8, stroke_width=0).shift(UP)
triangle = Triangle(color=logo_red, fill_opacity=0.9, stroke_width=0).shift(
RIGHT
)
pentagon = Polygon(
*[
[np.cos(2 * np.pi / 5 * i), np.sin(2 * np.pi / 5 * i), 0]
for i in range(5)
],
color=PURPLE_B,
fill_opacity=1,
stroke_width=0
).shift(UP + 2 * RIGHT)
shapes = VGroup(triangle, square, circle, pentagon, pi)
self.play(SpiralIn(shapes, fade_in_fraction=0.9))
self.wait()
self.play(FadeOut(shapes))
| true | true |
f7257af7f5369ac97b09687baeec3f79676d59fb | 17,484 | py | Python | SE4TeC_demo/GUI_function.py | JingweiZuo/SE2TeC | f2aab845aa648e366d0f6917a5d8abfd4d556d13 | [
"Apache-2.0"
] | 1 | 2020-05-10T11:23:11.000Z | 2020-05-10T11:23:11.000Z | SE4TeC_demo/GUI_function.py | JingweiZuo/SE4TeC | f2aab845aa648e366d0f6917a5d8abfd4d556d13 | [
"Apache-2.0"
] | null | null | null | SE4TeC_demo/GUI_function.py | JingweiZuo/SE4TeC | f2aab845aa648e366d0f6917a5d8abfd4d556d13 | [
"Apache-2.0"
] | null | null | null | import time
import tkinter as tk
from tkinter import *
import tkinter.filedialog as filedialog
from tkinter.filedialog import askopenfilename
import utils.utils as util
import utils.similarity_measures as sm
import SMAP.MatrixProfile as mp
import matplotlib
matplotlib.use("TkAgg")
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
from matplotlib.figure import Figure
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
LARGE_FONT= ("Verdana", 12)
class gui_function:
def __init__(self, master):
self.filename = 'file name'
self.training_filename = 'choose training set'
self.testing_filename = 'choose testing set'
#transfer the main test part to the class
self.master = master
self.dataset = util.Dataset()
self.testdataset = util.Dataset()
self.dataset_name = None
self.shapeletList1 = []
self.shapeletList2 = []
def add_dataset(self):
self.dataset_name = askopenfilename(parent=self.master, title="Choose a file")
array_tsdict = util.load_dataset(self.dataset_name)
dir = self.dataset_name.split("/")
datasetname = dir[-1]
self.dataset.update(array_tsdict, datasetname)
self.master.v_dsname.set(self.dataset.name)
self.master.v_tslength.set(self.dataset.tslength)
self.master.v_tsnbr.set(self.dataset.size)
self.master.v_classnbr.set(len(self.dataset.ClassList))
self.master.show_frame(self.master.frame2, "SMAPPage")
def add_testing_file(self):
self.testfile_name = askopenfilename(parent=self.master, title="Choose a file")
array_tsdict = util.load_dataset(self.testfile_name)
dir = self.testfile_name.split("/")
datasetname = dir[-1]
self.testdataset.update(array_tsdict, datasetname)
self.master.v_testdsname.set(self.testdataset.name)
self.master.v_testtslength.set(self.testdataset.tslength)
self.master.v_testtsnbr.set(self.testdataset.size)
self.master.v_testclassnbr.set(len(self.testdataset.ClassList))
self.master.testdataset= self.testdataset
def ShowAlgoFrame(self, algorithm):
self.master.frame2_1[algorithm].tkraise()
self.master.frame2_1[algorithm].grid(row=0, column=0, sticky=W)
def extractDP(self, master):
self.nbr_source = master.v_source.get()
self.nbr_target = master.v_target.get()
dataset = master.dataset
hash_source = dataset.tsNameDir[self.nbr_source]
hash_target = dataset.tsNameDir[self.nbr_target]
self.source = dataset.tsObjectDir[hash_source]
self.target = dataset.tsObjectDir[hash_target]
self.m = master.v_queryL.get()
index_start = master.v_queryI.get()
data = self.target.timeseries
index_end = index_start + self.m
query = self.source.timeseries[index_start:index_end]
#DP = sm.mass_v2(data, query)
#DP = sm.mass_v1(query, data)
DP = sm.euclidean_distance_unequal_lengths(data, query)
# display the figures on the CANVAS of the GUI
# CANVAS
# remove the axis_x of "self.axe2"
plt.setp(self.master.ax2.get_xaxis(), visible=False)
self.master.ax2.spines['bottom'].set_visible(False)
self.master.ax3.clear() # clear the previous plot at the same position
x = range(len(DP))
self.master.ax3.spines['top'].set_visible(False)
self.master.ax3.spines['right'].set_visible(False)
self.master.ax3.set_ylabel("Distance Profile")
self.master.ax3.plot(x, DP, linewidth=0.5, label="D. P. of Query in " +self.nbr_target)
self.master.ax3.legend()
self.master.canvas.draw()
# show the Nearest Neighbor in target TS
DP_list = DP.tolist()
index_inValue = DP_list.index(min(DP_list))
index_end = index_inValue + master.m
NearestN = self.target.timeseries[index_inValue:index_end]
x_target = range(len(self.target.timeseries))
x_NearestN = range(index_inValue, index_end)
self.ax2 = self.master.ax2
self.ax2.clear()
self.ax2.plot(x_target, self.target.timeseries, linewidth=0.5, label=self.nbr_target)
self.ax2.plot(x_NearestN, NearestN, linewidth=2, label="Nearest Neighbor of Query")
self.ax2.spines['top'].set_visible(False)
self.ax2.spines['right'].set_visible(False)
self.ax2.set_ylabel("Target TS")
self.ax2.legend(loc="upper right")
self.master.canvas.draw()
def extractMP(self, master):
self.nbr_source = master.v_source.get()
self.nbr_target = master.v_target.get()
dataset = master.dataset
hash_source = dataset.tsNameDir[self.nbr_source]
hash_target = dataset.tsNameDir[self.nbr_target]
self.source = dataset.tsObjectDir[hash_source]
self.target = dataset.tsObjectDir[hash_target]
self.m = master.v_queryL.get()
dp_all, MP= mp.computeMP(self.source, self.target, self.m, "mass_v2")
# CANVAS
# remove the axis_x of "self.axe2"
plt.setp(self.master.ax2.get_xaxis(), visible=False)
self.master.ax2.spines['bottom'].set_visible(False)
self.master.ax3.clear() # clear the previous plot at the same position
x = range(len(MP))
self.master.ax3.spines['top'].set_visible(False)
self.master.ax3.spines['right'].set_visible(False)
self.master.ax3.set_ylabel("Matrix Profile")
self.master.ax3.plot(x, MP, linewidth=0.5, label="M. P. of "+self.nbr_source+" towards " +self.nbr_target)
self.master.ax3.legend()
self.master.canvas.draw()
# show the matching pair in Source and Target TS
index_source = MP.index(min(MP))
index_source_end = index_source + self.m
x_pair_source = range(index_source, index_source_end)
pair_source = self.source.timeseries[index_source:index_source_end]
DP = sm.euclidean_distance_unequal_lengths(self.target.timeseries, pair_source)
index_target = DP.tolist().index(min(DP.tolist()))
index_target_end = index_target + self.m
x_pair_target = range(index_target, index_target_end)
pair_target = self.target.timeseries[index_target:index_target_end]
# remove the Query in Source TS
self.master.ax1.clear()
x = range(len(self.source.timeseries))
self.master.ax1.spines['top'].set_visible(False)
self.master.ax1.spines['right'].set_visible(False)
self.master.ax1.set_ylabel("Source TS")
self.master.ax1.plot(x_pair_source, pair_source, linewidth=2, color="red", label="Nearest Pair in source")
self.master.ax1.plot(x, self.source.timeseries, linewidth=0.5, label=self.nbr_source)
self.master.ax1.legend()
self.master.canvas.draw()
# remove the Nearest Neighbor in Target TS
self.master.ax2.clear()
x = range(len(self.target.timeseries))
self.master.ax2.spines['top'].set_visible(False)
self.master.ax2.spines['right'].set_visible(False)
self.master.ax2.set_ylabel("Target TS")
self.master.ax2.plot(x_pair_target, pair_target, linewidth=2, color="red", label="Nearest Pair in target")
self.master.ax2.plot(x, self.target.timeseries, linewidth=0.5, label=self.nbr_target)
self.master.ax2.legend()
self.master.canvas.draw()
def extractLB(self):
return 0
def extractRP(self, master):
source = master.source
input_class = str(master.v_class.get())
start = time.clock()
DP_all, mp_all, self.dist_differ, dist_threshold, self.dist_side_C, self.dist_side_nonC = mp.computeDistDiffer(source, master.dataset.tsObjectDir, self.m)
end = time.clock()
self.SMAP_time = round(end - start, 2)
if str(source.class_timeseries) == input_class:
RP = self.dist_side_C
else:
RP = self.dist_side_nonC
# CANVAS
# Configire the axis looking (start)
self.master.ax3.clear()
if (self.master.ax2.get_ylabel()!="Rep. Profile"):
self.master.ax2.clear()
plt.setp(self.master.ax2.get_xaxis(), visible=True)
self.master.ax2.spines['bottom'].set_visible(True)
self.master.ax3.axis("off")
# Configire the axis looking (end)
x = range(len(RP))
self.master.ax2.set_ylabel("Rep. Profile")
label = "Rep. P. of " + self.nbr_source + " in class " + input_class
self.master.ax2.plot(x, RP, linewidth=0.5, label=label)
self.master.ax2.legend()
self.master.canvas.draw()
# remove the Query in Source TS
self.master.ax1.clear()
x = range(len(self.source.timeseries))
self.master.ax1.spines['top'].set_visible(False)
self.master.ax1.spines['right'].set_visible(False)
self.master.ax1.set_ylabel("Source TS")
self.master.ax1.plot(x, self.source.timeseries, linewidth=0.5, label=self.nbr_source)
self.master.ax1.legend()
self.master.canvas.draw()
def extractDiscP(self, master):
'''source = master.source
dp_all, mp_all, dist_differ, dist_threshold, dist_side_C, dist_side_nonC = mp.computeDistDiffer(source, master.dataset.tsObjectDir, self.m)'''
DiscP = self.dist_differ
# CANVAS
# Configire the axis looking (start)
plt.setp(self.master.ax2.get_xaxis(), visible=False)
self.master.ax2.spines['bottom'].set_visible(False)
self.master.ax3.axis("on")
# Configire the axis looking (end)
x = range(len(DiscP))
self.master.ax3.set_ylabel("Discm. Profile")
label = "Discm. P. of " + self.nbr_source
self.master.ax3.plot(x, DiscP, linewidth=0.5, label=label)
self.master.ax3.legend()
self.master.canvas.draw()
# show the pattern found in source TS
discP_list = DiscP.tolist()
index_maxValue = discP_list.index(max(discP_list))
index_end = index_maxValue + master.m
source = master.source.timeseries
pattern = source[index_maxValue:index_end]
x_source = range(len(source))
x_pattern = range(index_maxValue, index_end)
# CANVAS
self.ax1 = self.master.ax1
self.ax1.clear()
self.ax1.plot(x_source, source, linewidth=0.5, label="Source TS")
self.ax1.plot(x_pattern, pattern, linewidth=2, color="red", label="Candidate Shaplet in "+ master.v_source.get())
self.ax1.spines['top'].set_visible(False)
self.ax1.spines['right'].set_visible(False)
self.ax1.set_ylabel("Source TS")
self.ax1.legend(loc="upper right")
self.master.canvas.draw()
self.master.v_timeSMAP.set(self.SMAP_time)
def extractDiscP_LB(self, master):
self.master.v_timeSMAPLB.set(0)
def drawShapelet(self, path, filename):
testFile = pd.read_csv(path + filename, header=None)
Class = testFile[0][0]
shapData = testFile[1][0]
shapData = shapData.strip('()').replace('[', '').replace(']', '')
shapeletList = []
# shapObjectList: DD, Thresh
shapObjectList = shapData.split("),(")
for shapObject in shapObjectList:
shap = Shapelet()
shapObject = shapObject.split(',')
shap.DD = float(shapObject[0])
shap.thresh = float(shapObject[1])
shap.Class = Class
shap.subseq = [float(s) for s in shapObject[2:]]
shapeletList.append(shap)
return shapeletList
def drawTS(self, path, filename):
tsObjectList1 = []
tsObjectList2 = []
testFile = pd.read_csv(path + filename, header=None)
tsClass1 = testFile[testFile[1] == 1]
tsClass2 = testFile[testFile[1] == -1]
for i in tsClass1.index:
ts = timeseries()
row = tsClass1.loc[i]
ts.id = row[0]
ts.Class = row[1]
ts.seq = row[2].split(',')
ts.seq = [float(val) for val in ts.seq]
tsObjectList1.append(ts)
for i in tsClass2.index:
ts = timeseries()
row = tsClass2.loc[i]
ts.id = row[0]
ts.Class = row[1]
ts.seq = row[2].split(',')
ts.seq = [float(val) for val in ts.seq]
tsObjectList2.append(ts)
return tsObjectList1, tsObjectList2
def showTSset(self):
path_ECG = "/Users/Jingwei/Desktop/PhD_study/Done/EDBTdemo2018/SMAP_results/ECG200/TS_raw/"
file_ECG = "TS.csv"
path = path_ECG
filename = file_ECG
tsObjectC1, tsObjectC2 = self.drawTS(path, filename)
self.fig = self.master.figure
if self.master.v_class.get()=="1.0":
self.fig.clf()
self.ax1 = self.fig.add_subplot(211)
self.ax2 = self.fig.add_subplot(212)
input_class = self.master.v_class.get()
if input_class == "1.0":
self.ax1.clear()
for ts in tsObjectC1[11:21]:
seq = ts.seq
self.ax1.set_ylabel("TS data class 1")
X = range(0, len(seq))
self.ax1.plot(X, seq, color='green', linewidth=0.5)
elif input_class == "-1.0":
self.ax2.clear()
for ts in tsObjectC2[0:10]:
seq = ts.seq
self.ax2.set_xlabel("index/offset")
self.ax2.set_ylabel("TS data class -1.0")
X = range(0, len(seq))
self.ax2.plot(X, seq, color='green', linewidth=0.5)
self.master.canvas.draw()
def extractShapeletCandidate(self):
path_ECG = "/Users/Jingwei/Desktop/PhD_study/Done/EDBTdemo2018/SMAP_results/ECG200/Shapelets/"
f1_ECG = "part-00043-956f02be-ab81-45db-9679-0bfd9150f5e8.csv" # 1
f2_ECG = "part-00013-956f02be-ab81-45db-9679-0bfd9150f5e8.csv" # -1
path = path_ECG
filename1 = f1_ECG
filename2 = f2_ECG
self.shapeletList1 = self.drawShapelet(path, filename1)
self.shapeletList2 = self.drawShapelet(path, filename2)
input_k = self.master.v_k.get()
input_c = self.master.v_class.get()
self.fig = self.master.figure
if input_c == "1.0":
i = 0
for shap in self.shapeletList1[:input_k]:
self.subaxe = self.fig.add_subplot(211)
shapdata = shap.subseq
# add a shift of 10 for shapelets
X = range(10, len(shapdata)+10)
self.subaxe.plot(X, shapdata, color='red', linewidth=2)
i = i + 0.1
elif input_c == "-1.0":
i = 0
for shap in self.shapeletList2[:input_k]:
self.subaxe = self.fig.add_subplot(212)
shapdata = shap.subseq
X = range(0, len(shapdata))
self.subaxe.plot(X, shapdata, color='blue', linewidth=2)
self.master.canvas.draw()
# canvas._tkcanvas.pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=True)
def extractED(self):
return 0
def extractEDMatrix(self, master):
self.master.v_timeUSE.set(0)
def predict(self, master):
#list of Shapelet from different class
testdataset = master.guiFunc.testdataset
nbr_testTS = master.v_testInstance.get()
print("---callback predict---")
print(nbr_testTS)
if nbr_testTS!="select":
hash_testTS = testdataset.tsNameDir[nbr_testTS]
self.testTS = testdataset.tsObjectDir[hash_testTS]
testTS = self.testTS.timeseries
min_dist = float('inf')
index_target = None
predict_class = '0'
match_shapelet = None
print("length os shapeletList1 is " + str(len(self.shapeletList1)))
for shap in self.shapeletList1 + self.shapeletList2:
DP = sm.euclidean_distance_unequal_lengths(testTS, shap.subseq)
DP = DP.tolist()
DP_min = min(DP)
if min_dist > DP_min:
min_dist = DP_min
index_target = DP.index(DP_min)
match_shapelet = shap
self.testTS = testdataset.tsObjectDir[hash_testTS]
# CANVAS
x = range(len(testTS))
shap_data = match_shapelet.subseq
x_shap = range(index_target, index_target + len(shap_data))
self.master.figure.clf()
self.ax = self.master.figure.add_subplot(111)
self.ax.spines['top'].set_visible(False)
self.ax.spines['right'].set_visible(False)
self.ax.plot(x, testTS, linewidth=0.5, label="testing TS: " + nbr_testTS)
self.ax.plot(x_shap, shap_data, linewidth=2, label="Matching Shapelet")
self.ax.set_ylabel("Testing TS")
self.ax.set_title("Real class: " + str(self.testTS.class_timeseries) + "; Prediction: " + str(match_shapelet.Class))
self.ax.legend(loc="upper right")
self.master.canvas.draw()
class Shapelet(object):
def __init__(self):
self.id = 0.0
self.Class = ''
self.subseq = None
self.DD = 0.0
self.thresh = 0.0
class timeseries(object):
def __init__(self):
self.id = None
self.Class = ''
self.seq = None
| 42.643902 | 162 | 0.623256 | import time
import tkinter as tk
from tkinter import *
import tkinter.filedialog as filedialog
from tkinter.filedialog import askopenfilename
import utils.utils as util
import utils.similarity_measures as sm
import SMAP.MatrixProfile as mp
import matplotlib
matplotlib.use("TkAgg")
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2Tk
from matplotlib.figure import Figure
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
LARGE_FONT= ("Verdana", 12)
class gui_function:
def __init__(self, master):
self.filename = 'file name'
self.training_filename = 'choose training set'
self.testing_filename = 'choose testing set'
self.master = master
self.dataset = util.Dataset()
self.testdataset = util.Dataset()
self.dataset_name = None
self.shapeletList1 = []
self.shapeletList2 = []
def add_dataset(self):
self.dataset_name = askopenfilename(parent=self.master, title="Choose a file")
array_tsdict = util.load_dataset(self.dataset_name)
dir = self.dataset_name.split("/")
datasetname = dir[-1]
self.dataset.update(array_tsdict, datasetname)
self.master.v_dsname.set(self.dataset.name)
self.master.v_tslength.set(self.dataset.tslength)
self.master.v_tsnbr.set(self.dataset.size)
self.master.v_classnbr.set(len(self.dataset.ClassList))
self.master.show_frame(self.master.frame2, "SMAPPage")
def add_testing_file(self):
self.testfile_name = askopenfilename(parent=self.master, title="Choose a file")
array_tsdict = util.load_dataset(self.testfile_name)
dir = self.testfile_name.split("/")
datasetname = dir[-1]
self.testdataset.update(array_tsdict, datasetname)
self.master.v_testdsname.set(self.testdataset.name)
self.master.v_testtslength.set(self.testdataset.tslength)
self.master.v_testtsnbr.set(self.testdataset.size)
self.master.v_testclassnbr.set(len(self.testdataset.ClassList))
self.master.testdataset= self.testdataset
def ShowAlgoFrame(self, algorithm):
self.master.frame2_1[algorithm].tkraise()
self.master.frame2_1[algorithm].grid(row=0, column=0, sticky=W)
def extractDP(self, master):
self.nbr_source = master.v_source.get()
self.nbr_target = master.v_target.get()
dataset = master.dataset
hash_source = dataset.tsNameDir[self.nbr_source]
hash_target = dataset.tsNameDir[self.nbr_target]
self.source = dataset.tsObjectDir[hash_source]
self.target = dataset.tsObjectDir[hash_target]
self.m = master.v_queryL.get()
index_start = master.v_queryI.get()
data = self.target.timeseries
index_end = index_start + self.m
query = self.source.timeseries[index_start:index_end]
DP = sm.euclidean_distance_unequal_lengths(data, query)
plt.setp(self.master.ax2.get_xaxis(), visible=False)
self.master.ax2.spines['bottom'].set_visible(False)
self.master.ax3.clear()
x = range(len(DP))
self.master.ax3.spines['top'].set_visible(False)
self.master.ax3.spines['right'].set_visible(False)
self.master.ax3.set_ylabel("Distance Profile")
self.master.ax3.plot(x, DP, linewidth=0.5, label="D. P. of Query in " +self.nbr_target)
self.master.ax3.legend()
self.master.canvas.draw()
DP_list = DP.tolist()
index_inValue = DP_list.index(min(DP_list))
index_end = index_inValue + master.m
NearestN = self.target.timeseries[index_inValue:index_end]
x_target = range(len(self.target.timeseries))
x_NearestN = range(index_inValue, index_end)
self.ax2 = self.master.ax2
self.ax2.clear()
self.ax2.plot(x_target, self.target.timeseries, linewidth=0.5, label=self.nbr_target)
self.ax2.plot(x_NearestN, NearestN, linewidth=2, label="Nearest Neighbor of Query")
self.ax2.spines['top'].set_visible(False)
self.ax2.spines['right'].set_visible(False)
self.ax2.set_ylabel("Target TS")
self.ax2.legend(loc="upper right")
self.master.canvas.draw()
def extractMP(self, master):
self.nbr_source = master.v_source.get()
self.nbr_target = master.v_target.get()
dataset = master.dataset
hash_source = dataset.tsNameDir[self.nbr_source]
hash_target = dataset.tsNameDir[self.nbr_target]
self.source = dataset.tsObjectDir[hash_source]
self.target = dataset.tsObjectDir[hash_target]
self.m = master.v_queryL.get()
dp_all, MP= mp.computeMP(self.source, self.target, self.m, "mass_v2")
plt.setp(self.master.ax2.get_xaxis(), visible=False)
self.master.ax2.spines['bottom'].set_visible(False)
self.master.ax3.clear()
x = range(len(MP))
self.master.ax3.spines['top'].set_visible(False)
self.master.ax3.spines['right'].set_visible(False)
self.master.ax3.set_ylabel("Matrix Profile")
self.master.ax3.plot(x, MP, linewidth=0.5, label="M. P. of "+self.nbr_source+" towards " +self.nbr_target)
self.master.ax3.legend()
self.master.canvas.draw()
index_source = MP.index(min(MP))
index_source_end = index_source + self.m
x_pair_source = range(index_source, index_source_end)
pair_source = self.source.timeseries[index_source:index_source_end]
DP = sm.euclidean_distance_unequal_lengths(self.target.timeseries, pair_source)
index_target = DP.tolist().index(min(DP.tolist()))
index_target_end = index_target + self.m
x_pair_target = range(index_target, index_target_end)
pair_target = self.target.timeseries[index_target:index_target_end]
self.master.ax1.clear()
x = range(len(self.source.timeseries))
self.master.ax1.spines['top'].set_visible(False)
self.master.ax1.spines['right'].set_visible(False)
self.master.ax1.set_ylabel("Source TS")
self.master.ax1.plot(x_pair_source, pair_source, linewidth=2, color="red", label="Nearest Pair in source")
self.master.ax1.plot(x, self.source.timeseries, linewidth=0.5, label=self.nbr_source)
self.master.ax1.legend()
self.master.canvas.draw()
self.master.ax2.clear()
x = range(len(self.target.timeseries))
self.master.ax2.spines['top'].set_visible(False)
self.master.ax2.spines['right'].set_visible(False)
self.master.ax2.set_ylabel("Target TS")
self.master.ax2.plot(x_pair_target, pair_target, linewidth=2, color="red", label="Nearest Pair in target")
self.master.ax2.plot(x, self.target.timeseries, linewidth=0.5, label=self.nbr_target)
self.master.ax2.legend()
self.master.canvas.draw()
def extractLB(self):
return 0
def extractRP(self, master):
source = master.source
input_class = str(master.v_class.get())
start = time.clock()
DP_all, mp_all, self.dist_differ, dist_threshold, self.dist_side_C, self.dist_side_nonC = mp.computeDistDiffer(source, master.dataset.tsObjectDir, self.m)
end = time.clock()
self.SMAP_time = round(end - start, 2)
if str(source.class_timeseries) == input_class:
RP = self.dist_side_C
else:
RP = self.dist_side_nonC
self.master.ax3.clear()
if (self.master.ax2.get_ylabel()!="Rep. Profile"):
self.master.ax2.clear()
plt.setp(self.master.ax2.get_xaxis(), visible=True)
self.master.ax2.spines['bottom'].set_visible(True)
self.master.ax3.axis("off")
x = range(len(RP))
self.master.ax2.set_ylabel("Rep. Profile")
label = "Rep. P. of " + self.nbr_source + " in class " + input_class
self.master.ax2.plot(x, RP, linewidth=0.5, label=label)
self.master.ax2.legend()
self.master.canvas.draw()
self.master.ax1.clear()
x = range(len(self.source.timeseries))
self.master.ax1.spines['top'].set_visible(False)
self.master.ax1.spines['right'].set_visible(False)
self.master.ax1.set_ylabel("Source TS")
self.master.ax1.plot(x, self.source.timeseries, linewidth=0.5, label=self.nbr_source)
self.master.ax1.legend()
self.master.canvas.draw()
def extractDiscP(self, master):
DiscP = self.dist_differ
plt.setp(self.master.ax2.get_xaxis(), visible=False)
self.master.ax2.spines['bottom'].set_visible(False)
self.master.ax3.axis("on")
x = range(len(DiscP))
self.master.ax3.set_ylabel("Discm. Profile")
label = "Discm. P. of " + self.nbr_source
self.master.ax3.plot(x, DiscP, linewidth=0.5, label=label)
self.master.ax3.legend()
self.master.canvas.draw()
discP_list = DiscP.tolist()
index_maxValue = discP_list.index(max(discP_list))
index_end = index_maxValue + master.m
source = master.source.timeseries
pattern = source[index_maxValue:index_end]
x_source = range(len(source))
x_pattern = range(index_maxValue, index_end)
self.ax1 = self.master.ax1
self.ax1.clear()
self.ax1.plot(x_source, source, linewidth=0.5, label="Source TS")
self.ax1.plot(x_pattern, pattern, linewidth=2, color="red", label="Candidate Shaplet in "+ master.v_source.get())
self.ax1.spines['top'].set_visible(False)
self.ax1.spines['right'].set_visible(False)
self.ax1.set_ylabel("Source TS")
self.ax1.legend(loc="upper right")
self.master.canvas.draw()
self.master.v_timeSMAP.set(self.SMAP_time)
def extractDiscP_LB(self, master):
self.master.v_timeSMAPLB.set(0)
def drawShapelet(self, path, filename):
testFile = pd.read_csv(path + filename, header=None)
Class = testFile[0][0]
shapData = testFile[1][0]
shapData = shapData.strip('()').replace('[', '').replace(']', '')
shapeletList = []
shapObjectList = shapData.split("),(")
for shapObject in shapObjectList:
shap = Shapelet()
shapObject = shapObject.split(',')
shap.DD = float(shapObject[0])
shap.thresh = float(shapObject[1])
shap.Class = Class
shap.subseq = [float(s) for s in shapObject[2:]]
shapeletList.append(shap)
return shapeletList
def drawTS(self, path, filename):
tsObjectList1 = []
tsObjectList2 = []
testFile = pd.read_csv(path + filename, header=None)
tsClass1 = testFile[testFile[1] == 1]
tsClass2 = testFile[testFile[1] == -1]
for i in tsClass1.index:
ts = timeseries()
row = tsClass1.loc[i]
ts.id = row[0]
ts.Class = row[1]
ts.seq = row[2].split(',')
ts.seq = [float(val) for val in ts.seq]
tsObjectList1.append(ts)
for i in tsClass2.index:
ts = timeseries()
row = tsClass2.loc[i]
ts.id = row[0]
ts.Class = row[1]
ts.seq = row[2].split(',')
ts.seq = [float(val) for val in ts.seq]
tsObjectList2.append(ts)
return tsObjectList1, tsObjectList2
def showTSset(self):
path_ECG = "/Users/Jingwei/Desktop/PhD_study/Done/EDBTdemo2018/SMAP_results/ECG200/TS_raw/"
file_ECG = "TS.csv"
path = path_ECG
filename = file_ECG
tsObjectC1, tsObjectC2 = self.drawTS(path, filename)
self.fig = self.master.figure
if self.master.v_class.get()=="1.0":
self.fig.clf()
self.ax1 = self.fig.add_subplot(211)
self.ax2 = self.fig.add_subplot(212)
input_class = self.master.v_class.get()
if input_class == "1.0":
self.ax1.clear()
for ts in tsObjectC1[11:21]:
seq = ts.seq
self.ax1.set_ylabel("TS data class 1")
X = range(0, len(seq))
self.ax1.plot(X, seq, color='green', linewidth=0.5)
elif input_class == "-1.0":
self.ax2.clear()
for ts in tsObjectC2[0:10]:
seq = ts.seq
self.ax2.set_xlabel("index/offset")
self.ax2.set_ylabel("TS data class -1.0")
X = range(0, len(seq))
self.ax2.plot(X, seq, color='green', linewidth=0.5)
self.master.canvas.draw()
def extractShapeletCandidate(self):
path_ECG = "/Users/Jingwei/Desktop/PhD_study/Done/EDBTdemo2018/SMAP_results/ECG200/Shapelets/"
f1_ECG = "part-00043-956f02be-ab81-45db-9679-0bfd9150f5e8.csv"
f2_ECG = "part-00013-956f02be-ab81-45db-9679-0bfd9150f5e8.csv"
path = path_ECG
filename1 = f1_ECG
filename2 = f2_ECG
self.shapeletList1 = self.drawShapelet(path, filename1)
self.shapeletList2 = self.drawShapelet(path, filename2)
input_k = self.master.v_k.get()
input_c = self.master.v_class.get()
self.fig = self.master.figure
if input_c == "1.0":
i = 0
for shap in self.shapeletList1[:input_k]:
self.subaxe = self.fig.add_subplot(211)
shapdata = shap.subseq
X = range(10, len(shapdata)+10)
self.subaxe.plot(X, shapdata, color='red', linewidth=2)
i = i + 0.1
elif input_c == "-1.0":
i = 0
for shap in self.shapeletList2[:input_k]:
self.subaxe = self.fig.add_subplot(212)
shapdata = shap.subseq
X = range(0, len(shapdata))
self.subaxe.plot(X, shapdata, color='blue', linewidth=2)
self.master.canvas.draw()
def extractED(self):
return 0
def extractEDMatrix(self, master):
self.master.v_timeUSE.set(0)
def predict(self, master):
testdataset = master.guiFunc.testdataset
nbr_testTS = master.v_testInstance.get()
print("---callback predict---")
print(nbr_testTS)
if nbr_testTS!="select":
hash_testTS = testdataset.tsNameDir[nbr_testTS]
self.testTS = testdataset.tsObjectDir[hash_testTS]
testTS = self.testTS.timeseries
min_dist = float('inf')
index_target = None
predict_class = '0'
match_shapelet = None
print("length os shapeletList1 is " + str(len(self.shapeletList1)))
for shap in self.shapeletList1 + self.shapeletList2:
DP = sm.euclidean_distance_unequal_lengths(testTS, shap.subseq)
DP = DP.tolist()
DP_min = min(DP)
if min_dist > DP_min:
min_dist = DP_min
index_target = DP.index(DP_min)
match_shapelet = shap
self.testTS = testdataset.tsObjectDir[hash_testTS]
x = range(len(testTS))
shap_data = match_shapelet.subseq
x_shap = range(index_target, index_target + len(shap_data))
self.master.figure.clf()
self.ax = self.master.figure.add_subplot(111)
self.ax.spines['top'].set_visible(False)
self.ax.spines['right'].set_visible(False)
self.ax.plot(x, testTS, linewidth=0.5, label="testing TS: " + nbr_testTS)
self.ax.plot(x_shap, shap_data, linewidth=2, label="Matching Shapelet")
self.ax.set_ylabel("Testing TS")
self.ax.set_title("Real class: " + str(self.testTS.class_timeseries) + "; Prediction: " + str(match_shapelet.Class))
self.ax.legend(loc="upper right")
self.master.canvas.draw()
class Shapelet(object):
def __init__(self):
self.id = 0.0
self.Class = ''
self.subseq = None
self.DD = 0.0
self.thresh = 0.0
class timeseries(object):
def __init__(self):
self.id = None
self.Class = ''
self.seq = None
| true | true |
f7257b07ee9ab3353bab98ac28e4ae6d47c6bf1c | 2,171 | py | Python | cifar10/models/repnet.py | NNHieu/loss-landscape | dfe517f23993ffbafea99272026d09e074e50b4f | [
"MIT"
] | null | null | null | cifar10/models/repnet.py | NNHieu/loss-landscape | dfe517f23993ffbafea99272026d09e074e50b4f | [
"MIT"
] | null | null | null | cifar10/models/repnet.py | NNHieu/loss-landscape | dfe517f23993ffbafea99272026d09e074e50b4f | [
"MIT"
] | null | null | null |
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import torch.autograd as autograd
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import torch.optim as optim
import os
import argparse
class ResNetLayer(nn.Module):
def __init__(self, n_channels, n_inner_channels, kernel_size=3, num_groups=8):
super().__init__()
self.conv1 = nn.Conv2d(n_channels, n_inner_channels, kernel_size, padding=kernel_size//2, bias=False)
self.conv2 = nn.Conv2d(n_inner_channels, n_channels, kernel_size, padding=kernel_size//2, bias=False)
self.norm1 = nn.GroupNorm(num_groups, n_inner_channels)
self.norm2 = nn.GroupNorm(num_groups, n_channels)
self.norm3 = nn.GroupNorm(num_groups, n_channels)
self.conv1.weight.data.normal_(0, 0.01)
self.conv2.weight.data.normal_(0, 0.01)
def forward(self, z, x):
if z is None:
y = self.norm1(F.relu(self.conv1(x)))
return self.norm3(F.relu(x + self.norm2(self.conv2(y))))
else:
y = self.norm1(F.relu(self.conv1(z)))
return self.norm3(F.relu(z + self.norm2(x + self.conv2(y))))
class RepeatConvLayer(nn.Module):
def __init__(self, f, num_repeat):
super().__init__()
self.f = f
self.num_repeat = num_repeat
def forward(self, x):
z = self.f(None, x)
for i in range(self.num_repeat):
z = self.f(z, x)
return z
def repeatNet(num_repeat):
chan = 48
f = ResNetLayer(chan, 64, kernel_size=3)
model = nn.Sequential(nn.Conv2d(3,chan, kernel_size=3, bias=True, padding=1),
nn.BatchNorm2d(chan),
RepeatConvLayer(f, num_repeat),
nn.BatchNorm2d(chan),
nn.AvgPool2d(8,8),
nn.Flatten(),
nn.Linear(chan*4*4,10))
return model
def repeatNet5():
return repeatNet(5)
def repeatNet10():
return repeatNet(10)
def repeatNet17():
return repeatNet(17)
| 30.577465 | 109 | 0.607094 |
import torch
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import torch.autograd as autograd
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import torch.optim as optim
import os
import argparse
class ResNetLayer(nn.Module):
def __init__(self, n_channels, n_inner_channels, kernel_size=3, num_groups=8):
super().__init__()
self.conv1 = nn.Conv2d(n_channels, n_inner_channels, kernel_size, padding=kernel_size//2, bias=False)
self.conv2 = nn.Conv2d(n_inner_channels, n_channels, kernel_size, padding=kernel_size//2, bias=False)
self.norm1 = nn.GroupNorm(num_groups, n_inner_channels)
self.norm2 = nn.GroupNorm(num_groups, n_channels)
self.norm3 = nn.GroupNorm(num_groups, n_channels)
self.conv1.weight.data.normal_(0, 0.01)
self.conv2.weight.data.normal_(0, 0.01)
def forward(self, z, x):
if z is None:
y = self.norm1(F.relu(self.conv1(x)))
return self.norm3(F.relu(x + self.norm2(self.conv2(y))))
else:
y = self.norm1(F.relu(self.conv1(z)))
return self.norm3(F.relu(z + self.norm2(x + self.conv2(y))))
class RepeatConvLayer(nn.Module):
def __init__(self, f, num_repeat):
super().__init__()
self.f = f
self.num_repeat = num_repeat
def forward(self, x):
z = self.f(None, x)
for i in range(self.num_repeat):
z = self.f(z, x)
return z
def repeatNet(num_repeat):
chan = 48
f = ResNetLayer(chan, 64, kernel_size=3)
model = nn.Sequential(nn.Conv2d(3,chan, kernel_size=3, bias=True, padding=1),
nn.BatchNorm2d(chan),
RepeatConvLayer(f, num_repeat),
nn.BatchNorm2d(chan),
nn.AvgPool2d(8,8),
nn.Flatten(),
nn.Linear(chan*4*4,10))
return model
def repeatNet5():
return repeatNet(5)
def repeatNet10():
return repeatNet(10)
def repeatNet17():
return repeatNet(17)
| true | true |
f7257b2056fac589a4b126844ed598c5e63b20c6 | 635 | py | Python | manage.py | nrsharip/iss-web | e8ca66ade3933dfac4795ba7c44e067c26a079e2 | [
"MIT"
] | 1 | 2020-09-08T21:47:50.000Z | 2020-09-08T21:47:50.000Z | manage.py | nrsharip/iss-web | e8ca66ade3933dfac4795ba7c44e067c26a079e2 | [
"MIT"
] | null | null | null | manage.py | nrsharip/iss-web | e8ca66ade3933dfac4795ba7c44e067c26a079e2 | [
"MIT"
] | null | null | null | #!/usr/bin/env python
"""Django's command-line utility for administrative tasks."""
import os
import sys
def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'web_server_moex.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
| 28.863636 | 79 | 0.686614 |
import os
import sys
def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'web_server_moex.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError(
"Couldn't import Django. Are you sure it's installed and "
"available on your PYTHONPATH environment variable? Did you "
"forget to activate a virtual environment?"
) from exc
execute_from_command_line(sys.argv)
if __name__ == '__main__':
main()
| true | true |
f7257b772a4fb5328778dcbc7b3b0d836de83e58 | 20,431 | py | Python | beta/spreadtrading/stEngine.py | black0144/vnpy | 0d0ea30dad14a0150f7500ff9a62528030321426 | [
"MIT"
] | 18 | 2019-02-21T05:42:41.000Z | 2022-03-31T10:17:51.000Z | beta/spreadtrading/stEngine.py | black0144/vnpy | 0d0ea30dad14a0150f7500ff9a62528030321426 | [
"MIT"
] | 1 | 2018-06-12T10:08:24.000Z | 2018-06-12T10:08:24.000Z | beta/spreadtrading/stEngine.py | black0144/vnpy | 0d0ea30dad14a0150f7500ff9a62528030321426 | [
"MIT"
] | 5 | 2017-12-20T09:57:17.000Z | 2021-08-01T19:47:14.000Z | # encoding: UTF-8
import json
import traceback
import shelve
import parser
import re
from vnpy.event import Event
from vnpy.trader.vtFunction import getJsonPath, getTempPath
from vnpy.trader.vtEvent import (EVENT_TICK, EVENT_TRADE, EVENT_POSITION,
EVENT_TIMER, EVENT_ORDER)
from vnpy.trader.vtObject import (VtSubscribeReq, VtOrderReq,
VtCancelOrderReq, VtLogData)
from vnpy.trader.vtConstant import (DIRECTION_LONG, DIRECTION_SHORT,
OFFSET_OPEN, OFFSET_CLOSE,
PRICETYPE_LIMITPRICE)
from .stBase import (StLeg, StSpread, EVENT_SPREADTRADING_TICK,
EVENT_SPREADTRADING_POS, EVENT_SPREADTRADING_LOG,
EVENT_SPREADTRADING_ALGO, EVENT_SPREADTRADING_ALGOLOG)
from .stAlgo import SniperAlgo
########################################################################
class StDataEngine(object):
"""价差数据计算引擎"""
settingFileName = 'ST_setting.json'
settingFilePath = getJsonPath(settingFileName, __file__)
#----------------------------------------------------------------------
def __init__(self, mainEngine, eventEngine):
"""Constructor"""
self.mainEngine = mainEngine
self.eventEngine = eventEngine
# 腿、价差相关字典
self.legDict = {} # vtSymbol:StLeg
self.spreadDict = {} # name:StSpread
self.vtSymbolSpreadDict = {} # vtSymbol:StSpread
self.registerEvent()
#----------------------------------------------------------------------
def loadSetting(self):
"""加载配置"""
try:
with open(self.settingFilePath) as f:
l = json.load(f)
for setting in l:
result, msg = self.createSpread(setting)
self.writeLog(msg)
self.writeLog(u'价差配置加载完成')
except:
content = u'价差配置加载出错，原因：' + traceback.format_exc()
self.writeLog(content)
#----------------------------------------------------------------------
def saveSetting(self):
"""保存配置"""
with open(self.settingFilePath) as f:
pass
#----------------------------------------------------------------------
def createSpread(self, setting):
"""创建价差"""
result = False
msg = ''
# 检查价差重名
if setting['name'] in self.spreadDict:
msg = u'%s价差存在重名' %setting['name']
return result, msg
# 检查腿是否已使用
l = []
l.append(setting['activeLeg']['vtSymbol'])
for d in setting['passiveLegs']:
l.append(d['vtSymbol'])
for vtSymbol in l:
if vtSymbol in self.vtSymbolSpreadDict:
existingSpread = self.vtSymbolSpreadDict[vtSymbol]
msg = u'%s合约已经存在于%s价差中' %(vtSymbol, existingSpread.name)
return result, msg
# 创建价差
spread = StSpread()
spread.name = setting['name']
spread.formula = setting['formula']
formula = spread.formula
if not re.match("[0-9A-Z\/\+\-\*\(\) ].*", formula) :
msg = u'%s价差存在公式问题请重新编写 %s' % (setting['name'] , spread.formula)
return result, msg
try :
spread.code = parser.expr(formula).compile()
except :
msg = u'%s价差存在公式问题请重新编写 %s' % (setting['name'] , spread.formula)
return result, msg
self.spreadDict[spread.name] = spread
# 创建主动腿
activeSetting = setting['activeLeg']
activeLeg = StLeg()
activeLeg.vtSymbol = str(activeSetting['vtSymbol'])
activeLeg.ratio = float(activeSetting['ratio'])
activeLeg.payup = int(activeSetting['payup'])
activeLeg.legname = str(activeSetting['legname'])
spread.addActiveLeg(activeLeg)
self.legDict[activeLeg.vtSymbol] = activeLeg
self.vtSymbolSpreadDict[activeLeg.vtSymbol] = spread
self.subscribeMarketData(activeLeg.vtSymbol)
# 创建被动腿
passiveSettingList = setting['passiveLegs']
passiveLegList = []
for d in passiveSettingList:
passiveLeg = StLeg()
passiveLeg.vtSymbol = str(d['vtSymbol'])
passiveLeg.ratio = float(d['ratio'])
passiveLeg.payup = int(d['payup'])
passiveLeg.legname = str(d['legname'])
spread.addPassiveLeg(passiveLeg)
self.legDict[passiveLeg.vtSymbol] = passiveLeg
self.vtSymbolSpreadDict[passiveLeg.vtSymbol] = spread
self.subscribeMarketData(passiveLeg.vtSymbol)
# 初始化价差
spread.initSpread()
self.putSpreadTickEvent(spread)
self.putSpreadPosEvent(spread)
# 返回结果
result = True
msg = u'%s价差创建成功' %spread.name
return result, msg
#----------------------------------------------------------------------
def processTickEvent(self, event):
"""处理行情推送"""
# 检查行情是否需要处理
tick = event.dict_['data']
if tick.vtSymbol not in self.legDict:
return
# 更新腿价格
leg = self.legDict[tick.vtSymbol]
leg.bidPrice = tick.bidPrice1
leg.askPrice = tick.askPrice1
leg.bidVolume = tick.bidVolume1
leg.askVolume = tick.askVolume1
# 更新价差价格
spread = self.vtSymbolSpreadDict[tick.vtSymbol]
spread.calculatePrice()
# 发出事件
self.putSpreadTickEvent(spread)
#----------------------------------------------------------------------
def putSpreadTickEvent(self, spread):
"""发出价差行情更新事件"""
event1 = Event(EVENT_SPREADTRADING_TICK+spread.name)
event1.dict_['data'] = spread
self.eventEngine.put(event1)
event2 = Event(EVENT_SPREADTRADING_TICK)
event2.dict_['data'] = spread
self.eventEngine.put(event2)
#----------------------------------------------------------------------
def processTradeEvent(self, event):
"""处理成交推送"""
# 检查成交是否需要处理
trade = event.dict_['data']
if trade.vtSymbol not in self.legDict:
return
# 更新腿持仓
leg = self.legDict[trade.vtSymbol]
direction = trade.direction
offset = trade.offset
if direction == DIRECTION_LONG:
if offset == OFFSET_OPEN:
leg.longPos += trade.volume
else:
leg.shortPos -= trade.volume
else:
if offset == OFFSET_OPEN:
leg.shortPos += trade.volume
else:
leg.longPos -= trade.volume
leg.netPos = leg.longPos - leg.shortPos
# 更新价差持仓
spread = self.vtSymbolSpreadDict[trade.vtSymbol]
spread.calculatePos()
# 推送价差持仓更新
event1 = Event(EVENT_SPREADTRADING_POS+spread.name)
event1.dict_['data'] = spread
self.eventEngine.put(event1)
event2 = Event(EVENT_SPREADTRADING_POS)
event2.dict_['data'] = spread
self.eventEngine.put(event2)
#----------------------------------------------------------------------
def processPosEvent(self, event):
"""处理持仓推送"""
# 检查持仓是否需要处理
pos = event.dict_['data']
if pos.vtSymbol not in self.legDict:
return
# 更新腿持仓
leg = self.legDict[pos.vtSymbol]
direction = pos.direction
if direction == DIRECTION_LONG:
leg.longPos = pos.position
else:
leg.shortPos = pos.position
leg.netPos = leg.longPos - leg.shortPos
# 更新价差持仓
spread = self.vtSymbolSpreadDict[pos.vtSymbol]
spread.calculatePos()
# 推送价差持仓更新
self.putSpreadPosEvent(spread)
#----------------------------------------------------------------------
def putSpreadPosEvent(self, spread):
"""发出价差持仓事件"""
event1 = Event(EVENT_SPREADTRADING_POS+spread.name)
event1.dict_['data'] = spread
self.eventEngine.put(event1)
event2 = Event(EVENT_SPREADTRADING_POS)
event2.dict_['data'] = spread
self.eventEngine.put(event2)
#----------------------------------------------------------------------
def registerEvent(self):
""""""
self.eventEngine.register(EVENT_TICK, self.processTickEvent)
self.eventEngine.register(EVENT_TRADE, self.processTradeEvent)
self.eventEngine.register(EVENT_POSITION, self.processPosEvent)
#----------------------------------------------------------------------
def subscribeMarketData(self, vtSymbol):
"""订阅行情"""
contract = self.mainEngine.getContract(vtSymbol)
if not contract:
self.writeLog(u'订阅行情失败，找不到该合约%s' %vtSymbol)
return
req = VtSubscribeReq()
req.symbol = contract.symbol
req.exchange = contract.exchange
self.mainEngine.subscribe(req, contract.gatewayName)
#----------------------------------------------------------------------
def writeLog(self, content):
"""发出日志"""
log = VtLogData()
log.logContent = content
event = Event(EVENT_SPREADTRADING_LOG)
event.dict_['data'] = log
self.eventEngine.put(event)
#----------------------------------------------------------------------
def getAllSpreads(self):
"""获取所有的价差"""
return self.spreadDict.values()
########################################################################
class StAlgoEngine(object):
"""价差算法交易引擎"""
algoFileName = 'SpreadTradingAlgo.vt'
algoFilePath = getTempPath(algoFileName)
#----------------------------------------------------------------------
def __init__(self, dataEngine, mainEngine, eventEngine):
"""Constructor"""
self.dataEngine = dataEngine
self.mainEngine = mainEngine
self.eventEngine = eventEngine
self.algoDict = {} # spreadName:algo
self.vtSymbolAlgoDict = {} # vtSymbol:algo
self.registerEvent()
#----------------------------------------------------------------------
def registerEvent(self):
"""注册事件监听"""
self.eventEngine.register(EVENT_SPREADTRADING_TICK, self.processSpreadTickEvent)
self.eventEngine.register(EVENT_SPREADTRADING_POS, self.processSpreadPosEvent)
self.eventEngine.register(EVENT_TRADE, self.processTradeEvent)
self.eventEngine.register(EVENT_ORDER, self.processOrderEvent)
self.eventEngine.register(EVENT_TIMER, self.processTimerEvent)
#----------------------------------------------------------------------
def processSpreadTickEvent(self, event):
"""处理价差行情事件"""
spread = event.dict_['data']
algo = self.algoDict.get(spread.name, None)
if algo:
algo.updateSpreadTick(spread)
#----------------------------------------------------------------------
def processSpreadPosEvent(self, event):
"""处理价差持仓事件"""
spread = event.dict_['data']
algo = self.algoDict.get(spread.name, None)
if algo:
algo.updateSpreadPos(spread)
#----------------------------------------------------------------------
def processTradeEvent(self, event):
"""处理成交事件"""
trade = event.dict_['data']
algo = self.vtSymbolAlgoDict.get(trade.vtSymbol, None)
if algo:
algo.updateTrade(trade)
#----------------------------------------------------------------------
def processOrderEvent(self, event):
"""处理委托事件"""
order = event.dict_['data']
algo = self.vtSymbolAlgoDict.get(order.vtSymbol, None)
if algo:
algo.updateOrder(order)
#----------------------------------------------------------------------
def processTimerEvent(self, event):
""""""
for algo in self.algoDict.values():
algo.updateTimer()
#----------------------------------------------------------------------
def sendOrder(self, vtSymbol, direction, offset, price, volume, payup=0):
"""发单"""
contract = self.mainEngine.getContract(vtSymbol)
if not contract:
return ''
req = VtOrderReq()
req.symbol = contract.symbol
req.exchange = contract.exchange
req.vtSymbol = contract.vtSymbol
req.direction = direction
req.offset = offset
req.volume = int(volume)
req.priceType = PRICETYPE_LIMITPRICE
if direction == DIRECTION_LONG:
req.price = price + payup * contract.priceTick
else:
req.price = price - payup * contract.priceTick
# 委托转换
reqList = self.mainEngine.convertOrderReq(req)
vtOrderIDList = []
for req in reqList:
vtOrderID = self.mainEngine.sendOrder(req, contract.gatewayName)
vtOrderIDList.append(vtOrderID)
return vtOrderIDList
#----------------------------------------------------------------------
def cancelOrder(self, vtOrderID):
"""撤单"""
order = self.mainEngine.getOrder(vtOrderID)
if not order:
return
req = VtCancelOrderReq()
req.symbol = order.symbol
req.exchange = order.exchange
req.frontID = order.frontID
req.sessionID = order.sessionID
req.orderID = order.orderID
self.mainEngine.cancelOrder(req, order.gatewayName)
#----------------------------------------------------------------------
def buy(self, vtSymbol, price, volume, payup=0):
"""买入"""
l = self.sendOrder(vtSymbol, DIRECTION_LONG, OFFSET_OPEN, price, volume, payup)
return l
#----------------------------------------------------------------------
def sell(self, vtSymbol, price, volume, payup=0):
"""卖出"""
l = self.sendOrder(vtSymbol, DIRECTION_SHORT, OFFSET_CLOSE, price, volume, payup)
return l
#----------------------------------------------------------------------
def short(self, vtSymbol, price, volume, payup=0):
"""卖空"""
l = self.sendOrder(vtSymbol, DIRECTION_SHORT, OFFSET_OPEN, price, volume, payup)
return l
#----------------------------------------------------------------------
def cover(self, vtSymbol, price, volume, payup=0):
"""平空"""
l = self.sendOrder(vtSymbol, DIRECTION_LONG, OFFSET_CLOSE, price, volume, payup)
return l
#----------------------------------------------------------------------
def putAlgoEvent(self, algo):
"""发出算法状态更新事件"""
event = Event(EVENT_SPREADTRADING_ALGO+algo.name)
self.eventEngine.put(event)
#----------------------------------------------------------------------
def writeLog(self, content):
"""输出日志"""
log = VtLogData()
log.logContent = content
event = Event(EVENT_SPREADTRADING_ALGOLOG)
event.dict_['data'] = log
self.eventEngine.put(event)
#----------------------------------------------------------------------
def saveSetting(self):
"""保存算法配置"""
setting = {}
for algo in self.algoDict.values():
setting[algo.spreadName] = algo.getAlgoParams()
f = shelve.open(self.algoFilePath)
f['setting'] = setting
f.close()
#----------------------------------------------------------------------
def loadSetting(self):
"""加载算法配置"""
# 创建算法对象
l = self.dataEngine.getAllSpreads()
for spread in l:
algo = SniperAlgo(self, spread)
self.algoDict[spread.name] = algo
# 保存腿代码和算法对象的映射
for leg in spread.allLegs:
self.vtSymbolAlgoDict[leg.vtSymbol] = algo
# 加载配置
f = shelve.open(self.algoFilePath)
setting = f.get('setting', None)
f.close()
if not setting:
return
for algo in self.algoDict.values():
if algo.spreadName in setting:
d = setting[algo.spreadName]
algo.setAlgoParams(d)
#----------------------------------------------------------------------
def stopAll(self):
"""停止全部算法"""
for algo in self.algoDict.values():
algo.stop()
#----------------------------------------------------------------------
def startAlgo(self, spreadName):
"""启动算法"""
algo = self.algoDict[spreadName]
algoActive = algo.start()
return algoActive
#----------------------------------------------------------------------
def stopAlgo(self, spreadName):
"""停止算法"""
algo = self.algoDict[spreadName]
algoActive = algo.stop()
return algoActive
#----------------------------------------------------------------------
def getAllAlgoParams(self):
"""获取所有算法的参数"""
return [algo.getAlgoParams() for algo in self.algoDict.values()]
#----------------------------------------------------------------------
def setAlgoBuyPrice(self, spreadName, buyPrice):
"""设置算法买开价格"""
algo = self.algoDict[spreadName]
algo.setBuyPrice(buyPrice)
#----------------------------------------------------------------------
def setAlgoSellPrice(self, spreadName, sellPrice):
"""设置算法卖平价格"""
algo = self.algoDict[spreadName]
algo.setSellPrice(sellPrice)
#----------------------------------------------------------------------
def setAlgoShortPrice(self, spreadName, shortPrice):
"""设置算法卖开价格"""
algo = self.algoDict[spreadName]
algo.setShortPrice(shortPrice)
#----------------------------------------------------------------------
def setAlgoCoverPrice(self, spreadName, coverPrice):
"""设置算法买平价格"""
algo = self.algoDict[spreadName]
algo.setCoverPrice(coverPrice)
#----------------------------------------------------------------------
def setAlgoMode(self, spreadName, mode):
"""设置算法工作模式"""
algo = self.algoDict[spreadName]
algo.setMode(mode)
#----------------------------------------------------------------------
def setAlgoMaxOrderSize(self, spreadName, maxOrderSize):
"""设置算法单笔委托限制"""
algo = self.algoDict[spreadName]
algo.setMaxOrderSize(maxOrderSize)
#----------------------------------------------------------------------
def setAlgoMaxPosSize(self, spreadName, maxPosSize):
"""设置算法持仓限制"""
algo = self.algoDict[spreadName]
algo.setMaxPosSize(maxPosSize)
########################################################################
class StEngine(object):
"""价差引擎"""
#----------------------------------------------------------------------
def __init__(self, mainEngine, eventEngine):
"""Constructor"""
self.mainEngine = mainEngine
self.eventEngine = eventEngine
self.dataEngine = StDataEngine(mainEngine, eventEngine)
self.algoEngine = StAlgoEngine(self.dataEngine, mainEngine, eventEngine)
#----------------------------------------------------------------------
def init(self):
"""初始化"""
self.dataEngine.loadSetting()
self.algoEngine.loadSetting()
#----------------------------------------------------------------------
def stop(self):
"""停止"""
self.dataEngine.saveSetting()
self.algoEngine.stopAll()
self.algoEngine.saveSetting()
| 34.805792 | 89 | 0.467623 |
import json
import traceback
import shelve
import parser
import re
from vnpy.event import Event
from vnpy.trader.vtFunction import getJsonPath, getTempPath
from vnpy.trader.vtEvent import (EVENT_TICK, EVENT_TRADE, EVENT_POSITION,
EVENT_TIMER, EVENT_ORDER)
from vnpy.trader.vtObject import (VtSubscribeReq, VtOrderReq,
VtCancelOrderReq, VtLogData)
from vnpy.trader.vtConstant import (DIRECTION_LONG, DIRECTION_SHORT,
OFFSET_OPEN, OFFSET_CLOSE,
PRICETYPE_LIMITPRICE)
from .stBase import (StLeg, StSpread, EVENT_SPREADTRADING_TICK,
EVENT_SPREADTRADING_POS, EVENT_SPREADTRADING_LOG,
EVENT_SPREADTRADING_ALGO, EVENT_SPREADTRADING_ALGOLOG)
from .stAlgo import SniperAlgo
ctiveLeg.legname = str(activeSetting['legname'])
spread.addActiveLeg(activeLeg)
self.legDict[activeLeg.vtSymbol] = activeLeg
self.vtSymbolSpreadDict[activeLeg.vtSymbol] = spread
self.subscribeMarketData(activeLeg.vtSymbol)
passiveSettingList = setting['passiveLegs']
passiveLegList = []
for d in passiveSettingList:
passiveLeg = StLeg()
passiveLeg.vtSymbol = str(d['vtSymbol'])
passiveLeg.ratio = float(d['ratio'])
passiveLeg.payup = int(d['payup'])
passiveLeg.legname = str(d['legname'])
spread.addPassiveLeg(passiveLeg)
self.legDict[passiveLeg.vtSymbol] = passiveLeg
self.vtSymbolSpreadDict[passiveLeg.vtSymbol] = spread
self.subscribeMarketData(passiveLeg.vtSymbol)
spread.initSpread()
self.putSpreadTickEvent(spread)
self.putSpreadPosEvent(spread)
result = True
msg = u'%s价差创建成功' %spread.name
return result, msg
def processTickEvent(self, event):
tick = event.dict_['data']
if tick.vtSymbol not in self.legDict:
return
leg = self.legDict[tick.vtSymbol]
leg.bidPrice = tick.bidPrice1
leg.askPrice = tick.askPrice1
leg.bidVolume = tick.bidVolume1
leg.askVolume = tick.askVolume1
spread = self.vtSymbolSpreadDict[tick.vtSymbol]
spread.calculatePrice()
self.putSpreadTickEvent(spread)
def putSpreadTickEvent(self, spread):
event1 = Event(EVENT_SPREADTRADING_TICK+spread.name)
event1.dict_['data'] = spread
self.eventEngine.put(event1)
event2 = Event(EVENT_SPREADTRADING_TICK)
event2.dict_['data'] = spread
self.eventEngine.put(event2)
def processTradeEvent(self, event):
trade = event.dict_['data']
if trade.vtSymbol not in self.legDict:
return
leg = self.legDict[trade.vtSymbol]
direction = trade.direction
offset = trade.offset
if direction == DIRECTION_LONG:
if offset == OFFSET_OPEN:
leg.longPos += trade.volume
else:
leg.shortPos -= trade.volume
else:
if offset == OFFSET_OPEN:
leg.shortPos += trade.volume
else:
leg.longPos -= trade.volume
leg.netPos = leg.longPos - leg.shortPos
spread = self.vtSymbolSpreadDict[trade.vtSymbol]
spread.calculatePos()
event1 = Event(EVENT_SPREADTRADING_POS+spread.name)
event1.dict_['data'] = spread
self.eventEngine.put(event1)
event2 = Event(EVENT_SPREADTRADING_POS)
event2.dict_['data'] = spread
self.eventEngine.put(event2)
def processPosEvent(self, event):
pos = event.dict_['data']
if pos.vtSymbol not in self.legDict:
return
leg = self.legDict[pos.vtSymbol]
direction = pos.direction
if direction == DIRECTION_LONG:
leg.longPos = pos.position
else:
leg.shortPos = pos.position
leg.netPos = leg.longPos - leg.shortPos
spread = self.vtSymbolSpreadDict[pos.vtSymbol]
spread.calculatePos()
self.putSpreadPosEvent(spread)
def putSpreadPosEvent(self, spread):
event1 = Event(EVENT_SPREADTRADING_POS+spread.name)
event1.dict_['data'] = spread
self.eventEngine.put(event1)
event2 = Event(EVENT_SPREADTRADING_POS)
event2.dict_['data'] = spread
self.eventEngine.put(event2)
def registerEvent(self):
self.eventEngine.register(EVENT_TICK, self.processTickEvent)
self.eventEngine.register(EVENT_TRADE, self.processTradeEvent)
self.eventEngine.register(EVENT_POSITION, self.processPosEvent)
def subscribeMarketData(self, vtSymbol):
contract = self.mainEngine.getContract(vtSymbol)
if not contract:
self.writeLog(u'订阅行情失败，找不到该合约%s' %vtSymbol)
return
req = VtSubscribeReq()
req.symbol = contract.symbol
req.exchange = contract.exchange
self.mainEngine.subscribe(req, contract.gatewayName)
def writeLog(self, content):
log = VtLogData()
log.logContent = content
event = Event(EVENT_SPREADTRADING_LOG)
event.dict_['data'] = log
self.eventEngine.put(event)
def getAllSpreads(self):
return self.spreadDict.values()
q)
vtOrderIDList = []
for req in reqList:
vtOrderID = self.mainEngine.sendOrder(req, contract.gatewayName)
vtOrderIDList.append(vtOrderID)
return vtOrderIDList
def cancelOrder(self, vtOrderID):
order = self.mainEngine.getOrder(vtOrderID)
if not order:
return
req = VtCancelOrderReq()
req.symbol = order.symbol
req.exchange = order.exchange
req.frontID = order.frontID
req.sessionID = order.sessionID
req.orderID = order.orderID
self.mainEngine.cancelOrder(req, order.gatewayName)
def buy(self, vtSymbol, price, volume, payup=0):
l = self.sendOrder(vtSymbol, DIRECTION_LONG, OFFSET_OPEN, price, volume, payup)
return l
def sell(self, vtSymbol, price, volume, payup=0):
l = self.sendOrder(vtSymbol, DIRECTION_SHORT, OFFSET_CLOSE, price, volume, payup)
return l
def short(self, vtSymbol, price, volume, payup=0):
l = self.sendOrder(vtSymbol, DIRECTION_SHORT, OFFSET_OPEN, price, volume, payup)
return l
def cover(self, vtSymbol, price, volume, payup=0):
l = self.sendOrder(vtSymbol, DIRECTION_LONG, OFFSET_CLOSE, price, volume, payup)
return l
def putAlgoEvent(self, algo):
event = Event(EVENT_SPREADTRADING_ALGO+algo.name)
self.eventEngine.put(event)
def writeLog(self, content):
log = VtLogData()
log.logContent = content
event = Event(EVENT_SPREADTRADING_ALGOLOG)
event.dict_['data'] = log
self.eventEngine.put(event)
def saveSetting(self):
setting = {}
for algo in self.algoDict.values():
setting[algo.spreadName] = algo.getAlgoParams()
f = shelve.open(self.algoFilePath)
f['setting'] = setting
f.close()
def loadSetting(self):
l = self.dataEngine.getAllSpreads()
for spread in l:
algo = SniperAlgo(self, spread)
self.algoDict[spread.name] = algo
for leg in spread.allLegs:
self.vtSymbolAlgoDict[leg.vtSymbol] = algo
f = shelve.open(self.algoFilePath)
setting = f.get('setting', None)
f.close()
if not setting:
return
for algo in self.algoDict.values():
if algo.spreadName in setting:
d = setting[algo.spreadName]
algo.setAlgoParams(d)
def stopAll(self):
for algo in self.algoDict.values():
algo.stop()
def startAlgo(self, spreadName):
algo = self.algoDict[spreadName]
algoActive = algo.start()
return algoActive
def stopAlgo(self, spreadName):
algo = self.algoDict[spreadName]
algoActive = algo.stop()
return algoActive
def getAllAlgoParams(self):
return [algo.getAlgoParams() for algo in self.algoDict.values()]
def setAlgoBuyPrice(self, spreadName, buyPrice):
algo = self.algoDict[spreadName]
algo.setBuyPrice(buyPrice)
def setAlgoSellPrice(self, spreadName, sellPrice):
algo = self.algoDict[spreadName]
algo.setSellPrice(sellPrice)
def setAlgoShortPrice(self, spreadName, shortPrice):
algo = self.algoDict[spreadName]
algo.setShortPrice(shortPrice)
def setAlgoCoverPrice(self, spreadName, coverPrice):
algo = self.algoDict[spreadName]
algo.setCoverPrice(coverPrice)
def setAlgoMode(self, spreadName, mode):
algo = self.algoDict[spreadName]
algo.setMode(mode)
def setAlgoMaxOrderSize(self, spreadName, maxOrderSize):
algo = self.algoDict[spreadName]
algo.setMaxOrderSize(maxOrderSize)
def setAlgoMaxPosSize(self, spreadName, maxPosSize):
algo = self.algoDict[spreadName]
algo.setMaxPosSize(maxPosSize)
| true | true |
f7257c269b0dd8b39aab1647dd5c4b9c17e4563f | 13,360 | py | Python | server/models/postgis/user.py | rustyb/osm-ireland-tasking-manager | 958c232ba50ca02e5adbc7541a4b9efa7186bfdc | [
"BSD-2-Clause"
] | null | null | null | server/models/postgis/user.py | rustyb/osm-ireland-tasking-manager | 958c232ba50ca02e5adbc7541a4b9efa7186bfdc | [
"BSD-2-Clause"
] | 4 | 2020-03-24T17:47:26.000Z | 2021-06-02T00:32:15.000Z | server/models/postgis/user.py | rustyb/osm-ireland-tasking-manager | 958c232ba50ca02e5adbc7541a4b9efa7186bfdc | [
"BSD-2-Clause"
] | 1 | 2021-01-30T20:12:18.000Z | 2021-01-30T20:12:18.000Z | import geojson
import datetime
import dateutil.parser
from server import db
from sqlalchemy import desc, text
from server.models.dtos.user_dto import UserDTO, UserMappedProjectsDTO, MappedProject, UserFilterDTO, Pagination, \
UserSearchQuery, UserSearchDTO, ProjectParticipantUser, ListedUser
from server.models.postgis.licenses import License, users_licenses_table
from server.models.postgis.project_info import ProjectInfo
from server.models.postgis.statuses import MappingLevel, ProjectStatus, UserRole
from server.models.postgis.utils import NotFound, timestamp
class User(db.Model):
""" Describes the history associated with a task """
__tablename__ = "users"
id = db.Column(db.BigInteger, primary_key=True, index=True)
validation_message = db.Column(db.Boolean, default=True, nullable=False)
username = db.Column(db.String, unique=True)
role = db.Column(db.Integer, default=0, nullable=False)
mapping_level = db.Column(db.Integer, default=1, nullable=False)
projects_mapped = db.Column(db.Integer, default=1, nullable=False)
tasks_mapped = db.Column(db.Integer, default=0, nullable=False)
tasks_validated = db.Column(db.Integer, default=0, nullable=False)
tasks_invalidated = db.Column(db.Integer, default=0, nullable=False)
projects_mapped = db.Column(db.ARRAY(db.Integer))
email_address = db.Column(db.String)
is_email_verified = db.Column(db.Boolean, default=False)
is_expert = db.Column(db.Boolean, default=False)
twitter_id = db.Column(db.String)
facebook_id = db.Column(db.String)
linkedin_id = db.Column(db.String)
date_registered = db.Column(db.DateTime, default=timestamp)
# Represents the date the user last had one of their tasks validated
last_validation_date = db.Column(db.DateTime, default=timestamp)
# Relationships
accepted_licenses = db.relationship("License", secondary=users_licenses_table)
def create(self):
""" Creates and saves the current model to the DB """
db.session.add(self)
db.session.commit()
def save(self):
db.session.commit()
def get_by_id(self, user_id: int):
""" Return the user for the specified id, or None if not found """
return User.query.get(user_id)
def get_by_username(self, username: str):
""" Return the user for the specified username, or None if not found """
return User.query.filter_by(username=username).one_or_none()
def update_username(self, username: str):
""" Update the username """
self.username = username
db.session.commit()
def update(self, user_dto: UserDTO):
""" Update the user details """
self.email_address = user_dto.email_address.lower() if user_dto.email_address else None
self.twitter_id = user_dto.twitter_id.lower() if user_dto.twitter_id else None
self.facebook_id = user_dto.facebook_id.lower() if user_dto.facebook_id else None
self.linkedin_id = user_dto.linkedin_id.lower() if user_dto.linkedin_id else None
self.validation_message = user_dto.validation_message
db.session.commit()
def set_email_verified_status(self, is_verified: bool):
""" Updates email verfied flag on successfully verified emails"""
self.is_email_verified = is_verified
db.session.commit()
def set_is_expert(self, is_expert: bool):
""" Enables or disables expert mode on the user"""
self.is_expert = is_expert
db.session.commit()
@staticmethod
def get_all_users(query: UserSearchQuery) -> UserSearchDTO:
""" Search and filter all users """
# Base query that applies to all searches
base = db.session.query(User.id, User.username, User.mapping_level, User.role)
# Add filter to query as required
if query.mapping_level:
base = base.filter(User.mapping_level == MappingLevel[query.mapping_level.upper()].value)
if query.username:
base = base.filter(User.username.ilike(query.username.lower() + '%'))
if query.role:
base = base.filter(User.role == UserRole[query.role.upper()].value)
results = base.order_by(User.username).paginate(query.page, 20, True)
dto = UserSearchDTO()
for result in results.items:
listed_user = ListedUser()
listed_user.id = result.id
listed_user.mapping_level = MappingLevel(result.mapping_level).name
listed_user.username = result.username
listed_user.role = UserRole(result.role).name
dto.users.append(listed_user)
dto.pagination = Pagination(results)
return dto
@staticmethod
def get_all_users_not_pagainated():
""" Get all users in DB"""
return db.session.query(User.id).all()
@staticmethod
def filter_users(user_filter: str, project_id: int, page: int,
is_project_manager:bool=False) -> UserFilterDTO:
""" Finds users that matches first characters, for auto-complete.
Users who have participated (mapped or validated) in the project, if given, will be
returned ahead of those who have not.
"""
# Note that the projects_mapped column includes both mapped and validated projects.
query = db.session.query(User.username, User.projects_mapped.any(project_id).label("participant")) \
.filter(User.username.ilike(user_filter.lower() + '%')) \
.order_by(desc("participant").nullslast(), User.username)
if is_project_manager:
query = query.filter(User.role.in_([UserRole.ADMIN.value, UserRole.PROJECT_MANAGER.value]))
results = query.paginate(page, 20, True)
if results.total == 0:
raise NotFound()
dto = UserFilterDTO()
for result in results.items:
dto.usernames.append(result.username)
if project_id is not None:
participant = ProjectParticipantUser()
participant.username = result.username
participant.project_id = project_id
participant.is_participant = bool(result.participant)
dto.users.append(participant)
dto.pagination = Pagination(results)
return dto
@staticmethod
def upsert_mapped_projects(user_id: int, project_id: int):
""" Adds projects to mapped_projects if it doesn't exist """
sql = "select * from users where id = :user_id and projects_mapped @> '{{:project_id}}'"
result = db.engine.execute(text(sql), user_id=user_id, project_id=project_id)
if result.rowcount > 0:
return # User has previously mapped this project so return
sql = '''update users
set projects_mapped = array_append(projects_mapped, :project_id)
where id = :user_id'''
db.engine.execute(text(sql), project_id=project_id, user_id=user_id)
@staticmethod
def get_mapped_projects(user_id: int, preferred_locale: str) -> UserMappedProjectsDTO:
""" Get all projects a user has mapped on """
# This query looks scary, but we're really just creating an outer join between the query that gets the
# counts of all mapped tasks and the query that gets counts of all validated tasks. This is necessary to
# handle cases where users have only validated tasks on a project, or only mapped on a project.
sql = '''SELECT p.id,
p.status,
p.default_locale,
c.mapped,
c.validated,
st_asgeojson(p.centroid)
FROM projects p,
(SELECT coalesce(v.project_id, m.project_id) project_id,
coalesce(v.validated, 0) validated,
coalesce(m.mapped, 0) mapped
FROM (SELECT t.project_id,
count (t.validated_by) validated
FROM tasks t
WHERE t.project_id IN (SELECT unnest(projects_mapped) FROM users WHERE id = :user_id)
AND t.validated_by = :user_id
GROUP BY t.project_id, t.validated_by) v
FULL OUTER JOIN
(SELECT t.project_id,
count(t.mapped_by) mapped
FROM tasks t
WHERE t.project_id IN (SELECT unnest(projects_mapped) FROM users WHERE id = :user_id)
AND t.mapped_by = :user_id
GROUP BY t.project_id, t.mapped_by) m
ON v.project_id = m.project_id) c
WHERE p.id = c.project_id ORDER BY p.id DESC'''
results = db.engine.execute(text(sql), user_id=user_id)
if results.rowcount == 0:
raise NotFound()
mapped_projects_dto = UserMappedProjectsDTO()
for row in results:
mapped_project = MappedProject()
mapped_project.project_id = row[0]
mapped_project.status = ProjectStatus(row[1]).name
mapped_project.tasks_mapped = row[3]
mapped_project.tasks_validated = row[4]
mapped_project.centroid = geojson.loads(row[5])
project_info = ProjectInfo.get_dto_for_locale(row[0], preferred_locale, row[2])
mapped_project.name = project_info.name
mapped_projects_dto.mapped_projects.append(mapped_project)
return mapped_projects_dto
def set_user_role(self, role: UserRole):
""" Sets the supplied role on the user """
self.role = role.value
db.session.commit()
def set_mapping_level(self, level: MappingLevel):
""" Sets the supplied level on the user """
self.mapping_level = level.value
db.session.commit()
def accept_license_terms(self, license_id: int):
""" Associate the user in scope with the supplied license """
image_license = License.get_by_id(license_id)
self.accepted_licenses.append(image_license)
db.session.commit()
def has_user_accepted_licence(self, license_id: int):
""" Test to see if the user has accepted the terms of the specified license"""
image_license = License.get_by_id(license_id)
if image_license in self.accepted_licenses:
return True
return False
def delete(self):
""" Delete the user in scope from DB """
db.session.delete(self)
db.session.commit()
def as_dto(self, logged_in_username: str) -> UserDTO:
""" Create DTO object from user in scope """
user_dto = UserDTO()
user_dto.id = self.id
user_dto.username = self.username
user_dto.role = UserRole(self.role).name
user_dto.mapping_level = MappingLevel(self.mapping_level).name
user_dto.is_expert = self.is_expert or False
user_dto.date_registered = str(self.date_registered)
try:
user_dto.projects_mapped = len(self.projects_mapped)
# Handle users that haven't touched a project yet.
except:
user_dto.projects_mapped = 0
user_dto.tasks_mapped = self.tasks_mapped
user_dto.tasks_validated = self.tasks_validated
user_dto.tasks_invalidated = self.tasks_invalidated
user_dto.twitter_id = self.twitter_id
user_dto.linkedin_id = self.linkedin_id
user_dto.facebook_id = self.facebook_id
user_dto.validation_message = self.validation_message
user_dto.total_time_spent = 0
user_dto.time_spent_mapping = 0
user_dto.time_spent_validating = 0
sql = """SELECT SUM(TO_TIMESTAMP(action_text, 'HH24:MI:SS')::TIME) FROM task_history
WHERE action='LOCKED_FOR_VALIDATION'
and user_id = :user_id;"""
total_validation_time = db.engine.execute(text(sql), user_id=self.id)
for row in total_validation_time:
total_validation_time = row[0]
if total_validation_time:
total_validation_seconds = total_validation_time.total_seconds()
user_dto.time_spent_validating = total_validation_seconds
user_dto.total_time_spent += user_dto.time_spent_validating
sql = """SELECT SUM(TO_TIMESTAMP(action_text, 'HH24:MI:SS')::TIME) FROM task_history
WHERE action='LOCKED_FOR_MAPPING'
and user_id = :user_id;"""
total_mapping_time = db.engine.execute(text(sql), user_id=self.id)
for row in total_mapping_time:
total_mapping_time = row[0]
if total_mapping_time:
total_mapping_seconds = total_mapping_time.total_seconds()
user_dto.time_spent_mapping = total_mapping_seconds
user_dto.total_time_spent += user_dto.time_spent_mapping
if self.username == logged_in_username:
# Only return email address when logged in user is looking at their own profile
user_dto.email_address = self.email_address
user_dto.is_email_verified = self.is_email_verified
return user_dto
| 44.385382 | 118 | 0.643114 | import geojson
import datetime
import dateutil.parser
from server import db
from sqlalchemy import desc, text
from server.models.dtos.user_dto import UserDTO, UserMappedProjectsDTO, MappedProject, UserFilterDTO, Pagination, \
UserSearchQuery, UserSearchDTO, ProjectParticipantUser, ListedUser
from server.models.postgis.licenses import License, users_licenses_table
from server.models.postgis.project_info import ProjectInfo
from server.models.postgis.statuses import MappingLevel, ProjectStatus, UserRole
from server.models.postgis.utils import NotFound, timestamp
class User(db.Model):
__tablename__ = "users"
id = db.Column(db.BigInteger, primary_key=True, index=True)
validation_message = db.Column(db.Boolean, default=True, nullable=False)
username = db.Column(db.String, unique=True)
role = db.Column(db.Integer, default=0, nullable=False)
mapping_level = db.Column(db.Integer, default=1, nullable=False)
projects_mapped = db.Column(db.Integer, default=1, nullable=False)
tasks_mapped = db.Column(db.Integer, default=0, nullable=False)
tasks_validated = db.Column(db.Integer, default=0, nullable=False)
tasks_invalidated = db.Column(db.Integer, default=0, nullable=False)
projects_mapped = db.Column(db.ARRAY(db.Integer))
email_address = db.Column(db.String)
is_email_verified = db.Column(db.Boolean, default=False)
is_expert = db.Column(db.Boolean, default=False)
twitter_id = db.Column(db.String)
facebook_id = db.Column(db.String)
linkedin_id = db.Column(db.String)
date_registered = db.Column(db.DateTime, default=timestamp)
last_validation_date = db.Column(db.DateTime, default=timestamp)
accepted_licenses = db.relationship("License", secondary=users_licenses_table)
def create(self):
db.session.add(self)
db.session.commit()
def save(self):
db.session.commit()
def get_by_id(self, user_id: int):
return User.query.get(user_id)
def get_by_username(self, username: str):
return User.query.filter_by(username=username).one_or_none()
def update_username(self, username: str):
self.username = username
db.session.commit()
def update(self, user_dto: UserDTO):
self.email_address = user_dto.email_address.lower() if user_dto.email_address else None
self.twitter_id = user_dto.twitter_id.lower() if user_dto.twitter_id else None
self.facebook_id = user_dto.facebook_id.lower() if user_dto.facebook_id else None
self.linkedin_id = user_dto.linkedin_id.lower() if user_dto.linkedin_id else None
self.validation_message = user_dto.validation_message
db.session.commit()
def set_email_verified_status(self, is_verified: bool):
self.is_email_verified = is_verified
db.session.commit()
def set_is_expert(self, is_expert: bool):
self.is_expert = is_expert
db.session.commit()
@staticmethod
def get_all_users(query: UserSearchQuery) -> UserSearchDTO:
base = db.session.query(User.id, User.username, User.mapping_level, User.role)
if query.mapping_level:
base = base.filter(User.mapping_level == MappingLevel[query.mapping_level.upper()].value)
if query.username:
base = base.filter(User.username.ilike(query.username.lower() + '%'))
if query.role:
base = base.filter(User.role == UserRole[query.role.upper()].value)
results = base.order_by(User.username).paginate(query.page, 20, True)
dto = UserSearchDTO()
for result in results.items:
listed_user = ListedUser()
listed_user.id = result.id
listed_user.mapping_level = MappingLevel(result.mapping_level).name
listed_user.username = result.username
listed_user.role = UserRole(result.role).name
dto.users.append(listed_user)
dto.pagination = Pagination(results)
return dto
@staticmethod
def get_all_users_not_pagainated():
return db.session.query(User.id).all()
@staticmethod
def filter_users(user_filter: str, project_id: int, page: int,
is_project_manager:bool=False) -> UserFilterDTO:
query = db.session.query(User.username, User.projects_mapped.any(project_id).label("participant")) \
.filter(User.username.ilike(user_filter.lower() + '%')) \
.order_by(desc("participant").nullslast(), User.username)
if is_project_manager:
query = query.filter(User.role.in_([UserRole.ADMIN.value, UserRole.PROJECT_MANAGER.value]))
results = query.paginate(page, 20, True)
if results.total == 0:
raise NotFound()
dto = UserFilterDTO()
for result in results.items:
dto.usernames.append(result.username)
if project_id is not None:
participant = ProjectParticipantUser()
participant.username = result.username
participant.project_id = project_id
participant.is_participant = bool(result.participant)
dto.users.append(participant)
dto.pagination = Pagination(results)
return dto
@staticmethod
def upsert_mapped_projects(user_id: int, project_id: int):
sql = "select * from users where id = :user_id and projects_mapped @> '{{:project_id}}'"
result = db.engine.execute(text(sql), user_id=user_id, project_id=project_id)
if result.rowcount > 0:
return
sql = '''update users
set projects_mapped = array_append(projects_mapped, :project_id)
where id = :user_id'''
db.engine.execute(text(sql), project_id=project_id, user_id=user_id)
@staticmethod
def get_mapped_projects(user_id: int, preferred_locale: str) -> UserMappedProjectsDTO:
# counts of all mapped tasks and the query that gets counts of all validated tasks. This is necessary to
# handle cases where users have only validated tasks on a project, or only mapped on a project.
sql = '''SELECT p.id,
p.status,
p.default_locale,
c.mapped,
c.validated,
st_asgeojson(p.centroid)
FROM projects p,
(SELECT coalesce(v.project_id, m.project_id) project_id,
coalesce(v.validated, 0) validated,
coalesce(m.mapped, 0) mapped
FROM (SELECT t.project_id,
count (t.validated_by) validated
FROM tasks t
WHERE t.project_id IN (SELECT unnest(projects_mapped) FROM users WHERE id = :user_id)
AND t.validated_by = :user_id
GROUP BY t.project_id, t.validated_by) v
FULL OUTER JOIN
(SELECT t.project_id,
count(t.mapped_by) mapped
FROM tasks t
WHERE t.project_id IN (SELECT unnest(projects_mapped) FROM users WHERE id = :user_id)
AND t.mapped_by = :user_id
GROUP BY t.project_id, t.mapped_by) m
ON v.project_id = m.project_id) c
WHERE p.id = c.project_id ORDER BY p.id DESC'''
results = db.engine.execute(text(sql), user_id=user_id)
if results.rowcount == 0:
raise NotFound()
mapped_projects_dto = UserMappedProjectsDTO()
for row in results:
mapped_project = MappedProject()
mapped_project.project_id = row[0]
mapped_project.status = ProjectStatus(row[1]).name
mapped_project.tasks_mapped = row[3]
mapped_project.tasks_validated = row[4]
mapped_project.centroid = geojson.loads(row[5])
project_info = ProjectInfo.get_dto_for_locale(row[0], preferred_locale, row[2])
mapped_project.name = project_info.name
mapped_projects_dto.mapped_projects.append(mapped_project)
return mapped_projects_dto
def set_user_role(self, role: UserRole):
self.role = role.value
db.session.commit()
def set_mapping_level(self, level: MappingLevel):
self.mapping_level = level.value
db.session.commit()
def accept_license_terms(self, license_id: int):
image_license = License.get_by_id(license_id)
self.accepted_licenses.append(image_license)
db.session.commit()
def has_user_accepted_licence(self, license_id: int):
image_license = License.get_by_id(license_id)
if image_license in self.accepted_licenses:
return True
return False
def delete(self):
db.session.delete(self)
db.session.commit()
def as_dto(self, logged_in_username: str) -> UserDTO:
user_dto = UserDTO()
user_dto.id = self.id
user_dto.username = self.username
user_dto.role = UserRole(self.role).name
user_dto.mapping_level = MappingLevel(self.mapping_level).name
user_dto.is_expert = self.is_expert or False
user_dto.date_registered = str(self.date_registered)
try:
user_dto.projects_mapped = len(self.projects_mapped)
# Handle users that haven't touched a project yet.
except:
user_dto.projects_mapped = 0
user_dto.tasks_mapped = self.tasks_mapped
user_dto.tasks_validated = self.tasks_validated
user_dto.tasks_invalidated = self.tasks_invalidated
user_dto.twitter_id = self.twitter_id
user_dto.linkedin_id = self.linkedin_id
user_dto.facebook_id = self.facebook_id
user_dto.validation_message = self.validation_message
user_dto.total_time_spent = 0
user_dto.time_spent_mapping = 0
user_dto.time_spent_validating = 0
sql = """SELECT SUM(TO_TIMESTAMP(action_text, 'HH24:MI:SS')::TIME) FROM task_history
WHERE action='LOCKED_FOR_VALIDATION'
and user_id = :user_id;"""
total_validation_time = db.engine.execute(text(sql), user_id=self.id)
for row in total_validation_time:
total_validation_time = row[0]
if total_validation_time:
total_validation_seconds = total_validation_time.total_seconds()
user_dto.time_spent_validating = total_validation_seconds
user_dto.total_time_spent += user_dto.time_spent_validating
sql = """SELECT SUM(TO_TIMESTAMP(action_text, 'HH24:MI:SS')::TIME) FROM task_history
WHERE action='LOCKED_FOR_MAPPING'
and user_id = :user_id;"""
total_mapping_time = db.engine.execute(text(sql), user_id=self.id)
for row in total_mapping_time:
total_mapping_time = row[0]
if total_mapping_time:
total_mapping_seconds = total_mapping_time.total_seconds()
user_dto.time_spent_mapping = total_mapping_seconds
user_dto.total_time_spent += user_dto.time_spent_mapping
if self.username == logged_in_username:
user_dto.email_address = self.email_address
user_dto.is_email_verified = self.is_email_verified
return user_dto
| true | true |
f7257d2547d1644fe9f677f8883223e1b992288c | 7,585 | py | Python | mmdet/models/detectors/maskformer.py | ayulockin/mmdetection | 6b87ac22b8d9dea8cc28b9ce84909e6c311e6268 | [
"Apache-2.0"
] | 2 | 2021-11-27T03:30:42.000Z | 2022-01-01T05:14:18.000Z | mmdet/models/detectors/maskformer.py | Bella-ing/mmdetection | 70f6d9cfade4a2f0b198e4f64776521d181b28be | [
"Apache-2.0"
] | 1 | 2020-05-20T08:13:44.000Z | 2020-05-20T08:13:44.000Z | mmdet/models/detectors/maskformer.py | Bella-ing/mmdetection | 70f6d9cfade4a2f0b198e4f64776521d181b28be | [
"Apache-2.0"
] | null | null | null | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
from mmdet.core import INSTANCE_OFFSET
from mmdet.core.visualization import imshow_det_bboxes
from ..builder import DETECTORS, build_backbone, build_head, build_neck
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class MaskFormer(SingleStageDetector):
r"""Implementation of `Per-Pixel Classification is
NOT All You Need for Semantic Segmentation
<https://arxiv.org/pdf/2107.06278>`_."""
def __init__(self,
backbone,
neck=None,
panoptic_head=None,
train_cfg=None,
test_cfg=None,
init_cfg=None):
super(SingleStageDetector, self).__init__(init_cfg=init_cfg)
self.backbone = build_backbone(backbone)
if neck is not None:
self.neck = build_neck(neck)
panoptic_head.update(train_cfg=train_cfg)
panoptic_head.update(test_cfg=test_cfg)
self.panoptic_head = build_head(panoptic_head)
self.num_things_classes = self.panoptic_head.num_things_classes
self.num_stuff_classes = self.panoptic_head.num_stuff_classes
self.num_classes = self.panoptic_head.num_classes
self.train_cfg = train_cfg
self.test_cfg = test_cfg
def forward_dummy(self, img, img_metas):
"""Used for computing network flops. See
`mmdetection/tools/analysis_tools/get_flops.py`
Args:
img (Tensor): of shape (N, C, H, W) encoding input images.
Typically these should be mean centered and std scaled.
img_metas (list[Dict]): list of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmdet/datasets/pipelines/formatting.py:Collect`.
"""
super(SingleStageDetector, self).forward_train(img, img_metas)
x = self.extract_feat(img)
outs = self.panoptic_head(x, img_metas)
return outs
def forward_train(self,
img,
img_metas,
gt_bboxes,
gt_labels,
gt_masks,
gt_semantic_seg,
gt_bboxes_ignore=None,
**kargs):
"""
Args:
img (Tensor): of shape (N, C, H, W) encoding input images.
Typically these should be mean centered and std scaled.
img_metas (list[Dict]): list of image info dict where each dict
has: 'img_shape', 'scale_factor', 'flip', and may also contain
'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
For details on the values of these keys see
`mmdet/datasets/pipelines/formatting.py:Collect`.
gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
gt_labels (list[Tensor]): class indices corresponding to each box.
gt_masks (list[BitmapMasks]): true segmentation masks for each box
used if the architecture supports a segmentation task.
gt_semantic_seg (list[tensor]): semantic segmentation mask for
images.
gt_bboxes_ignore (list[Tensor]): specify which bounding
boxes can be ignored when computing the loss.
Defaults to None.
Returns:
dict[str, Tensor]: a dictionary of loss components
"""
# add batch_input_shape in img_metas
super(SingleStageDetector, self).forward_train(img, img_metas)
x = self.extract_feat(img)
losses = self.panoptic_head.forward_train(x, img_metas, gt_bboxes,
gt_labels, gt_masks,
gt_semantic_seg,
gt_bboxes_ignore)
return losses
def simple_test(self, img, img_metas, **kwargs):
"""Test without augmentation."""
feat = self.extract_feat(img)
mask_results = self.panoptic_head.simple_test(feat, img_metas,
**kwargs)
results = []
for mask in mask_results:
result = {'pan_results': mask.detach().cpu().numpy()}
results.append(result)
return results
def aug_test(self, imgs, img_metas, **kwargs):
raise NotImplementedError
def onnx_export(self, img, img_metas):
raise NotImplementedError
def show_result(self,
img,
result,
score_thr=0.3,
bbox_color=(72, 101, 241),
text_color=(72, 101, 241),
mask_color=None,
thickness=2,
font_size=13,
win_name='',
show=False,
wait_time=0,
out_file=None):
"""Draw `result` over `img`.
Args:
img (str or Tensor): The image to be displayed.
result (dict): The results.
score_thr (float, optional): Minimum score of bboxes to be shown.
Default: 0.3.
bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
The tuple of color should be in BGR order. Default: 'green'.
text_color (str or tuple(int) or :obj:`Color`):Color of texts.
The tuple of color should be in BGR order. Default: 'green'.
mask_color (None or str or tuple(int) or :obj:`Color`):
Color of masks. The tuple of color should be in BGR order.
Default: None.
thickness (int): Thickness of lines. Default: 2.
font_size (int): Font size of texts. Default: 13.
win_name (str): The window name. Default: ''.
wait_time (float): Value of waitKey param.
Default: 0.
show (bool): Whether to show the image.
Default: False.
out_file (str or None): The filename to write the image.
Default: None.
Returns:
img (Tensor): Only if not `show` or `out_file`.
"""
img = mmcv.imread(img)
img = img.copy()
pan_results = result['pan_results']
# keep objects ahead
ids = np.unique(pan_results)[::-1]
legal_indices = ids != self.num_classes # for VOID label
ids = ids[legal_indices]
labels = np.array([id % INSTANCE_OFFSET for id in ids], dtype=np.int64)
segms = (pan_results[None] == ids[:, None, None])
# if out_file specified, do not show image in window
if out_file is not None:
show = False
# draw bounding boxes
img = imshow_det_bboxes(
img,
segms=segms,
labels=labels,
class_names=self.CLASSES,
bbox_color=bbox_color,
text_color=text_color,
mask_color=mask_color,
thickness=thickness,
font_size=font_size,
win_name=win_name,
show=show,
wait_time=wait_time,
out_file=out_file)
if not (show or out_file):
return img
| 39.921053 | 79 | 0.562162 |
import mmcv
import numpy as np
from mmdet.core import INSTANCE_OFFSET
from mmdet.core.visualization import imshow_det_bboxes
from ..builder import DETECTORS, build_backbone, build_head, build_neck
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class MaskFormer(SingleStageDetector):
def __init__(self,
backbone,
neck=None,
panoptic_head=None,
train_cfg=None,
test_cfg=None,
init_cfg=None):
super(SingleStageDetector, self).__init__(init_cfg=init_cfg)
self.backbone = build_backbone(backbone)
if neck is not None:
self.neck = build_neck(neck)
panoptic_head.update(train_cfg=train_cfg)
panoptic_head.update(test_cfg=test_cfg)
self.panoptic_head = build_head(panoptic_head)
self.num_things_classes = self.panoptic_head.num_things_classes
self.num_stuff_classes = self.panoptic_head.num_stuff_classes
self.num_classes = self.panoptic_head.num_classes
self.train_cfg = train_cfg
self.test_cfg = test_cfg
def forward_dummy(self, img, img_metas):
super(SingleStageDetector, self).forward_train(img, img_metas)
x = self.extract_feat(img)
outs = self.panoptic_head(x, img_metas)
return outs
def forward_train(self,
img,
img_metas,
gt_bboxes,
gt_labels,
gt_masks,
gt_semantic_seg,
gt_bboxes_ignore=None,
**kargs):
super(SingleStageDetector, self).forward_train(img, img_metas)
x = self.extract_feat(img)
losses = self.panoptic_head.forward_train(x, img_metas, gt_bboxes,
gt_labels, gt_masks,
gt_semantic_seg,
gt_bboxes_ignore)
return losses
def simple_test(self, img, img_metas, **kwargs):
feat = self.extract_feat(img)
mask_results = self.panoptic_head.simple_test(feat, img_metas,
**kwargs)
results = []
for mask in mask_results:
result = {'pan_results': mask.detach().cpu().numpy()}
results.append(result)
return results
def aug_test(self, imgs, img_metas, **kwargs):
raise NotImplementedError
def onnx_export(self, img, img_metas):
raise NotImplementedError
def show_result(self,
img,
result,
score_thr=0.3,
bbox_color=(72, 101, 241),
text_color=(72, 101, 241),
mask_color=None,
thickness=2,
font_size=13,
win_name='',
show=False,
wait_time=0,
out_file=None):
img = mmcv.imread(img)
img = img.copy()
pan_results = result['pan_results']
ids = np.unique(pan_results)[::-1]
legal_indices = ids != self.num_classes
ids = ids[legal_indices]
labels = np.array([id % INSTANCE_OFFSET for id in ids], dtype=np.int64)
segms = (pan_results[None] == ids[:, None, None])
if out_file is not None:
show = False
img = imshow_det_bboxes(
img,
segms=segms,
labels=labels,
class_names=self.CLASSES,
bbox_color=bbox_color,
text_color=text_color,
mask_color=mask_color,
thickness=thickness,
font_size=font_size,
win_name=win_name,
show=show,
wait_time=wait_time,
out_file=out_file)
if not (show or out_file):
return img
| true | true |
f7257da3da9f350d02208ba89525fe652376caa1 | 19,232 | py | Python | yaldevtools/source_code.py | libyal/libyal | 407e4710c9c11000dc45427d72bbdbdc2861c51a | [
"Apache-2.0"
] | 176 | 2015-01-11T01:57:37.000Z | 2022-03-30T05:31:33.000Z | yaldevtools/source_code.py | libyal/libyal | 407e4710c9c11000dc45427d72bbdbdc2861c51a | [
"Apache-2.0"
] | 79 | 2015-01-07T19:05:32.000Z | 2022-01-25T15:19:29.000Z | yaldevtools/source_code.py | libyal/libyal | 407e4710c9c11000dc45427d72bbdbdc2861c51a | [
"Apache-2.0"
] | 25 | 2015-07-16T13:29:00.000Z | 2022-02-12T08:15:19.000Z | # -*- coding: utf-8 -*-
"""The source code classes."""
import collections
from yaldevtools import definitions
class EnumDeclaration(object):
"""Enumeration type declaration.
Attributes:
name (str): name.
constants (dict[str, str]): constant values per name.
"""
def __init__(self, name):
"""Initializes an enumeration type declaration.
Args:
name (str): name.
"""
super(EnumDeclaration, self).__init__()
self.constants = collections.OrderedDict()
self.name = name
class FunctionArgument(object):
"""Function argument."""
def __init__(self, argument_string):
"""Initializes a function argument.
Args:
argument_string (str): function argument.
"""
super(FunctionArgument, self).__init__()
self._strings = [argument_string]
def AddArgumentString(self, argument_string):
"""Adds an argument string to the function argument.
Args:
argument_string (str): function argument.
"""
self._strings.append(argument_string)
def CopyToString(self):
"""Copies the function argument to a string.
Returns:
str: function argument.
"""
number_of_strings = len(self._strings)
argument_string = ''
if number_of_strings == 1:
argument_string = self._strings[0]
elif number_of_strings > 1:
argument_string = '{0:s}{1:s}'.format(
self._strings[0], ', '.join(self._strings[1:]))
return argument_string
class FunctionPrototype(object):
"""Function prototype.
Attributes:
arguments (list[FunctionArgument]): function arguments.
have_bfio (bool): True if the function prototype is defined if BFIO is
defined.
have_debug_output (bool): True if the function prototype is defined if
debug output is defined.
have_extern (bool): True if the function prototype is defined as
externally available (API).
have_wide_character_type (bool): True if the function prototype is
defined if the wide character type is defined.
name (str): name.
return_type (str): return type.
return_values (set[str]): return values or None if the function does not
return values.
value_description (str): description of the value.
"""
def __init__(self, name, return_type):
"""Initializes a function prototype.
Args:
name (str): name.
return_type (str): return type.
"""
super(FunctionPrototype, self).__init__()
self._parsed_value = False
self._value_name = None
self._value_type = None
self.arguments = []
self.have_bfio = False
self.have_debug_output = False
self.have_extern = False
self.have_wide_character_type = False
self.name = name
self.return_type = return_type
self.return_values = None
self.value_description = None
def AddArgument(self, argument):
"""Adds an argument to the function prototype.
Args:
argument (FunctionArgument): function argument.
"""
self.arguments.append(argument)
def AddArgumentString(self, argument_string):
"""Adds an argument string to the function prototype.
Args:
argument_string (str): function argument.
"""
function_argument = FunctionArgument(argument_string)
self.arguments.append(function_argument)
def CopyToManpageString(self):
"""Copies the function prototype to a string to be used in manpage.
Returns:
str: function prototype to be used in manpage.
"""
argument_strings = []
for function_argument in self.arguments:
argument_string = function_argument.CopyToString()
argument_string = '"{0:s}"'.format(argument_string)
argument_strings.append(argument_string)
return ' '.join(argument_strings)
def CopyToString(self):
"""Copies the function prototype to a string.
Returns:
str: function prototype.
"""
argument_strings = []
for function_argument in self.arguments:
argument_string = function_argument.CopyToString()
argument_strings.append(argument_string)
return ', '.join(argument_strings)
def _ParseValue(self):
"""Parses the value name and type."""
# Strip the library name.
_, _, function_name = self.name.partition('_')
# Strip the library type.
_, _, function_name = function_name.partition('_')
value_name = None
value_type = None
number_of_arguments = len(self.arguments)
if function_name.startswith('get_utf'):
if number_of_arguments in (3, 4):
_, _, value_name = function_name.partition('_')
_, _, value_name = value_name.partition('_')
elif function_name.startswith('get_'):
# TODO: handle by_index, by_path getters
if number_of_arguments == 3:
_, _, value_name = function_name.partition('_')
self._parsed_value = True
self._value_name = value_name
self._value_type = value_type
def GetValueName(self):
"""Determines the value name of a getter or setter function.
Returns:
str: value name or None if not available.
"""
if not self._parsed_value:
self._ParseValue()
return self._value_name
def GetValueType(self):
"""Determines the value type of a getter or setter function.
Returns:
str: value type or None if not available.
"""
if not self._parsed_value:
self._ParseValue()
return self._value_type
class PythonTypeObjectFunctionPrototype(object):
"""Python type object function prototype.
Attributes:
arguments (list[str]): arguments.
data_type (str): data type.
function_type (str): function type.
object_type (str): object type.
return_values (set[str]): return values or None if the function does not
return values.
value_description (str): description of the value.
value_type (str): value type.
"""
def __init__(self, python_module_name, type_name, type_function):
"""Initializes a Python type object function prototype.
Args:
python_module_name (str): python module name.
type_name (str): type name.
type_function (str): type function.
"""
super(PythonTypeObjectFunctionPrototype, self).__init__()
self._name = None
self._python_module_name = python_module_name
self._type_function = type_function
self._type_name = type_name
self._value_name = None
self.arguments = []
self.data_type = definitions.DATA_TYPE_NONE
self.function_type = None
self.object_type = None
self.return_values = None
self.value_description = None
self.value_type = None
@property
def name(self):
"""str: name."""
if self._name is None:
self._name = '{0:s}_{1:s}_{2:s}'.format(
self._python_module_name, self._type_name, self.type_function)
return self._name
@property
def type_function(self):
"""str: type function."""
# TODO: make overrides more generic.
if self._type_function == 'set_parent_file':
return 'set_parent'
if (self._type_function.startswith('copy_') and
not self._type_function.startswith('copy_from_')):
return 'get_{0:s}'.format(self._type_function[5:])
if (self._type_function.startswith('get_utf8_') or
self._type_function.startswith('set_utf8_')):
return ''.join([self._type_function[:4], self._type_function[9:]])
if self._type_function.startswith('get_data_as_'):
_, _, type_function_suffix = self._type_function.partition('_data_as_')
if type_function_suffix in (
'16bit_integer', '32bit_integer', '64bit_integer'):
return 'get_data_as_integer'
if type_function_suffix in ('filetime', 'floatingtime'):
return 'get_data_as_datetime'
if type_function_suffix == 'utf8_string':
return 'get_data_as_string'
return self._type_function
if self._type_function.startswith('get_'):
type_function_prefix, _, type_function_suffix = (
self._type_function.partition('_by_'))
if type_function_suffix in ('entry', 'index'):
return type_function_prefix
if type_function_suffix in ('utf8_name', 'utf8_path'):
return ''.join([self._type_function[:-10], self._type_function[-5:]])
if self._type_function.endswith('_utf8_string'):
return ''.join([self._type_function[:-12], self._type_function[-7:]])
if self._type_function.endswith('_utf8_string_size'):
return ''.join([self._type_function[:-17], self._type_function[-12:]])
return self._type_function
@property
def value_name(self):
"""str: value name."""
if self._value_name is None:
# TODO: make overrides more generic.
if self.function_type == definitions.FUNCTION_TYPE_COPY:
if self._type_function.startswith('copy_'):
self._value_name = self._type_function[5:]
elif self.function_type == definitions.FUNCTION_TYPE_COPY_FROM:
if self._type_function.startswith('copy_from_'):
self._value_name = self._type_function[10:]
elif self.function_type == definitions.FUNCTION_TYPE_COPY_TO:
if self._type_function.startswith('get_'):
self._value_name = self._type_function[4:]
elif self.function_type in (
definitions.FUNCTION_TYPE_GET,
definitions.FUNCTION_TYPE_GET_BY_IDENTIFIER,
definitions.FUNCTION_TYPE_GET_BY_INDEX,
definitions.FUNCTION_TYPE_GET_BY_NAME,
definitions.FUNCTION_TYPE_GET_BY_PATH):
type_function_prefix, _, _ = self._type_function.partition('_by_')
if type_function_prefix.startswith('get_'):
type_function_prefix = type_function_prefix[4:]
if type_function_prefix.startswith('utf8_'):
type_function_prefix = type_function_prefix[5:]
self._value_name = type_function_prefix
elif self.function_type == definitions.FUNCTION_TYPE_IS:
if self._type_function.startswith('is_'):
self._value_name = self._type_function[3:]
elif self.function_type == definitions.FUNCTION_TYPE_SET:
if self._type_function.startswith('set_utf8_'):
self._value_name = self._type_function[9:]
elif self._type_function.startswith('set_'):
self._value_name = self._type_function[4:]
return self._value_name
def DataTypeIsDatetime(self):
"""Determines if the data type is a datetime type.
Returns:
bool: True if the data type is a datetime type.
"""
return self.data_type in (
definitions.DATA_TYPE_FAT_DATE_TIME,
definitions.DATA_TYPE_FILETIME,
definitions.DATA_TYPE_FLOATINGTIME,
definitions.DATA_TYPE_POSIX_TIME)
def DataTypeIsFloat(self):
"""Determines if the data type is a floating-point type.
Returns:
bool: True if the data type is a floating-point type.
"""
return self.data_type in (
definitions.DATA_TYPE_FLOAT,
definitions.DATA_TYPE_DOUBLE)
def DataTypeIsInteger(self):
"""Determines if the data type is an integer type.
Returns:
bool: True if the data type is an integer type.
"""
return self.data_type in (
definitions.DATA_TYPE_INT,
definitions.DATA_TYPE_INT32,
definitions.DATA_TYPE_OFF64,
definitions.DATA_TYPE_SIZE32,
definitions.DATA_TYPE_SIZE64,
definitions.DATA_TYPE_UINT8,
definitions.DATA_TYPE_UINT16,
definitions.DATA_TYPE_UINT32,
definitions.DATA_TYPE_UINT64)
def GetAttributeDescription(self):
"""Retrieves the fuction as attribute description.
Returns:
str: function as attribute description.
"""
description = ''
type_function = self.type_function
value_name = self.value_name
if value_name:
value_name = value_name.replace('_', ' ')
if type_function == 'get_ascii_codepage':
description = (
'The codepage used for ASCII strings in the {0:s}.').format(
self._type_name)
elif type_function == 'get_data_as_boolean':
description = 'The data as a boolean.'
elif type_function == 'get_data_as_datetime':
description = 'The data as a datetime object.'
elif type_function == 'get_data_as_integer':
description = 'The data as an integer.'
elif type_function == 'get_data_as_floating_point':
description = 'The data as a floating point.'
elif type_function == 'get_data_as_string':
description = 'The data as a string.'
elif self.function_type == definitions.FUNCTION_TYPE_IS:
type_name = self._type_name
if type_name:
type_name = type_name.replace('_', ' ')
description = 'Indicates the {0:s} is {1:s}.'.format(
type_name, value_name)
elif self.value_description:
description = 'The {0:s}.'.format(self.value_description)
elif value_name:
description = 'The {0:s}.'.format(value_name)
return description
def GetDataTypeDescription(self):
"""Retrieves the data type description.
Returns:
str: data type description.
"""
if self.data_type == definitions.DATA_TYPE_BINARY_DATA:
data_type_description = 'Binary string'
elif self.data_type == definitions.DATA_TYPE_BOOLEAN:
data_type_description = 'Boolean'
elif self.DataTypeIsDatetime():
data_type_description = 'Datetime'
elif self.data_type == definitions.DATA_TYPE_OBJECT:
data_type_description = 'Object'
elif self.DataTypeIsFloat():
data_type_description = 'Float'
elif self.DataTypeIsInteger():
data_type_description = 'Integer'
elif self.data_type in (
definitions.DATA_TYPE_GUID,
definitions.DATA_TYPE_STRING,
definitions.DATA_TYPE_UUID):
data_type_description = 'Unicode string'
elif self.data_type == definitions.DATA_TYPE_NARROW_STRING:
data_type_description = 'String'
elif self.data_type == definitions.DATA_TYPE_NONE:
data_type_description = 'None'
else:
data_type_description = self.data_type
if (data_type_description != 'None' and self.return_values and
'None' in self.return_values):
data_type_description = '{0:s} or None'.format(data_type_description)
return data_type_description
def GetDescription(self):
"""Retrieves the description.
Returns:
list[str]: lines of the description.
"""
description = ['']
type_function = self.type_function
type_name = self._type_name
if type_name:
type_name = type_name.replace('_', ' ')
value_name = self.value_name
if value_name:
value_name = value_name.replace('_', ' ')
if type_function == 'close':
description = ['Closes a {0:s}.'.format(type_name)]
elif type_function == 'get_ascii_codepage':
description = [(
'Retrieves the codepage for ASCII strings used in '
'the {0:s}.').format(type_name)]
elif type_function == 'get_data_as_boolean':
description = ['Retrieves the data as a boolean.']
elif type_function == 'get_data_as_datetime':
description = ['Retrieves the data as a datetime object.']
elif type_function == 'get_data_as_integer':
description = ['Retrieves the data as an integer.']
elif type_function == 'get_data_as_floating_point':
description = ['Retrieves the data as a floating point.']
elif type_function == 'get_data_as_string':
description = ['Retrieves the data as a string.']
elif type_function == 'get_string':
description = ['Retrieves the {0:s} formatted as a string.'.format(
type_name)]
elif type_function == 'open':
description = ['Opens a {0:s}.'.format(type_name)]
elif type_function == 'open_file_object':
description = [(
'Opens a {0:s} using a file-like object.').format(type_name)]
elif type_function == 'read_buffer':
if self.value_description:
description = ['Reads a buffer of {0:s}.'.format(
self.value_description)]
else:
description = ['Reads a buffer of data.']
elif type_function == 'read_buffer_at_offset':
if self.value_description:
description = ['Reads a buffer of {0:s} at a specific offset.'.format(
self.value_description)]
else:
description = ['Reads a buffer of data at a specific offset.']
elif type_function == 'seek_offset':
if self.value_description:
description = ['Seeks an offset within the {0:s}.'.format(
self.value_description)]
else:
description = ['Seeks an offset within the data.']
elif type_function == 'set_ascii_codepage':
description = [
('Sets the codepage for ASCII strings used in the '
'{0:s}.').format(type_name),
('Expects the codepage to be a string containing a Python '
'codec definition.')]
elif type_function == 'set_parent':
description = ['Sets the parent file.']
elif type_function == 'signal_abort':
description = ['Signals the {0:s} to abort the current activity.'.format(
type_name)]
elif self.function_type == definitions.FUNCTION_TYPE_GET_BY_INDEX:
_, _, argument_suffix = self.arguments[0].rpartition('_')
if self.value_description:
description = ['Retrieves the {0:s} specified by the {1:s}.'.format(
self.value_description, argument_suffix)]
else:
description = ['Retrieves the {0:s} specified by the {1:s}.'.format(
value_name, argument_suffix)]
elif self.function_type in (
definitions.FUNCTION_TYPE_GET_BY_IDENTIFIER,
definitions.FUNCTION_TYPE_GET_BY_NAME,
definitions.FUNCTION_TYPE_GET_BY_PATH):
_, _, type_function_suffix = type_function.partition('_by_')
if self.value_description:
description = ['Retrieves the {0:s} specified by the {1:s}.'.format(
self.value_description, type_function_suffix)]
else:
description = ['Retrieves the {0:s} specified by the {1:s}.'.format(
value_name, type_function_suffix)]
elif self.function_type == definitions.FUNCTION_TYPE_COPY_FROM:
# TODO: fix value name.
description = ['Copies the {0:s} from the {1:s}.'.format(
type_name, value_name)]
elif self.function_type in (
definitions.FUNCTION_TYPE_COPY, definitions.FUNCTION_TYPE_GET):
if self.value_description:
description = ['Retrieves the {0:s}.'.format(self.value_description)]
else:
description = ['Retrieves the {0:s}.'.format(value_name)]
elif self.function_type == definitions.FUNCTION_TYPE_IS:
description = ['Determines if the {0:s} is {1:s}.'.format(
type_name, value_name)]
elif self.function_type == definitions.FUNCTION_TYPE_SET:
description = ['Sets the {0:s}.'.format(value_name)]
return description
def GetValueNameAndPrefix(self):
"""Determines the value name and its prefix.
Returns:
tuple[str, str]: value name and prefix.
"""
if self.value_name:
value_name_prefix, _, value_name = self.value_name.partition('_')
if value_name_prefix in ('root', 'sub'):
return value_name, value_name_prefix
return self.value_name, None
| 31.271545 | 79 | 0.674085 |
import collections
from yaldevtools import definitions
class EnumDeclaration(object):
def __init__(self, name):
super(EnumDeclaration, self).__init__()
self.constants = collections.OrderedDict()
self.name = name
class FunctionArgument(object):
def __init__(self, argument_string):
super(FunctionArgument, self).__init__()
self._strings = [argument_string]
def AddArgumentString(self, argument_string):
self._strings.append(argument_string)
def CopyToString(self):
number_of_strings = len(self._strings)
argument_string = ''
if number_of_strings == 1:
argument_string = self._strings[0]
elif number_of_strings > 1:
argument_string = '{0:s}{1:s}'.format(
self._strings[0], ', '.join(self._strings[1:]))
return argument_string
class FunctionPrototype(object):
def __init__(self, name, return_type):
super(FunctionPrototype, self).__init__()
self._parsed_value = False
self._value_name = None
self._value_type = None
self.arguments = []
self.have_bfio = False
self.have_debug_output = False
self.have_extern = False
self.have_wide_character_type = False
self.name = name
self.return_type = return_type
self.return_values = None
self.value_description = None
def AddArgument(self, argument):
self.arguments.append(argument)
def AddArgumentString(self, argument_string):
function_argument = FunctionArgument(argument_string)
self.arguments.append(function_argument)
def CopyToManpageString(self):
argument_strings = []
for function_argument in self.arguments:
argument_string = function_argument.CopyToString()
argument_string = '"{0:s}"'.format(argument_string)
argument_strings.append(argument_string)
return ' '.join(argument_strings)
def CopyToString(self):
argument_strings = []
for function_argument in self.arguments:
argument_string = function_argument.CopyToString()
argument_strings.append(argument_string)
return ', '.join(argument_strings)
def _ParseValue(self):
_, _, function_name = self.name.partition('_')
_, _, function_name = function_name.partition('_')
value_name = None
value_type = None
number_of_arguments = len(self.arguments)
if function_name.startswith('get_utf'):
if number_of_arguments in (3, 4):
_, _, value_name = function_name.partition('_')
_, _, value_name = value_name.partition('_')
elif function_name.startswith('get_'):
if number_of_arguments == 3:
_, _, value_name = function_name.partition('_')
self._parsed_value = True
self._value_name = value_name
self._value_type = value_type
def GetValueName(self):
if not self._parsed_value:
self._ParseValue()
return self._value_name
def GetValueType(self):
if not self._parsed_value:
self._ParseValue()
return self._value_type
class PythonTypeObjectFunctionPrototype(object):
def __init__(self, python_module_name, type_name, type_function):
super(PythonTypeObjectFunctionPrototype, self).__init__()
self._name = None
self._python_module_name = python_module_name
self._type_function = type_function
self._type_name = type_name
self._value_name = None
self.arguments = []
self.data_type = definitions.DATA_TYPE_NONE
self.function_type = None
self.object_type = None
self.return_values = None
self.value_description = None
self.value_type = None
@property
def name(self):
if self._name is None:
self._name = '{0:s}_{1:s}_{2:s}'.format(
self._python_module_name, self._type_name, self.type_function)
return self._name
@property
def type_function(self):
if self._type_function == 'set_parent_file':
return 'set_parent'
if (self._type_function.startswith('copy_') and
not self._type_function.startswith('copy_from_')):
return 'get_{0:s}'.format(self._type_function[5:])
if (self._type_function.startswith('get_utf8_') or
self._type_function.startswith('set_utf8_')):
return ''.join([self._type_function[:4], self._type_function[9:]])
if self._type_function.startswith('get_data_as_'):
_, _, type_function_suffix = self._type_function.partition('_data_as_')
if type_function_suffix in (
'16bit_integer', '32bit_integer', '64bit_integer'):
return 'get_data_as_integer'
if type_function_suffix in ('filetime', 'floatingtime'):
return 'get_data_as_datetime'
if type_function_suffix == 'utf8_string':
return 'get_data_as_string'
return self._type_function
if self._type_function.startswith('get_'):
type_function_prefix, _, type_function_suffix = (
self._type_function.partition('_by_'))
if type_function_suffix in ('entry', 'index'):
return type_function_prefix
if type_function_suffix in ('utf8_name', 'utf8_path'):
return ''.join([self._type_function[:-10], self._type_function[-5:]])
if self._type_function.endswith('_utf8_string'):
return ''.join([self._type_function[:-12], self._type_function[-7:]])
if self._type_function.endswith('_utf8_string_size'):
return ''.join([self._type_function[:-17], self._type_function[-12:]])
return self._type_function
@property
def value_name(self):
if self._value_name is None:
if self.function_type == definitions.FUNCTION_TYPE_COPY:
if self._type_function.startswith('copy_'):
self._value_name = self._type_function[5:]
elif self.function_type == definitions.FUNCTION_TYPE_COPY_FROM:
if self._type_function.startswith('copy_from_'):
self._value_name = self._type_function[10:]
elif self.function_type == definitions.FUNCTION_TYPE_COPY_TO:
if self._type_function.startswith('get_'):
self._value_name = self._type_function[4:]
elif self.function_type in (
definitions.FUNCTION_TYPE_GET,
definitions.FUNCTION_TYPE_GET_BY_IDENTIFIER,
definitions.FUNCTION_TYPE_GET_BY_INDEX,
definitions.FUNCTION_TYPE_GET_BY_NAME,
definitions.FUNCTION_TYPE_GET_BY_PATH):
type_function_prefix, _, _ = self._type_function.partition('_by_')
if type_function_prefix.startswith('get_'):
type_function_prefix = type_function_prefix[4:]
if type_function_prefix.startswith('utf8_'):
type_function_prefix = type_function_prefix[5:]
self._value_name = type_function_prefix
elif self.function_type == definitions.FUNCTION_TYPE_IS:
if self._type_function.startswith('is_'):
self._value_name = self._type_function[3:]
elif self.function_type == definitions.FUNCTION_TYPE_SET:
if self._type_function.startswith('set_utf8_'):
self._value_name = self._type_function[9:]
elif self._type_function.startswith('set_'):
self._value_name = self._type_function[4:]
return self._value_name
def DataTypeIsDatetime(self):
return self.data_type in (
definitions.DATA_TYPE_FAT_DATE_TIME,
definitions.DATA_TYPE_FILETIME,
definitions.DATA_TYPE_FLOATINGTIME,
definitions.DATA_TYPE_POSIX_TIME)
def DataTypeIsFloat(self):
return self.data_type in (
definitions.DATA_TYPE_FLOAT,
definitions.DATA_TYPE_DOUBLE)
def DataTypeIsInteger(self):
return self.data_type in (
definitions.DATA_TYPE_INT,
definitions.DATA_TYPE_INT32,
definitions.DATA_TYPE_OFF64,
definitions.DATA_TYPE_SIZE32,
definitions.DATA_TYPE_SIZE64,
definitions.DATA_TYPE_UINT8,
definitions.DATA_TYPE_UINT16,
definitions.DATA_TYPE_UINT32,
definitions.DATA_TYPE_UINT64)
def GetAttributeDescription(self):
description = ''
type_function = self.type_function
value_name = self.value_name
if value_name:
value_name = value_name.replace('_', ' ')
if type_function == 'get_ascii_codepage':
description = (
'The codepage used for ASCII strings in the {0:s}.').format(
self._type_name)
elif type_function == 'get_data_as_boolean':
description = 'The data as a boolean.'
elif type_function == 'get_data_as_datetime':
description = 'The data as a datetime object.'
elif type_function == 'get_data_as_integer':
description = 'The data as an integer.'
elif type_function == 'get_data_as_floating_point':
description = 'The data as a floating point.'
elif type_function == 'get_data_as_string':
description = 'The data as a string.'
elif self.function_type == definitions.FUNCTION_TYPE_IS:
type_name = self._type_name
if type_name:
type_name = type_name.replace('_', ' ')
description = 'Indicates the {0:s} is {1:s}.'.format(
type_name, value_name)
elif self.value_description:
description = 'The {0:s}.'.format(self.value_description)
elif value_name:
description = 'The {0:s}.'.format(value_name)
return description
def GetDataTypeDescription(self):
if self.data_type == definitions.DATA_TYPE_BINARY_DATA:
data_type_description = 'Binary string'
elif self.data_type == definitions.DATA_TYPE_BOOLEAN:
data_type_description = 'Boolean'
elif self.DataTypeIsDatetime():
data_type_description = 'Datetime'
elif self.data_type == definitions.DATA_TYPE_OBJECT:
data_type_description = 'Object'
elif self.DataTypeIsFloat():
data_type_description = 'Float'
elif self.DataTypeIsInteger():
data_type_description = 'Integer'
elif self.data_type in (
definitions.DATA_TYPE_GUID,
definitions.DATA_TYPE_STRING,
definitions.DATA_TYPE_UUID):
data_type_description = 'Unicode string'
elif self.data_type == definitions.DATA_TYPE_NARROW_STRING:
data_type_description = 'String'
elif self.data_type == definitions.DATA_TYPE_NONE:
data_type_description = 'None'
else:
data_type_description = self.data_type
if (data_type_description != 'None' and self.return_values and
'None' in self.return_values):
data_type_description = '{0:s} or None'.format(data_type_description)
return data_type_description
def GetDescription(self):
description = ['']
type_function = self.type_function
type_name = self._type_name
if type_name:
type_name = type_name.replace('_', ' ')
value_name = self.value_name
if value_name:
value_name = value_name.replace('_', ' ')
if type_function == 'close':
description = ['Closes a {0:s}.'.format(type_name)]
elif type_function == 'get_ascii_codepage':
description = [(
'Retrieves the codepage for ASCII strings used in '
'the {0:s}.').format(type_name)]
elif type_function == 'get_data_as_boolean':
description = ['Retrieves the data as a boolean.']
elif type_function == 'get_data_as_datetime':
description = ['Retrieves the data as a datetime object.']
elif type_function == 'get_data_as_integer':
description = ['Retrieves the data as an integer.']
elif type_function == 'get_data_as_floating_point':
description = ['Retrieves the data as a floating point.']
elif type_function == 'get_data_as_string':
description = ['Retrieves the data as a string.']
elif type_function == 'get_string':
description = ['Retrieves the {0:s} formatted as a string.'.format(
type_name)]
elif type_function == 'open':
description = ['Opens a {0:s}.'.format(type_name)]
elif type_function == 'open_file_object':
description = [(
'Opens a {0:s} using a file-like object.').format(type_name)]
elif type_function == 'read_buffer':
if self.value_description:
description = ['Reads a buffer of {0:s}.'.format(
self.value_description)]
else:
description = ['Reads a buffer of data.']
elif type_function == 'read_buffer_at_offset':
if self.value_description:
description = ['Reads a buffer of {0:s} at a specific offset.'.format(
self.value_description)]
else:
description = ['Reads a buffer of data at a specific offset.']
elif type_function == 'seek_offset':
if self.value_description:
description = ['Seeks an offset within the {0:s}.'.format(
self.value_description)]
else:
description = ['Seeks an offset within the data.']
elif type_function == 'set_ascii_codepage':
description = [
('Sets the codepage for ASCII strings used in the '
'{0:s}.').format(type_name),
('Expects the codepage to be a string containing a Python '
'codec definition.')]
elif type_function == 'set_parent':
description = ['Sets the parent file.']
elif type_function == 'signal_abort':
description = ['Signals the {0:s} to abort the current activity.'.format(
type_name)]
elif self.function_type == definitions.FUNCTION_TYPE_GET_BY_INDEX:
_, _, argument_suffix = self.arguments[0].rpartition('_')
if self.value_description:
description = ['Retrieves the {0:s} specified by the {1:s}.'.format(
self.value_description, argument_suffix)]
else:
description = ['Retrieves the {0:s} specified by the {1:s}.'.format(
value_name, argument_suffix)]
elif self.function_type in (
definitions.FUNCTION_TYPE_GET_BY_IDENTIFIER,
definitions.FUNCTION_TYPE_GET_BY_NAME,
definitions.FUNCTION_TYPE_GET_BY_PATH):
_, _, type_function_suffix = type_function.partition('_by_')
if self.value_description:
description = ['Retrieves the {0:s} specified by the {1:s}.'.format(
self.value_description, type_function_suffix)]
else:
description = ['Retrieves the {0:s} specified by the {1:s}.'.format(
value_name, type_function_suffix)]
elif self.function_type == definitions.FUNCTION_TYPE_COPY_FROM:
description = ['Copies the {0:s} from the {1:s}.'.format(
type_name, value_name)]
elif self.function_type in (
definitions.FUNCTION_TYPE_COPY, definitions.FUNCTION_TYPE_GET):
if self.value_description:
description = ['Retrieves the {0:s}.'.format(self.value_description)]
else:
description = ['Retrieves the {0:s}.'.format(value_name)]
elif self.function_type == definitions.FUNCTION_TYPE_IS:
description = ['Determines if the {0:s} is {1:s}.'.format(
type_name, value_name)]
elif self.function_type == definitions.FUNCTION_TYPE_SET:
description = ['Sets the {0:s}.'.format(value_name)]
return description
def GetValueNameAndPrefix(self):
if self.value_name:
value_name_prefix, _, value_name = self.value_name.partition('_')
if value_name_prefix in ('root', 'sub'):
return value_name, value_name_prefix
return self.value_name, None
| true | true |
f7257dbe457fadac40393d1ec3dd31766bbf6237 | 540 | py | Python | sample_app/admin.py | imimran/inline-in-fieldset | c20568904011889001d92024c8881782a84aa00c | [
"MIT"
] | null | null | null | sample_app/admin.py | imimran/inline-in-fieldset | c20568904011889001d92024c8881782a84aa00c | [
"MIT"
] | null | null | null | sample_app/admin.py | imimran/inline-in-fieldset | c20568904011889001d92024c8881782a84aa00c | [
"MIT"
] | null | null | null | from django.contrib import admin
from .models import Student, Subject
class SubjectInline(admin.TabularInline):
model = Subject
insert_after = 'name'
class StudentAdmin(admin.ModelAdmin):
fields = (
'name',
'department',
'gender',
)
inlines = [
SubjectInline,
]
change_form_template = 'admin/custom/change_form.html'
class Media:
css = {
'all': (
'css/admin.css',
)
}
admin.site.register(Student, StudentAdmin)
| 16.875 | 58 | 0.572222 | from django.contrib import admin
from .models import Student, Subject
class SubjectInline(admin.TabularInline):
model = Subject
insert_after = 'name'
class StudentAdmin(admin.ModelAdmin):
fields = (
'name',
'department',
'gender',
)
inlines = [
SubjectInline,
]
change_form_template = 'admin/custom/change_form.html'
class Media:
css = {
'all': (
'css/admin.css',
)
}
admin.site.register(Student, StudentAdmin)
| true | true |
f7257df7d21d92286f4cc0d13478da8406845b2f | 26,886 | py | Python | zipline/data/resample.py | Code37/zipline | de038dbf584980af4f30822f8e5d306bac2a44cb | [
"Apache-2.0"
] | 412 | 2017-04-30T14:35:47.000Z | 2022-03-29T02:58:33.000Z | zipline/data/resample.py | waijay1992/zipline | 8beba055aa4211dc2debc5c3083077cbd19d0bbc | [
"Apache-2.0"
] | 116 | 2017-05-15T04:45:45.000Z | 2020-05-30T19:09:00.000Z | zipline/data/resample.py | waijay1992/zipline | 8beba055aa4211dc2debc5c3083077cbd19d0bbc | [
"Apache-2.0"
] | 80 | 2017-05-03T13:17:33.000Z | 2021-02-08T15:42:09.000Z | # Copyright 2016 Quantopian, Inc.
#
# 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 collections import OrderedDict
from abc import ABCMeta, abstractmethod
import numpy as np
import pandas as pd
from six import with_metaclass
from zipline.data._resample import (
_minute_to_session_open,
_minute_to_session_high,
_minute_to_session_low,
_minute_to_session_close,
_minute_to_session_volume,
)
from zipline.data.bar_reader import NoDataOnDate
from zipline.data.minute_bars import MinuteBarReader
from zipline.data.session_bars import SessionBarReader
from zipline.utils.memoize import lazyval
_MINUTE_TO_SESSION_OHCLV_HOW = OrderedDict((
('open', 'first'),
('high', 'max'),
('low', 'min'),
('close', 'last'),
('volume', 'sum'),
))
def minute_frame_to_session_frame(minute_frame, calendar):
"""
Resample a DataFrame with minute data into the frame expected by a
BcolzDailyBarWriter.
Parameters
----------
minute_frame : pd.DataFrame
A DataFrame with the columns `open`, `high`, `low`, `close`, `volume`,
and `dt` (minute dts)
calendar : zipline.utils.calendars.trading_calendar.TradingCalendar
A TradingCalendar on which session labels to resample from minute
to session.
Return
------
session_frame : pd.DataFrame
A DataFrame with the columns `open`, `high`, `low`, `close`, `volume`,
and `day` (datetime-like).
"""
how = OrderedDict((c, _MINUTE_TO_SESSION_OHCLV_HOW[c])
for c in minute_frame.columns)
return minute_frame.groupby(calendar.minute_to_session_label).agg(how)
def minute_to_session(column, close_locs, data, out):
"""
Resample an array with minute data into an array with session data.
This function assumes that the minute data is the exact length of all
minutes in the sessions in the output.
Parameters
----------
column : str
The `open`, `high`, `low`, `close`, or `volume` column.
close_locs : array[intp]
The locations in `data` which are the market close minutes.
data : array[float64|uint32]
The minute data to be sampled into session data.
The first value should align with the market open of the first session,
containing values for all minutes for all sessions. With the last value
being the market close of the last session.
out : array[float64|uint32]
The output array into which to write the sampled sessions.
"""
if column == 'open':
_minute_to_session_open(close_locs, data, out)
elif column == 'high':
_minute_to_session_high(close_locs, data, out)
elif column == 'low':
_minute_to_session_low(close_locs, data, out)
elif column == 'close':
_minute_to_session_close(close_locs, data, out)
elif column == 'volume':
_minute_to_session_volume(close_locs, data, out)
return out
class DailyHistoryAggregator(object):
"""
Converts minute pricing data into a daily summary, to be used for the
last slot in a call to history with a frequency of `1d`.
This summary is the same as a daily bar rollup of minute data, with the
distinction that the summary is truncated to the `dt` requested.
i.e. the aggregation slides forward during a the course of simulation day.
Provides aggregation for `open`, `high`, `low`, `close`, and `volume`.
The aggregation rules for each price type is documented in their respective
"""
def __init__(self, market_opens, minute_reader, trading_calendar):
self._market_opens = market_opens
self._minute_reader = minute_reader
self._trading_calendar = trading_calendar
# The caches are structured as (date, market_open, entries), where
# entries is a dict of asset -> (last_visited_dt, value)
#
# Whenever an aggregation method determines the current value,
# the entry for the respective asset should be overwritten with a new
# entry for the current dt.value (int) and aggregation value.
#
# When the requested dt's date is different from date the cache is
# flushed, so that the cache entries do not grow unbounded.
#
# Example cache:
# cache = (date(2016, 3, 17),
# pd.Timestamp('2016-03-17 13:31', tz='UTC'),
# {
# 1: (1458221460000000000, np.nan),
# 2: (1458221460000000000, 42.0),
# })
self._caches = {
'open': None,
'high': None,
'low': None,
'close': None,
'volume': None
}
# The int value is used for deltas to avoid extra computation from
# creating new Timestamps.
self._one_min = pd.Timedelta('1 min').value
def _prelude(self, dt, field):
session = self._trading_calendar.minute_to_session_label(dt)
dt_value = dt.value
cache = self._caches[field]
if cache is None or cache[0] != session:
market_open = self._market_opens.loc[session]
cache = self._caches[field] = (session, market_open, {})
_, market_open, entries = cache
market_open = market_open.tz_localize('UTC')
if dt != market_open:
prev_dt = dt_value - self._one_min
else:
prev_dt = None
return market_open, prev_dt, dt_value, entries
def opens(self, assets, dt):
"""
The open field's aggregation returns the first value that occurs
for the day, if there has been no data on or before the `dt` the open
is `nan`.
Once the first non-nan open is seen, that value remains constant per
asset for the remainder of the day.
Returns
-------
np.array with dtype=float64, in order of assets parameter.
"""
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'open')
opens = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
opens.append(np.NaN)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'open')
entries[asset] = (dt_value, val)
opens.append(val)
continue
else:
try:
last_visited_dt, first_open = entries[asset]
if last_visited_dt == dt_value:
opens.append(first_open)
continue
elif not pd.isnull(first_open):
opens.append(first_open)
entries[asset] = (dt_value, first_open)
continue
else:
after_last = pd.Timestamp(
last_visited_dt + self._one_min, tz='UTC')
window = self._minute_reader.load_raw_arrays(
['open'],
after_last,
dt,
[asset],
)[0]
nonnan = window[~pd.isnull(window)]
if len(nonnan):
val = nonnan[0]
else:
val = np.nan
entries[asset] = (dt_value, val)
opens.append(val)
continue
except KeyError:
window = self._minute_reader.load_raw_arrays(
['open'],
market_open,
dt,
[asset],
)[0]
nonnan = window[~pd.isnull(window)]
if len(nonnan):
val = nonnan[0]
else:
val = np.nan
entries[asset] = (dt_value, val)
opens.append(val)
continue
return np.array(opens)
def highs(self, assets, dt):
"""
The high field's aggregation returns the largest high seen between
the market open and the current dt.
If there has been no data on or before the `dt` the high is `nan`.
Returns
-------
np.array with dtype=float64, in order of assets parameter.
"""
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'high')
highs = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
highs.append(np.NaN)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'high')
entries[asset] = (dt_value, val)
highs.append(val)
continue
else:
try:
last_visited_dt, last_max = entries[asset]
if last_visited_dt == dt_value:
highs.append(last_max)
continue
elif last_visited_dt == prev_dt:
curr_val = self._minute_reader.get_value(
asset, dt, 'high')
if pd.isnull(curr_val):
val = last_max
elif pd.isnull(last_max):
val = curr_val
else:
val = max(last_max, curr_val)
entries[asset] = (dt_value, val)
highs.append(val)
continue
else:
after_last = pd.Timestamp(
last_visited_dt + self._one_min, tz='UTC')
window = self._minute_reader.load_raw_arrays(
['high'],
after_last,
dt,
[asset],
)[0].T
val = np.nanmax(np.append(window, last_max))
entries[asset] = (dt_value, val)
highs.append(val)
continue
except KeyError:
window = self._minute_reader.load_raw_arrays(
['high'],
market_open,
dt,
[asset],
)[0].T
val = np.nanmax(window)
entries[asset] = (dt_value, val)
highs.append(val)
continue
return np.array(highs)
def lows(self, assets, dt):
"""
The low field's aggregation returns the smallest low seen between
the market open and the current dt.
If there has been no data on or before the `dt` the low is `nan`.
Returns
-------
np.array with dtype=float64, in order of assets parameter.
"""
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'low')
lows = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
lows.append(np.NaN)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'low')
entries[asset] = (dt_value, val)
lows.append(val)
continue
else:
try:
last_visited_dt, last_min = entries[asset]
if last_visited_dt == dt_value:
lows.append(last_min)
continue
elif last_visited_dt == prev_dt:
curr_val = self._minute_reader.get_value(
asset, dt, 'low')
val = np.nanmin([last_min, curr_val])
entries[asset] = (dt_value, val)
lows.append(val)
continue
else:
after_last = pd.Timestamp(
last_visited_dt + self._one_min, tz='UTC')
window = self._minute_reader.load_raw_arrays(
['low'],
after_last,
dt,
[asset],
)[0].T
val = np.nanmin(np.append(window, last_min))
entries[asset] = (dt_value, val)
lows.append(val)
continue
except KeyError:
window = self._minute_reader.load_raw_arrays(
['low'],
market_open,
dt,
[asset],
)[0].T
val = np.nanmin(window)
entries[asset] = (dt_value, val)
lows.append(val)
continue
return np.array(lows)
def closes(self, assets, dt):
"""
The close field's aggregation returns the latest close at the given
dt.
If the close for the given dt is `nan`, the most recent non-nan
`close` is used.
If there has been no data on or before the `dt` the close is `nan`.
Returns
-------
np.array with dtype=float64, in order of assets parameter.
"""
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'close')
closes = []
session_label = self._trading_calendar.minute_to_session_label(dt)
def _get_filled_close(asset):
"""
Returns the most recent non-nan close for the asset in this
session. If there has been no data in this session on or before the
`dt`, returns `nan`
"""
window = self._minute_reader.load_raw_arrays(
['close'],
market_open,
dt,
[asset],
)[0]
try:
return window[~np.isnan(window)][-1]
except IndexError:
return np.NaN
for asset in assets:
if not asset.is_alive_for_session(session_label):
closes.append(np.NaN)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'close')
entries[asset] = (dt_value, val)
closes.append(val)
continue
else:
try:
last_visited_dt, last_close = entries[asset]
if last_visited_dt == dt_value:
closes.append(last_close)
continue
elif last_visited_dt == prev_dt:
val = self._minute_reader.get_value(
asset, dt, 'close')
if pd.isnull(val):
val = last_close
entries[asset] = (dt_value, val)
closes.append(val)
continue
else:
val = self._minute_reader.get_value(
asset, dt, 'close')
if pd.isnull(val):
val = _get_filled_close(asset)
entries[asset] = (dt_value, val)
closes.append(val)
continue
except KeyError:
val = self._minute_reader.get_value(
asset, dt, 'close')
if pd.isnull(val):
val = _get_filled_close(asset)
entries[asset] = (dt_value, val)
closes.append(val)
continue
return np.array(closes)
def volumes(self, assets, dt):
"""
The volume field's aggregation returns the sum of all volumes
between the market open and the `dt`
If there has been no data on or before the `dt` the volume is 0.
Returns
-------
np.array with dtype=int64, in order of assets parameter.
"""
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'volume')
volumes = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
volumes.append(0)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'volume')
entries[asset] = (dt_value, val)
volumes.append(val)
continue
else:
try:
last_visited_dt, last_total = entries[asset]
if last_visited_dt == dt_value:
volumes.append(last_total)
continue
elif last_visited_dt == prev_dt:
val = self._minute_reader.get_value(
asset, dt, 'volume')
val += last_total
entries[asset] = (dt_value, val)
volumes.append(val)
continue
else:
after_last = pd.Timestamp(
last_visited_dt + self._one_min, tz='UTC')
window = self._minute_reader.load_raw_arrays(
['volume'],
after_last,
dt,
[asset],
)[0]
val = np.nansum(window) + last_total
entries[asset] = (dt_value, val)
volumes.append(val)
continue
except KeyError:
window = self._minute_reader.load_raw_arrays(
['volume'],
market_open,
dt,
[asset],
)[0]
val = np.nansum(window)
entries[asset] = (dt_value, val)
volumes.append(val)
continue
return np.array(volumes)
class MinuteResampleSessionBarReader(SessionBarReader):
def __init__(self, calendar, minute_bar_reader):
self._calendar = calendar
self._minute_bar_reader = minute_bar_reader
def _get_resampled(self, columns, start_session, end_session, assets):
range_open = self._calendar.session_open(start_session)
range_close = self._calendar.session_close(end_session)
minute_data = self._minute_bar_reader.load_raw_arrays(
columns,
range_open,
range_close,
assets,
)
# Get the index of the close minute for each session in the range.
# If the range contains only one session, the only close in the range
# is the last minute in the data. Otherwise, we need to get all the
# session closes and find their indices in the range of minutes.
if start_session == end_session:
close_ilocs = np.array([len(minute_data[0]) - 1], dtype=np.int64)
else:
minutes = self._calendar.minutes_in_range(
range_open,
range_close,
)
session_closes = self._calendar.session_closes_in_range(
start_session,
end_session,
)
close_ilocs = minutes.searchsorted(session_closes.values)
results = []
shape = (len(close_ilocs), len(assets))
for col in columns:
if col != 'volume':
out = np.full(shape, np.nan)
else:
out = np.zeros(shape, dtype=np.uint32)
results.append(out)
for i in range(len(assets)):
for j, column in enumerate(columns):
data = minute_data[j][:, i]
minute_to_session(column, close_ilocs, data, results[j][:, i])
return results
@property
def trading_calendar(self):
return self._calendar
def load_raw_arrays(self, columns, start_dt, end_dt, sids):
return self._get_resampled(columns, start_dt, end_dt, sids)
def get_value(self, sid, session, colname):
# WARNING: This will need caching or other optimization if used in a
# tight loop.
# This was developed to complete interface, but has not been tuned
# for real world use.
return self._get_resampled([colname], session, session, [sid])[0][0][0]
@lazyval
def sessions(self):
cal = self._calendar
first = self._minute_bar_reader.first_trading_day
last = cal.minute_to_session_label(
self._minute_bar_reader.last_available_dt)
return cal.sessions_in_range(first, last)
@lazyval
def last_available_dt(self):
return self.trading_calendar.minute_to_session_label(
self._minute_bar_reader.last_available_dt
)
@property
def first_trading_day(self):
return self._minute_bar_reader.first_trading_day
def get_last_traded_dt(self, asset, dt):
return self.trading_calendar.minute_to_session_label(
self._minute_bar_reader.get_last_traded_dt(asset, dt))
class ReindexBarReader(with_metaclass(ABCMeta)):
"""
A base class for readers which reindexes results, filling in the additional
indices with empty data.
Used to align the reading assets which trade on different calendars.
Currently only supports a ``trading_calendar`` which is a superset of the
``reader``'s calendar.
Parameters
----------
- trading_calendar : zipline.utils.trading_calendar.TradingCalendar
The calendar to use when indexing results from the reader.
- reader : MinuteBarReader|SessionBarReader
The reader which has a calendar that is a subset of the desired
``trading_calendar``.
- first_trading_session : pd.Timestamp
The first trading session the reader should provide. Must be specified,
since the ``reader``'s first session may not exactly align with the
desired calendar. Specifically, in the case where the first session
on the target calendar is a holiday on the ``reader``'s calendar.
- last_trading_session : pd.Timestamp
The last trading session the reader should provide. Must be specified,
since the ``reader``'s last session may not exactly align with the
desired calendar. Specifically, in the case where the last session
on the target calendar is a holiday on the ``reader``'s calendar.
"""
def __init__(self,
trading_calendar,
reader,
first_trading_session,
last_trading_session):
self._trading_calendar = trading_calendar
self._reader = reader
self._first_trading_session = first_trading_session
self._last_trading_session = last_trading_session
@property
def last_available_dt(self):
return self._reader.last_available_dt
def get_last_traded_dt(self, sid, dt):
return self._reader.get_last_traded_dt(sid, dt)
@property
def first_trading_day(self):
return self._reader.first_trading_day
def get_value(self, sid, dt, field):
# Give an empty result if no data is present.
try:
return self._reader.get_value(sid, dt, field)
except NoDataOnDate:
if field == 'volume':
return 0
else:
return np.nan
@abstractmethod
def _outer_dts(self, start_dt, end_dt):
raise NotImplementedError
@abstractmethod
def _inner_dts(self, start_dt, end_dt):
raise NotImplementedError
@property
def trading_calendar(self):
return self._trading_calendar
@lazyval
def sessions(self):
return self.trading_calendar.sessions_in_range(
self._first_trading_session,
self._last_trading_session
)
def load_raw_arrays(self, fields, start_dt, end_dt, sids):
outer_dts = self._outer_dts(start_dt, end_dt)
inner_dts = self._inner_dts(start_dt, end_dt)
indices = outer_dts.searchsorted(inner_dts)
shape = len(outer_dts), len(sids)
outer_results = []
if len(inner_dts) > 0:
inner_results = self._reader.load_raw_arrays(
fields, inner_dts[0], inner_dts[-1], sids)
else:
inner_results = None
for i, field in enumerate(fields):
if field != 'volume':
out = np.full(shape, np.nan)
else:
out = np.zeros(shape, dtype=np.uint32)
if inner_results is not None:
out[indices] = inner_results[i]
outer_results.append(out)
return outer_results
class ReindexMinuteBarReader(ReindexBarReader, MinuteBarReader):
"""
See: ``ReindexBarReader``
"""
def _outer_dts(self, start_dt, end_dt):
return self._trading_calendar.minutes_in_range(start_dt, end_dt)
def _inner_dts(self, start_dt, end_dt):
return self._reader.calendar.minutes_in_range(start_dt, end_dt)
class ReindexSessionBarReader(ReindexBarReader, SessionBarReader):
"""
See: ``ReindexBarReader``
"""
def _outer_dts(self, start_dt, end_dt):
return self.trading_calendar.sessions_in_range(start_dt, end_dt)
def _inner_dts(self, start_dt, end_dt):
return self._reader.trading_calendar.sessions_in_range(
start_dt, end_dt)
| 36.6794 | 79 | 0.542699 |
from collections import OrderedDict
from abc import ABCMeta, abstractmethod
import numpy as np
import pandas as pd
from six import with_metaclass
from zipline.data._resample import (
_minute_to_session_open,
_minute_to_session_high,
_minute_to_session_low,
_minute_to_session_close,
_minute_to_session_volume,
)
from zipline.data.bar_reader import NoDataOnDate
from zipline.data.minute_bars import MinuteBarReader
from zipline.data.session_bars import SessionBarReader
from zipline.utils.memoize import lazyval
_MINUTE_TO_SESSION_OHCLV_HOW = OrderedDict((
('open', 'first'),
('high', 'max'),
('low', 'min'),
('close', 'last'),
('volume', 'sum'),
))
def minute_frame_to_session_frame(minute_frame, calendar):
how = OrderedDict((c, _MINUTE_TO_SESSION_OHCLV_HOW[c])
for c in minute_frame.columns)
return minute_frame.groupby(calendar.minute_to_session_label).agg(how)
def minute_to_session(column, close_locs, data, out):
if column == 'open':
_minute_to_session_open(close_locs, data, out)
elif column == 'high':
_minute_to_session_high(close_locs, data, out)
elif column == 'low':
_minute_to_session_low(close_locs, data, out)
elif column == 'close':
_minute_to_session_close(close_locs, data, out)
elif column == 'volume':
_minute_to_session_volume(close_locs, data, out)
return out
class DailyHistoryAggregator(object):
def __init__(self, market_opens, minute_reader, trading_calendar):
self._market_opens = market_opens
self._minute_reader = minute_reader
self._trading_calendar = trading_calendar
# flushed, so that the cache entries do not grow unbounded.
#
# Example cache:
# cache = (date(2016, 3, 17),
# pd.Timestamp('2016-03-17 13:31', tz='UTC'),
# {
# 1: (1458221460000000000, np.nan),
# 2: (1458221460000000000, 42.0),
# })
self._caches = {
'open': None,
'high': None,
'low': None,
'close': None,
'volume': None
}
# The int value is used for deltas to avoid extra computation from
# creating new Timestamps.
self._one_min = pd.Timedelta('1 min').value
def _prelude(self, dt, field):
session = self._trading_calendar.minute_to_session_label(dt)
dt_value = dt.value
cache = self._caches[field]
if cache is None or cache[0] != session:
market_open = self._market_opens.loc[session]
cache = self._caches[field] = (session, market_open, {})
_, market_open, entries = cache
market_open = market_open.tz_localize('UTC')
if dt != market_open:
prev_dt = dt_value - self._one_min
else:
prev_dt = None
return market_open, prev_dt, dt_value, entries
def opens(self, assets, dt):
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'open')
opens = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
opens.append(np.NaN)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'open')
entries[asset] = (dt_value, val)
opens.append(val)
continue
else:
try:
last_visited_dt, first_open = entries[asset]
if last_visited_dt == dt_value:
opens.append(first_open)
continue
elif not pd.isnull(first_open):
opens.append(first_open)
entries[asset] = (dt_value, first_open)
continue
else:
after_last = pd.Timestamp(
last_visited_dt + self._one_min, tz='UTC')
window = self._minute_reader.load_raw_arrays(
['open'],
after_last,
dt,
[asset],
)[0]
nonnan = window[~pd.isnull(window)]
if len(nonnan):
val = nonnan[0]
else:
val = np.nan
entries[asset] = (dt_value, val)
opens.append(val)
continue
except KeyError:
window = self._minute_reader.load_raw_arrays(
['open'],
market_open,
dt,
[asset],
)[0]
nonnan = window[~pd.isnull(window)]
if len(nonnan):
val = nonnan[0]
else:
val = np.nan
entries[asset] = (dt_value, val)
opens.append(val)
continue
return np.array(opens)
def highs(self, assets, dt):
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'high')
highs = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
highs.append(np.NaN)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'high')
entries[asset] = (dt_value, val)
highs.append(val)
continue
else:
try:
last_visited_dt, last_max = entries[asset]
if last_visited_dt == dt_value:
highs.append(last_max)
continue
elif last_visited_dt == prev_dt:
curr_val = self._minute_reader.get_value(
asset, dt, 'high')
if pd.isnull(curr_val):
val = last_max
elif pd.isnull(last_max):
val = curr_val
else:
val = max(last_max, curr_val)
entries[asset] = (dt_value, val)
highs.append(val)
continue
else:
after_last = pd.Timestamp(
last_visited_dt + self._one_min, tz='UTC')
window = self._minute_reader.load_raw_arrays(
['high'],
after_last,
dt,
[asset],
)[0].T
val = np.nanmax(np.append(window, last_max))
entries[asset] = (dt_value, val)
highs.append(val)
continue
except KeyError:
window = self._minute_reader.load_raw_arrays(
['high'],
market_open,
dt,
[asset],
)[0].T
val = np.nanmax(window)
entries[asset] = (dt_value, val)
highs.append(val)
continue
return np.array(highs)
def lows(self, assets, dt):
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'low')
lows = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
lows.append(np.NaN)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'low')
entries[asset] = (dt_value, val)
lows.append(val)
continue
else:
try:
last_visited_dt, last_min = entries[asset]
if last_visited_dt == dt_value:
lows.append(last_min)
continue
elif last_visited_dt == prev_dt:
curr_val = self._minute_reader.get_value(
asset, dt, 'low')
val = np.nanmin([last_min, curr_val])
entries[asset] = (dt_value, val)
lows.append(val)
continue
else:
after_last = pd.Timestamp(
last_visited_dt + self._one_min, tz='UTC')
window = self._minute_reader.load_raw_arrays(
['low'],
after_last,
dt,
[asset],
)[0].T
val = np.nanmin(np.append(window, last_min))
entries[asset] = (dt_value, val)
lows.append(val)
continue
except KeyError:
window = self._minute_reader.load_raw_arrays(
['low'],
market_open,
dt,
[asset],
)[0].T
val = np.nanmin(window)
entries[asset] = (dt_value, val)
lows.append(val)
continue
return np.array(lows)
def closes(self, assets, dt):
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'close')
closes = []
session_label = self._trading_calendar.minute_to_session_label(dt)
def _get_filled_close(asset):
window = self._minute_reader.load_raw_arrays(
['close'],
market_open,
dt,
[asset],
)[0]
try:
return window[~np.isnan(window)][-1]
except IndexError:
return np.NaN
for asset in assets:
if not asset.is_alive_for_session(session_label):
closes.append(np.NaN)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'close')
entries[asset] = (dt_value, val)
closes.append(val)
continue
else:
try:
last_visited_dt, last_close = entries[asset]
if last_visited_dt == dt_value:
closes.append(last_close)
continue
elif last_visited_dt == prev_dt:
val = self._minute_reader.get_value(
asset, dt, 'close')
if pd.isnull(val):
val = last_close
entries[asset] = (dt_value, val)
closes.append(val)
continue
else:
val = self._minute_reader.get_value(
asset, dt, 'close')
if pd.isnull(val):
val = _get_filled_close(asset)
entries[asset] = (dt_value, val)
closes.append(val)
continue
except KeyError:
val = self._minute_reader.get_value(
asset, dt, 'close')
if pd.isnull(val):
val = _get_filled_close(asset)
entries[asset] = (dt_value, val)
closes.append(val)
continue
return np.array(closes)
def volumes(self, assets, dt):
market_open, prev_dt, dt_value, entries = self._prelude(dt, 'volume')
volumes = []
session_label = self._trading_calendar.minute_to_session_label(dt)
for asset in assets:
if not asset.is_alive_for_session(session_label):
volumes.append(0)
continue
if prev_dt is None:
val = self._minute_reader.get_value(asset, dt, 'volume')
entries[asset] = (dt_value, val)
volumes.append(val)
continue
else:
try:
last_visited_dt, last_total = entries[asset]
if last_visited_dt == dt_value:
volumes.append(last_total)
continue
elif last_visited_dt == prev_dt:
val = self._minute_reader.get_value(
asset, dt, 'volume')
val += last_total
entries[asset] = (dt_value, val)
volumes.append(val)
continue
else:
after_last = pd.Timestamp(
last_visited_dt + self._one_min, tz='UTC')
window = self._minute_reader.load_raw_arrays(
['volume'],
after_last,
dt,
[asset],
)[0]
val = np.nansum(window) + last_total
entries[asset] = (dt_value, val)
volumes.append(val)
continue
except KeyError:
window = self._minute_reader.load_raw_arrays(
['volume'],
market_open,
dt,
[asset],
)[0]
val = np.nansum(window)
entries[asset] = (dt_value, val)
volumes.append(val)
continue
return np.array(volumes)
class MinuteResampleSessionBarReader(SessionBarReader):
def __init__(self, calendar, minute_bar_reader):
self._calendar = calendar
self._minute_bar_reader = minute_bar_reader
def _get_resampled(self, columns, start_session, end_session, assets):
range_open = self._calendar.session_open(start_session)
range_close = self._calendar.session_close(end_session)
minute_data = self._minute_bar_reader.load_raw_arrays(
columns,
range_open,
range_close,
assets,
)
# Get the index of the close minute for each session in the range.
# If the range contains only one session, the only close in the range
# is the last minute in the data. Otherwise, we need to get all the
# session closes and find their indices in the range of minutes.
if start_session == end_session:
close_ilocs = np.array([len(minute_data[0]) - 1], dtype=np.int64)
else:
minutes = self._calendar.minutes_in_range(
range_open,
range_close,
)
session_closes = self._calendar.session_closes_in_range(
start_session,
end_session,
)
close_ilocs = minutes.searchsorted(session_closes.values)
results = []
shape = (len(close_ilocs), len(assets))
for col in columns:
if col != 'volume':
out = np.full(shape, np.nan)
else:
out = np.zeros(shape, dtype=np.uint32)
results.append(out)
for i in range(len(assets)):
for j, column in enumerate(columns):
data = minute_data[j][:, i]
minute_to_session(column, close_ilocs, data, results[j][:, i])
return results
@property
def trading_calendar(self):
return self._calendar
def load_raw_arrays(self, columns, start_dt, end_dt, sids):
return self._get_resampled(columns, start_dt, end_dt, sids)
def get_value(self, sid, session, colname):
# WARNING: This will need caching or other optimization if used in a
# tight loop.
# This was developed to complete interface, but has not been tuned
# for real world use.
return self._get_resampled([colname], session, session, [sid])[0][0][0]
@lazyval
def sessions(self):
cal = self._calendar
first = self._minute_bar_reader.first_trading_day
last = cal.minute_to_session_label(
self._minute_bar_reader.last_available_dt)
return cal.sessions_in_range(first, last)
@lazyval
def last_available_dt(self):
return self.trading_calendar.minute_to_session_label(
self._minute_bar_reader.last_available_dt
)
@property
def first_trading_day(self):
return self._minute_bar_reader.first_trading_day
def get_last_traded_dt(self, asset, dt):
return self.trading_calendar.minute_to_session_label(
self._minute_bar_reader.get_last_traded_dt(asset, dt))
class ReindexBarReader(with_metaclass(ABCMeta)):
def __init__(self,
trading_calendar,
reader,
first_trading_session,
last_trading_session):
self._trading_calendar = trading_calendar
self._reader = reader
self._first_trading_session = first_trading_session
self._last_trading_session = last_trading_session
@property
def last_available_dt(self):
return self._reader.last_available_dt
def get_last_traded_dt(self, sid, dt):
return self._reader.get_last_traded_dt(sid, dt)
@property
def first_trading_day(self):
return self._reader.first_trading_day
def get_value(self, sid, dt, field):
# Give an empty result if no data is present.
try:
return self._reader.get_value(sid, dt, field)
except NoDataOnDate:
if field == 'volume':
return 0
else:
return np.nan
@abstractmethod
def _outer_dts(self, start_dt, end_dt):
raise NotImplementedError
@abstractmethod
def _inner_dts(self, start_dt, end_dt):
raise NotImplementedError
@property
def trading_calendar(self):
return self._trading_calendar
@lazyval
def sessions(self):
return self.trading_calendar.sessions_in_range(
self._first_trading_session,
self._last_trading_session
)
def load_raw_arrays(self, fields, start_dt, end_dt, sids):
outer_dts = self._outer_dts(start_dt, end_dt)
inner_dts = self._inner_dts(start_dt, end_dt)
indices = outer_dts.searchsorted(inner_dts)
shape = len(outer_dts), len(sids)
outer_results = []
if len(inner_dts) > 0:
inner_results = self._reader.load_raw_arrays(
fields, inner_dts[0], inner_dts[-1], sids)
else:
inner_results = None
for i, field in enumerate(fields):
if field != 'volume':
out = np.full(shape, np.nan)
else:
out = np.zeros(shape, dtype=np.uint32)
if inner_results is not None:
out[indices] = inner_results[i]
outer_results.append(out)
return outer_results
class ReindexMinuteBarReader(ReindexBarReader, MinuteBarReader):
def _outer_dts(self, start_dt, end_dt):
return self._trading_calendar.minutes_in_range(start_dt, end_dt)
def _inner_dts(self, start_dt, end_dt):
return self._reader.calendar.minutes_in_range(start_dt, end_dt)
class ReindexSessionBarReader(ReindexBarReader, SessionBarReader):
def _outer_dts(self, start_dt, end_dt):
return self.trading_calendar.sessions_in_range(start_dt, end_dt)
def _inner_dts(self, start_dt, end_dt):
return self._reader.trading_calendar.sessions_in_range(
start_dt, end_dt)
| true | true |
f725827d5a741e222353b63795ed67c9692af41b | 1,201 | py | Python | load_testing/ial2_sign_up.locustfile.py | isabella232/identity-loadtest | d915fe5920978672246a1de46b0d9530f7d38fcc | [
"CC0-1.0"
] | null | null | null | load_testing/ial2_sign_up.locustfile.py | isabella232/identity-loadtest | d915fe5920978672246a1de46b0d9530f7d38fcc | [
"CC0-1.0"
] | 1 | 2021-02-24T02:55:22.000Z | 2021-02-24T02:55:22.000Z | load_testing/ial2_sign_up.locustfile.py | isabella232/identity-loadtest | d915fe5920978672246a1de46b0d9530f7d38fcc | [
"CC0-1.0"
] | null | null | null | from locust import HttpUser, TaskSet, task, between
from common_flows import flow_ial2_proofing, flow_sign_up, flow_helper
class IAL2SignUpLoad(TaskSet):
# Preload drivers license data
license_front = flow_helper.load_fixture("mont-front.jpeg")
license_back = flow_helper.load_fixture("mont-back.jpeg")
def on_start(self):
print("*** Starting Sign-Up and IAL2 proof load tests ***")
def on_stop(self):
print("*** Ending IAL2 Sign-Up load tests ***")
""" @task(<weight>) : value=3 executes 3x as often as value=1 """
""" Things inside task are synchronous. Tasks are async """
@task(1)
def sign_up_and_proof_load_test(self):
# Sign up flow
flow_sign_up.do_sign_up(self)
# Get /account page
flow_helper.do_request(self, "get", "/account", "/account")
# IAL2 Proofing flow
flow_ial2_proofing.do_ial2_proofing(self)
# Get the /account page now
flow_helper.do_request(self, "get", "/account", "/account")
# Now log out
flow_helper.do_request(self, "get", "/logout", "/")
class WebsiteUser(HttpUser):
tasks = [IAL2SignUpLoad]
wait_time = between(5, 9)
| 29.292683 | 70 | 0.654455 | from locust import HttpUser, TaskSet, task, between
from common_flows import flow_ial2_proofing, flow_sign_up, flow_helper
class IAL2SignUpLoad(TaskSet):
license_front = flow_helper.load_fixture("mont-front.jpeg")
license_back = flow_helper.load_fixture("mont-back.jpeg")
def on_start(self):
print("*** Starting Sign-Up and IAL2 proof load tests ***")
def on_stop(self):
print("*** Ending IAL2 Sign-Up load tests ***")
@task(1)
def sign_up_and_proof_load_test(self):
flow_sign_up.do_sign_up(self)
flow_helper.do_request(self, "get", "/account", "/account")
flow_ial2_proofing.do_ial2_proofing(self)
flow_helper.do_request(self, "get", "/account", "/account")
flow_helper.do_request(self, "get", "/logout", "/")
class WebsiteUser(HttpUser):
tasks = [IAL2SignUpLoad]
wait_time = between(5, 9)
| true | true |
f725827e2a3d139f12b578cfd7d4e3af8491e768 | 927 | py | Python | bin/pdbqt2pdb_ref.py | gicsaw/pdbtools | 10a9441f0345d34e90ca1c454a6aa460b7da926d | [
"MIT"
] | null | null | null | bin/pdbqt2pdb_ref.py | gicsaw/pdbtools | 10a9441f0345d34e90ca1c454a6aa460b7da926d | [
"MIT"
] | null | null | null | bin/pdbqt2pdb_ref.py | gicsaw/pdbtools | 10a9441f0345d34e90ca1c454a6aa460b7da926d | [
"MIT"
] | null | null | null | #!/usr/bin/env python
import pdbtools.ligand_tools as ligand_tools
def main():
import argparse
title_line = 'convert pdbqt to pdb using reference pdb file'
parser = argparse.ArgumentParser(description=title_line)
parser.add_argument('-i', '--input_file', required=True,
help='input ligand pdbqt file')
parser.add_argument('-o', '--output_file', required=True,
help='output ligand pdb file')
parser.add_argument('-r', '--ref_file', required=True,
help='reference ligand pdb file')
args = parser.parse_args()
ligand_input_file = args.input_file
ligand_output_file = args.output_file
ref_file = args.ref_file
e = ligand_tools.pdbqt_to_pdb_ref(ligand_input_file, ligand_output_file,
ref_file)
if e is not None:
print(e)
if __name__ == "__main__":
main()
| 30.9 | 76 | 0.629989 |
import pdbtools.ligand_tools as ligand_tools
def main():
import argparse
title_line = 'convert pdbqt to pdb using reference pdb file'
parser = argparse.ArgumentParser(description=title_line)
parser.add_argument('-i', '--input_file', required=True,
help='input ligand pdbqt file')
parser.add_argument('-o', '--output_file', required=True,
help='output ligand pdb file')
parser.add_argument('-r', '--ref_file', required=True,
help='reference ligand pdb file')
args = parser.parse_args()
ligand_input_file = args.input_file
ligand_output_file = args.output_file
ref_file = args.ref_file
e = ligand_tools.pdbqt_to_pdb_ref(ligand_input_file, ligand_output_file,
ref_file)
if e is not None:
print(e)
if __name__ == "__main__":
main()
| true | true |
f72583f1e1e8e5ca6b7b3190c8fd9abe0893dc59 | 9,848 | py | Python | pysnmp-with-texts/CISCO-HARDWARE-IP-VERIFY-MIB.py | agustinhenze/mibs.snmplabs.com | 1fc5c07860542b89212f4c8ab807057d9a9206c7 | [
"Apache-2.0"
] | 8 | 2019-05-09T17:04:00.000Z | 2021-06-09T06:50:51.000Z | pysnmp-with-texts/CISCO-HARDWARE-IP-VERIFY-MIB.py | agustinhenze/mibs.snmplabs.com | 1fc5c07860542b89212f4c8ab807057d9a9206c7 | [
"Apache-2.0"
] | 4 | 2019-05-31T16:42:59.000Z | 2020-01-31T21:57:17.000Z | pysnmp-with-texts/CISCO-HARDWARE-IP-VERIFY-MIB.py | agustinhenze/mibs.snmplabs.com | 1fc5c07860542b89212f4c8ab807057d9a9206c7 | [
"Apache-2.0"
] | 10 | 2019-04-30T05:51:36.000Z | 2022-02-16T03:33:41.000Z | #
# PySNMP MIB module CISCO-HARDWARE-IP-VERIFY-MIB (http://snmplabs.com/pysmi)
# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/CISCO-HARDWARE-IP-VERIFY-MIB
# Produced by pysmi-0.3.4 at Wed May 1 11:59:41 2019
# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4
# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15)
#
Integer, ObjectIdentifier, OctetString = mibBuilder.importSymbols("ASN1", "Integer", "ObjectIdentifier", "OctetString")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsIntersection, ValueSizeConstraint, SingleValueConstraint, ConstraintsUnion, ValueRangeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsIntersection", "ValueSizeConstraint", "SingleValueConstraint", "ConstraintsUnion", "ValueRangeConstraint")
ciscoMgmt, = mibBuilder.importSymbols("CISCO-SMI", "ciscoMgmt")
ObjectGroup, NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "ObjectGroup", "NotificationGroup", "ModuleCompliance")
Gauge32, Integer32, Counter64, MibScalar, MibTable, MibTableRow, MibTableColumn, ModuleIdentity, NotificationType, iso, Unsigned32, Counter32, Bits, MibIdentifier, TimeTicks, ObjectIdentity, IpAddress = mibBuilder.importSymbols("SNMPv2-SMI", "Gauge32", "Integer32", "Counter64", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "ModuleIdentity", "NotificationType", "iso", "Unsigned32", "Counter32", "Bits", "MibIdentifier", "TimeTicks", "ObjectIdentity", "IpAddress")
DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention")
ciscoHardwareIpVerifyMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 9, 9, 804))
ciscoHardwareIpVerifyMIB.setRevisions(('2012-09-04 00:00',))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
if mibBuilder.loadTexts: ciscoHardwareIpVerifyMIB.setRevisionsDescriptions(('Initial version of this MIB module.',))
if mibBuilder.loadTexts: ciscoHardwareIpVerifyMIB.setLastUpdated('201209040000Z')
if mibBuilder.loadTexts: ciscoHardwareIpVerifyMIB.setOrganization('Cisco Systems, Inc.')
if mibBuilder.loadTexts: ciscoHardwareIpVerifyMIB.setContactInfo('Cisco Systems Customer Service Postal: 170 W Tasman Drive San Jose, CA 95134 USA Tel: +1 800 553-NETS E-mail: cs-lan-switch-snmp@cisco.com')
if mibBuilder.loadTexts: ciscoHardwareIpVerifyMIB.setDescription("This MIB module defines management objects for configuration and monitoring of the Intrusion Detection System (IDS) that checks for IP packet verification. The following terms are used throughout the MIB: IDS: Intrusion Detection System CRC: Cyclic Redundancy Check DF: Don't Fragment ")
ciscoHardwareIpVerifyMIBNotifs = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 804, 0))
ciscoHardwareIpVerifyMIBObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 804, 1))
ciscoHardwareIpVerifyMIBConform = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 804, 2))
chivIpVerifyTable = MibTable((1, 3, 6, 1, 4, 1, 9, 9, 804, 1, 1), )
if mibBuilder.loadTexts: chivIpVerifyTable.setStatus('current')
if mibBuilder.loadTexts: chivIpVerifyTable.setDescription('A list of IDS check configuration and statistical information for each IP type and each IDS check type on the management device.')
chivIpVerifyEntry = MibTableRow((1, 3, 6, 1, 4, 1, 9, 9, 804, 1, 1, 1), ).setIndexNames((0, "CISCO-HARDWARE-IP-VERIFY-MIB", "chivIpVerifyCheckIpType"), (0, "CISCO-HARDWARE-IP-VERIFY-MIB", "chivIpVerifyCheckTypeName"))
if mibBuilder.loadTexts: chivIpVerifyEntry.setStatus('current')
if mibBuilder.loadTexts: chivIpVerifyEntry.setDescription('An entry contains the IDS packet check configuration information and the associated counters.')
chivIpVerifyCheckIpType = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 804, 1, 1, 1, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("ipv4", 1), ("ipv6", 2))))
if mibBuilder.loadTexts: chivIpVerifyCheckIpType.setStatus('current')
if mibBuilder.loadTexts: chivIpVerifyCheckIpType.setDescription('This object indicates the IP address type for IDS packet check.')
chivIpVerifyCheckTypeName = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 804, 1, 1, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17))).clone(namedValues=NamedValues(("addressSrcBroadcast", 1), ("addressSrcMulticast", 2), ("addressDestZero", 3), ("addressIdentical", 4), ("addressSrcReserved", 5), ("addressClassE", 6), ("checksum", 7), ("protocol", 8), ("fragment", 9), ("lengthMinimum", 10), ("lengthConsistent", 11), ("lengthMaximumFragment", 12), ("lengthMaximumUdp", 13), ("lengthMaximumTcp", 14), ("tcpFlags", 15), ("tcpTinyFlags", 16), ("version", 17))))
if mibBuilder.loadTexts: chivIpVerifyCheckTypeName.setStatus('current')
if mibBuilder.loadTexts: chivIpVerifyCheckTypeName.setDescription('This object indicates the IDS packet check type which can be configured on the device. Each check type is a specific criteria. Those IP packets that matches the certain criteria are dropped. addressSrcBroadcast(1) Drop the IPv4 packet if the source address is a broadcast IPv4 address. addressSrcMulticast(2) Drop the IPv4 packet if the source address is a multicast IPv4 address. addressDestZero(3) Drop the IPv4 packet if the destination address is 0.0.0.0. addressIdentical(4) Drop the IPv4 packet if the source IPv4 address is identical to destination IPv4 address. addressSrcReserved(5) Drop the IPv4 packet if the source address is a reserved IPv4 address. addressClassE(6) Drop the IPv4 packet if either the source address or destination address is a class E IPv4 address. checksum(7) Drops the IPv4 packet if its checksum is invalid. protocol(8) Drop the IPv4 packet if the packet fragment has an invalid IP protocol number fragment(9) Drop the IPv4 packet if the packet fragment has a nonzero offset and the DF bit is active. lengthMinimum(10) Drop the IPv4 packet if the Ethernet frame length is less than the IP packet length plus four octets (the CRC length). lengthConsistent(11) Drop the IPv4 or IPv6 packet where the Ethernet frame size is greater than or equal to the IP packet length plus the Ethernet header. lengthMaximumFragment(12) Drop the IPv4 or IPv6 packet if the maximum fragment offset is greater than 65536. lengthMaximumUdp(13) Drop the IPv4 or IPv6 packet if the IP payload length is less than the UDP packet length. lengthMaximumTcp(14) Drop the IPv4 or IPv6 packet if the TCP length is greater than the IP payload length. tcpFlags(15) Drop the IPv4 packet if verification of TCP packet header fails. tcpTinyFlags(16) Drop the IPv4 or IPv6 packet if the IP fragment offset is 1, or if the IP fragment offset is 0 and the IP payload length is less than 16. version(17) Drop the IPv4 packet if the Ethertype is not set to 4 (IPv4); and drops the IPv6 packet if the Ethertype is not set to 6 (IPv6).')
chivIpVerifyCheckStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 804, 1, 1, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("disabled", 1), ("enabled", 2)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: chivIpVerifyCheckStatus.setStatus('current')
if mibBuilder.loadTexts: chivIpVerifyCheckStatus.setDescription('This object specifies the IDS packet check configuration status.')
chivIpVerifyPacketsDropped = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 804, 1, 1, 1, 4), Counter64()).setMaxAccess("readonly")
if mibBuilder.loadTexts: chivIpVerifyPacketsDropped.setStatus('current')
if mibBuilder.loadTexts: chivIpVerifyPacketsDropped.setDescription('This object indicates the number of packets which has been dropped.')
ciscoHardwareIpVerifyMIBCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 804, 2, 1))
ciscoHardwareIpVerifyMIBGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 804, 2, 2))
ciscoHardwareIpVerifyMIBCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 9, 9, 804, 2, 1, 1)).setObjects(("CISCO-HARDWARE-IP-VERIFY-MIB", "ciscoHardwareIpVerifyMIBStatisticGroup"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoHardwareIpVerifyMIBCompliance = ciscoHardwareIpVerifyMIBCompliance.setStatus('current')
if mibBuilder.loadTexts: ciscoHardwareIpVerifyMIBCompliance.setDescription('The compliance statement for the CISCO-HARDWARE-IP-VERIFY-MIB.')
ciscoHardwareIpVerifyMIBStatisticGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 9, 9, 804, 2, 2, 1)).setObjects(("CISCO-HARDWARE-IP-VERIFY-MIB", "chivIpVerifyCheckStatus"), ("CISCO-HARDWARE-IP-VERIFY-MIB", "chivIpVerifyPacketsDropped"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoHardwareIpVerifyMIBStatisticGroup = ciscoHardwareIpVerifyMIBStatisticGroup.setStatus('current')
if mibBuilder.loadTexts: ciscoHardwareIpVerifyMIBStatisticGroup.setDescription('A collection of objects that provides configuration and statistical information for IDS packet check.')
mibBuilder.exportSymbols("CISCO-HARDWARE-IP-VERIFY-MIB", ciscoHardwareIpVerifyMIBConform=ciscoHardwareIpVerifyMIBConform, chivIpVerifyCheckIpType=chivIpVerifyCheckIpType, ciscoHardwareIpVerifyMIBStatisticGroup=ciscoHardwareIpVerifyMIBStatisticGroup, ciscoHardwareIpVerifyMIBGroups=ciscoHardwareIpVerifyMIBGroups, ciscoHardwareIpVerifyMIBCompliances=ciscoHardwareIpVerifyMIBCompliances, chivIpVerifyTable=chivIpVerifyTable, chivIpVerifyCheckStatus=chivIpVerifyCheckStatus, ciscoHardwareIpVerifyMIBObjects=ciscoHardwareIpVerifyMIBObjects, PYSNMP_MODULE_ID=ciscoHardwareIpVerifyMIB, ciscoHardwareIpVerifyMIBNotifs=ciscoHardwareIpVerifyMIBNotifs, chivIpVerifyEntry=chivIpVerifyEntry, ciscoHardwareIpVerifyMIBCompliance=ciscoHardwareIpVerifyMIBCompliance, chivIpVerifyCheckTypeName=chivIpVerifyCheckTypeName, chivIpVerifyPacketsDropped=chivIpVerifyPacketsDropped, ciscoHardwareIpVerifyMIB=ciscoHardwareIpVerifyMIB)
| 172.77193 | 2,096 | 0.792141 |
Integer, ObjectIdentifier, OctetString = mibBuilder.importSymbols("ASN1", "Integer", "ObjectIdentifier", "OctetString")
NamedValues, = mibBuilder.importSymbols("ASN1-ENUMERATION", "NamedValues")
ConstraintsIntersection, ValueSizeConstraint, SingleValueConstraint, ConstraintsUnion, ValueRangeConstraint = mibBuilder.importSymbols("ASN1-REFINEMENT", "ConstraintsIntersection", "ValueSizeConstraint", "SingleValueConstraint", "ConstraintsUnion", "ValueRangeConstraint")
ciscoMgmt, = mibBuilder.importSymbols("CISCO-SMI", "ciscoMgmt")
ObjectGroup, NotificationGroup, ModuleCompliance = mibBuilder.importSymbols("SNMPv2-CONF", "ObjectGroup", "NotificationGroup", "ModuleCompliance")
Gauge32, Integer32, Counter64, MibScalar, MibTable, MibTableRow, MibTableColumn, ModuleIdentity, NotificationType, iso, Unsigned32, Counter32, Bits, MibIdentifier, TimeTicks, ObjectIdentity, IpAddress = mibBuilder.importSymbols("SNMPv2-SMI", "Gauge32", "Integer32", "Counter64", "MibScalar", "MibTable", "MibTableRow", "MibTableColumn", "ModuleIdentity", "NotificationType", "iso", "Unsigned32", "Counter32", "Bits", "MibIdentifier", "TimeTicks", "ObjectIdentity", "IpAddress")
DisplayString, TextualConvention = mibBuilder.importSymbols("SNMPv2-TC", "DisplayString", "TextualConvention")
ciscoHardwareIpVerifyMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 9, 9, 804))
ciscoHardwareIpVerifyMIB.setRevisions(('2012-09-04 00:00',))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
if mibBuilder.loadTexts: ciscoHardwareIpVerifyMIB.setRevisionsDescriptions(('Initial version of this MIB module.',))
if mibBuilder.loadTexts: ciscoHardwareIpVerifyMIB.setLastUpdated('201209040000Z')
if mibBuilder.loadTexts: ciscoHardwareIpVerifyMIB.setOrganization('Cisco Systems, Inc.')
if mibBuilder.loadTexts: ciscoHardwareIpVerifyMIB.setContactInfo('Cisco Systems Customer Service Postal: 170 W Tasman Drive San Jose, CA 95134 USA Tel: +1 800 553-NETS E-mail: cs-lan-switch-snmp@cisco.com')
if mibBuilder.loadTexts: ciscoHardwareIpVerifyMIB.setDescription("This MIB module defines management objects for configuration and monitoring of the Intrusion Detection System (IDS) that checks for IP packet verification. The following terms are used throughout the MIB: IDS: Intrusion Detection System CRC: Cyclic Redundancy Check DF: Don't Fragment ")
ciscoHardwareIpVerifyMIBNotifs = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 804, 0))
ciscoHardwareIpVerifyMIBObjects = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 804, 1))
ciscoHardwareIpVerifyMIBConform = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 804, 2))
chivIpVerifyTable = MibTable((1, 3, 6, 1, 4, 1, 9, 9, 804, 1, 1), )
if mibBuilder.loadTexts: chivIpVerifyTable.setStatus('current')
if mibBuilder.loadTexts: chivIpVerifyTable.setDescription('A list of IDS check configuration and statistical information for each IP type and each IDS check type on the management device.')
chivIpVerifyEntry = MibTableRow((1, 3, 6, 1, 4, 1, 9, 9, 804, 1, 1, 1), ).setIndexNames((0, "CISCO-HARDWARE-IP-VERIFY-MIB", "chivIpVerifyCheckIpType"), (0, "CISCO-HARDWARE-IP-VERIFY-MIB", "chivIpVerifyCheckTypeName"))
if mibBuilder.loadTexts: chivIpVerifyEntry.setStatus('current')
if mibBuilder.loadTexts: chivIpVerifyEntry.setDescription('An entry contains the IDS packet check configuration information and the associated counters.')
chivIpVerifyCheckIpType = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 804, 1, 1, 1, 1), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("ipv4", 1), ("ipv6", 2))))
if mibBuilder.loadTexts: chivIpVerifyCheckIpType.setStatus('current')
if mibBuilder.loadTexts: chivIpVerifyCheckIpType.setDescription('This object indicates the IP address type for IDS packet check.')
chivIpVerifyCheckTypeName = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 804, 1, 1, 1, 2), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17))).clone(namedValues=NamedValues(("addressSrcBroadcast", 1), ("addressSrcMulticast", 2), ("addressDestZero", 3), ("addressIdentical", 4), ("addressSrcReserved", 5), ("addressClassE", 6), ("checksum", 7), ("protocol", 8), ("fragment", 9), ("lengthMinimum", 10), ("lengthConsistent", 11), ("lengthMaximumFragment", 12), ("lengthMaximumUdp", 13), ("lengthMaximumTcp", 14), ("tcpFlags", 15), ("tcpTinyFlags", 16), ("version", 17))))
if mibBuilder.loadTexts: chivIpVerifyCheckTypeName.setStatus('current')
if mibBuilder.loadTexts: chivIpVerifyCheckTypeName.setDescription('This object indicates the IDS packet check type which can be configured on the device. Each check type is a specific criteria. Those IP packets that matches the certain criteria are dropped. addressSrcBroadcast(1) Drop the IPv4 packet if the source address is a broadcast IPv4 address. addressSrcMulticast(2) Drop the IPv4 packet if the source address is a multicast IPv4 address. addressDestZero(3) Drop the IPv4 packet if the destination address is 0.0.0.0. addressIdentical(4) Drop the IPv4 packet if the source IPv4 address is identical to destination IPv4 address. addressSrcReserved(5) Drop the IPv4 packet if the source address is a reserved IPv4 address. addressClassE(6) Drop the IPv4 packet if either the source address or destination address is a class E IPv4 address. checksum(7) Drops the IPv4 packet if its checksum is invalid. protocol(8) Drop the IPv4 packet if the packet fragment has an invalid IP protocol number fragment(9) Drop the IPv4 packet if the packet fragment has a nonzero offset and the DF bit is active. lengthMinimum(10) Drop the IPv4 packet if the Ethernet frame length is less than the IP packet length plus four octets (the CRC length). lengthConsistent(11) Drop the IPv4 or IPv6 packet where the Ethernet frame size is greater than or equal to the IP packet length plus the Ethernet header. lengthMaximumFragment(12) Drop the IPv4 or IPv6 packet if the maximum fragment offset is greater than 65536. lengthMaximumUdp(13) Drop the IPv4 or IPv6 packet if the IP payload length is less than the UDP packet length. lengthMaximumTcp(14) Drop the IPv4 or IPv6 packet if the TCP length is greater than the IP payload length. tcpFlags(15) Drop the IPv4 packet if verification of TCP packet header fails. tcpTinyFlags(16) Drop the IPv4 or IPv6 packet if the IP fragment offset is 1, or if the IP fragment offset is 0 and the IP payload length is less than 16. version(17) Drop the IPv4 packet if the Ethertype is not set to 4 (IPv4); and drops the IPv6 packet if the Ethertype is not set to 6 (IPv6).')
chivIpVerifyCheckStatus = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 804, 1, 1, 1, 3), Integer32().subtype(subtypeSpec=ConstraintsUnion(SingleValueConstraint(1, 2))).clone(namedValues=NamedValues(("disabled", 1), ("enabled", 2)))).setMaxAccess("readwrite")
if mibBuilder.loadTexts: chivIpVerifyCheckStatus.setStatus('current')
if mibBuilder.loadTexts: chivIpVerifyCheckStatus.setDescription('This object specifies the IDS packet check configuration status.')
chivIpVerifyPacketsDropped = MibTableColumn((1, 3, 6, 1, 4, 1, 9, 9, 804, 1, 1, 1, 4), Counter64()).setMaxAccess("readonly")
if mibBuilder.loadTexts: chivIpVerifyPacketsDropped.setStatus('current')
if mibBuilder.loadTexts: chivIpVerifyPacketsDropped.setDescription('This object indicates the number of packets which has been dropped.')
ciscoHardwareIpVerifyMIBCompliances = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 804, 2, 1))
ciscoHardwareIpVerifyMIBGroups = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 9, 804, 2, 2))
ciscoHardwareIpVerifyMIBCompliance = ModuleCompliance((1, 3, 6, 1, 4, 1, 9, 9, 804, 2, 1, 1)).setObjects(("CISCO-HARDWARE-IP-VERIFY-MIB", "ciscoHardwareIpVerifyMIBStatisticGroup"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoHardwareIpVerifyMIBCompliance = ciscoHardwareIpVerifyMIBCompliance.setStatus('current')
if mibBuilder.loadTexts: ciscoHardwareIpVerifyMIBCompliance.setDescription('The compliance statement for the CISCO-HARDWARE-IP-VERIFY-MIB.')
ciscoHardwareIpVerifyMIBStatisticGroup = ObjectGroup((1, 3, 6, 1, 4, 1, 9, 9, 804, 2, 2, 1)).setObjects(("CISCO-HARDWARE-IP-VERIFY-MIB", "chivIpVerifyCheckStatus"), ("CISCO-HARDWARE-IP-VERIFY-MIB", "chivIpVerifyPacketsDropped"))
if getattr(mibBuilder, 'version', (0, 0, 0)) > (4, 4, 0):
ciscoHardwareIpVerifyMIBStatisticGroup = ciscoHardwareIpVerifyMIBStatisticGroup.setStatus('current')
if mibBuilder.loadTexts: ciscoHardwareIpVerifyMIBStatisticGroup.setDescription('A collection of objects that provides configuration and statistical information for IDS packet check.')
mibBuilder.exportSymbols("CISCO-HARDWARE-IP-VERIFY-MIB", ciscoHardwareIpVerifyMIBConform=ciscoHardwareIpVerifyMIBConform, chivIpVerifyCheckIpType=chivIpVerifyCheckIpType, ciscoHardwareIpVerifyMIBStatisticGroup=ciscoHardwareIpVerifyMIBStatisticGroup, ciscoHardwareIpVerifyMIBGroups=ciscoHardwareIpVerifyMIBGroups, ciscoHardwareIpVerifyMIBCompliances=ciscoHardwareIpVerifyMIBCompliances, chivIpVerifyTable=chivIpVerifyTable, chivIpVerifyCheckStatus=chivIpVerifyCheckStatus, ciscoHardwareIpVerifyMIBObjects=ciscoHardwareIpVerifyMIBObjects, PYSNMP_MODULE_ID=ciscoHardwareIpVerifyMIB, ciscoHardwareIpVerifyMIBNotifs=ciscoHardwareIpVerifyMIBNotifs, chivIpVerifyEntry=chivIpVerifyEntry, ciscoHardwareIpVerifyMIBCompliance=ciscoHardwareIpVerifyMIBCompliance, chivIpVerifyCheckTypeName=chivIpVerifyCheckTypeName, chivIpVerifyPacketsDropped=chivIpVerifyPacketsDropped, ciscoHardwareIpVerifyMIB=ciscoHardwareIpVerifyMIB)
| true | true |
f72583f3abd30086f322b31d478f793386b37978 | 1,402 | py | Python | plugins/infocenteruri.py | LandGrey/taoman | 3ad45823e7af0a8a9ee6e1296446c3a6aab43fe4 | [
"MIT"
] | 207 | 2017-05-03T10:31:45.000Z | 2022-03-26T09:49:03.000Z | plugins/infocenteruri.py | the8robot/taoman | 3ad45823e7af0a8a9ee6e1296446c3a6aab43fe4 | [
"MIT"
] | 1 | 2020-03-29T03:28:29.000Z | 2020-03-29T04:41:03.000Z | plugins/infocenteruri.py | the8robot/taoman | 3ad45823e7af0a8a9ee6e1296446c3a6aab43fe4 | [
"MIT"
] | 59 | 2017-05-06T02:43:36.000Z | 2022-01-22T12:39:07.000Z | #!/usr/bin/env python
# coding:utf-8
#
"""
Copyright (c) 2017 LandGrey (https://github.com/LandGrey/taoman)
License: MIT
"""
import urllib
import requests
from lib.fun import crawl_link_handle
from lib.config import baidu_base_url, get_head, timeout, baidu_first_pattern, self_pattern, intranet_ip_pattern, \
ip_simple_pattern
def crawlinfocenter(domain):
domains = []
data = {'wd': 'site:{0} 信息化|网络中心'.format(domain)}
requests.packages.urllib3.disable_warnings()
req = requests.get(baidu_base_url + urllib.urlencode(data), headers=get_head(), timeout=timeout, verify=False)
content = req.text
match = baidu_first_pattern.findall(content)
if match:
info_center_url = crawl_link_handle(match[0][0])
reqs = requests.get('http://' + info_center_url, headers=get_head(), timeout=timeout, verify=False)
matchs = self_pattern.findall(reqs.text)
for m in matchs:
domains.append(crawl_link_handle(m)
if domain in m
else (crawl_link_handle(m)
if ip_simple_pattern.findall(crawl_link_handle(m)
if not intranet_ip_pattern.findall(crawl_link_handle(m))
else '')
else ''))
return domains
| 38.944444 | 118 | 0.599857 |
import urllib
import requests
from lib.fun import crawl_link_handle
from lib.config import baidu_base_url, get_head, timeout, baidu_first_pattern, self_pattern, intranet_ip_pattern, \
ip_simple_pattern
def crawlinfocenter(domain):
domains = []
data = {'wd': 'site:{0} 信息化|网络中心'.format(domain)}
requests.packages.urllib3.disable_warnings()
req = requests.get(baidu_base_url + urllib.urlencode(data), headers=get_head(), timeout=timeout, verify=False)
content = req.text
match = baidu_first_pattern.findall(content)
if match:
info_center_url = crawl_link_handle(match[0][0])
reqs = requests.get('http://' + info_center_url, headers=get_head(), timeout=timeout, verify=False)
matchs = self_pattern.findall(reqs.text)
for m in matchs:
domains.append(crawl_link_handle(m)
if domain in m
else (crawl_link_handle(m)
if ip_simple_pattern.findall(crawl_link_handle(m)
if not intranet_ip_pattern.findall(crawl_link_handle(m))
else '')
else ''))
return domains
| true | true |
f725848078385894b2570cd95107b891abee18ba | 7,574 | py | Python | file_hash.py | XVicarious/file_hash | ebab0151dbbd2d162742008d9088ad03a38f495e | [
"MIT"
] | null | null | null | file_hash.py | XVicarious/file_hash | ebab0151dbbd2d162742008d9088ad03a38f495e | [
"MIT"
] | 1 | 2018-10-27T09:02:13.000Z | 2018-10-27T09:02:13.000Z | file_hash.py | XVicarious/file_hash | ebab0151dbbd2d162742008d9088ad03a38f495e | [
"MIT"
] | null | null | null | """Hash your files for easy identification."""
import hashlib
import logging
import os
from builtins import * # noqa pylint: disable=unused-import, redefined-builtin
from typing import Dict
from flexget import plugin
from flexget.event import event
from flexget.logger import FlexGetLogger
from .cunit import IECUnit
PLUGIN_ID = 'file_hash'
log: FlexGetLogger = logging.getLogger(PLUGIN_ID)
class FileHashPlugin(object):
"""
Task class that does the hashing.
By default file_hash will:
- Use blake2b if it is available, otherwise it will use MD5
- Start at 50MiB into the file
- If the file is less than 50MiB, it starts at the beginning
- Hashes 25MiB of the file after the starting point
- If the file does not have 25MiB after the starting point, it will hash from the starting point to the end
- Choose MAX two 'size', 'start', 'stop'
Examples:
# Use file_hash with the default settings.
file_hash: yes
# Use sha1 with the rest of the default settings
file_hash: sha1
# Hash 1MiB, 25MiB into the file with algorithm SHA256
file_hash:
algorithm: sha256
size: 1
start: 25
# Hash from 25MiB in to 35MiB in
file_hash:
start: 25
stop: 35
"""
@staticmethod
def __default_algo():
return 'blake2b' if 'blake2b' in hashlib.algorithms_available else 'md5'
hash_size_default = 25
hash_start_default = 50
schema = {
'oneOf': [
{'type': 'boolean'},
{'type': 'string',
'enum': list(hashlib.algorithms_available)},
{'type': 'object',
'properties': {
'algorithm': {
'type': 'string',
'enum': list(hashlib.algorithms_available)},
'size': {'type': 'integer', 'default': hash_size_default},
'start': {'type': 'integer', 'default': hash_start_default},
'stop': {'type': 'integer'},
'time': {'type': 'boolean', 'default': 'boolean'}}},
],
}
plugin_fields = {'file_hash_type', 'file_hash_hash', 'file_hash_modified', 'file_hash_bytes'}
@staticmethod
def __strict_boolean(check):
if isinstance(check, bool) and check:
return True
return False
def __get_algo(self, config):
return self.__default_algo()
def compare_entry(self, entry, config):
if 'file_hash' in entry:
file_hash = entry['file_hash']
match_algo = file_hash.algorithm == self.__get_algo(config)
match_file_size = file_hash.file_size == os.path.getsize(entry['location'])
match_modified = file_hash.modified == os.path.getmtime(entry['location'])
match_start = file_hash.start == config.get('start')
match_stop = file_hash.stop == config.get('stop')
match_chunk_size = file_hash.chunk_size == config.get('size')
match_strict = match_file_size and match_start and match_stop and match_chunk_size
if match_algo and match_strict:
return True
return False
def on_task_metainfo(self, task, config):
"""Call the plugin."""
log.info('Starting file_hash')
# todo: add conditions to adapt to users' configuration
if self.__strict_boolean(config):
config = {True}
hash_portion = {
'algorithm': self.__get_algo(config),
'size': IECUnit.MiB * (config['size'] if 'size' in config else self.hash_size_default),
'start': IECUnit.MiB * (config['start'] if 'start' in config else self.hash_start_default),
'stop': IECUnit.MiB * (config['stop'] if 'stop' in config else -1),
}
hasher = hashlib.new(hash_portion['algorithm'])
log.verbose('Hasing with algorithm: %s', hash_portion['algorithm'])
log.debug('Hashing %s MiB of each file.', hash_portion['size'])
log.debug('Hashing starting %s MiB into file.', hash_portion['start'])
log.debug('Hashing ending at %s MiB.', hash_portion['stop'])
len_entries = len(task.entries)
idx = 0
for entry in task.entries:
idx += 1
file_size = os.path.getsize(entry['location'])
if self.compare_entry(entry, config):
log.verbose('This file seems to be unmodified, skipping')
continue
log.verbose('%s/%s: Hasing %s', idx, len_entries, entry['location'])
current_hasher = hasher.copy()
tmp_hash_portion_start = -1
if file_size < hash_portion['start']:
log.debug('The file is only %s MiB, adjusting start location.', float(file_size / IECUnit.MiB))
if file_size < hash_portion['size']:
log.debug('The file is less than the set size to to hash, setting start position to 0')
tmp_hash_portion_start = 0
else:
tmp_hash_portion_start = file_size - hash_portion['size']
log.debug('The size of the file is greater than the set size to hash, \
setting start position to %s MiB', tmp_hash_portion_start)
with open(entry['location'], 'rb') as to_hash:
to_hash.seek(tmp_hash_portion_start if tmp_hash_portion_start > -1 else hash_portion['start'])
piece = to_hash.read(hash_portion['size'])
current_hasher.update(piece)
file_digest = current_hasher.hexdigest()
file_modified = os.path.getmtime(entry['location'])
filehash = FileHash(hash_portion, file_digest, file_modified, file_size)
entry['file_hash'] = filehash
log.debug(filehash)
to_hash.close()
class FileHash(object):
"""Store the information from the hashing."""
algorithm = None
file_hash = None
modified = None
start = None
stop = None
chunk_size = None
size = None
def __init__(self, config_settings: Dict, file_hash, modified, size):
"""
Initialize a FileHash object.
config_settings -- ends up being the config for the plugin
file_hash -- the hash of the file
modified -- last time the file was modified
size -- size of the file in bytes
"""
self.algorithm = config_settings['algorithm']
self.start = config_settings['start']
self.stop = config_settings['stop']
self.chunk_size = config_settings['size']
self.file_hash = file_hash
self.modified = modified
self.size = size
def __eq__(self, other):
return isinstance(other, FileHash) and\
self.algorithm == other.algorithm and\
self.file_hash == other.file_hash
def __repr__(self):
"""Represent a FileHash."""
return """<FileHash: \
algorithm={0}, \
start={1}, stop={2}, \
chunk_size={3}, \
file_hash={4}, \
modified={5}, \
size={6}""".format(
self.algorithm,
self.start, self.stop,
self.chunk_size,
self.file_hash,
self.modified,
self.size)
@event('plugin.register')
def register_plugin():
plugin.register(FileHashPlugin, PLUGIN_ID, api_ver=2, interfaces=['task', 'series_metainfo', 'movie_metainfo'])
| 37.127451 | 115 | 0.591761 |
import hashlib
import logging
import os
from builtins import *
from typing import Dict
from flexget import plugin
from flexget.event import event
from flexget.logger import FlexGetLogger
from .cunit import IECUnit
PLUGIN_ID = 'file_hash'
log: FlexGetLogger = logging.getLogger(PLUGIN_ID)
class FileHashPlugin(object):
@staticmethod
def __default_algo():
return 'blake2b' if 'blake2b' in hashlib.algorithms_available else 'md5'
hash_size_default = 25
hash_start_default = 50
schema = {
'oneOf': [
{'type': 'boolean'},
{'type': 'string',
'enum': list(hashlib.algorithms_available)},
{'type': 'object',
'properties': {
'algorithm': {
'type': 'string',
'enum': list(hashlib.algorithms_available)},
'size': {'type': 'integer', 'default': hash_size_default},
'start': {'type': 'integer', 'default': hash_start_default},
'stop': {'type': 'integer'},
'time': {'type': 'boolean', 'default': 'boolean'}}},
],
}
plugin_fields = {'file_hash_type', 'file_hash_hash', 'file_hash_modified', 'file_hash_bytes'}
@staticmethod
def __strict_boolean(check):
if isinstance(check, bool) and check:
return True
return False
def __get_algo(self, config):
return self.__default_algo()
def compare_entry(self, entry, config):
if 'file_hash' in entry:
file_hash = entry['file_hash']
match_algo = file_hash.algorithm == self.__get_algo(config)
match_file_size = file_hash.file_size == os.path.getsize(entry['location'])
match_modified = file_hash.modified == os.path.getmtime(entry['location'])
match_start = file_hash.start == config.get('start')
match_stop = file_hash.stop == config.get('stop')
match_chunk_size = file_hash.chunk_size == config.get('size')
match_strict = match_file_size and match_start and match_stop and match_chunk_size
if match_algo and match_strict:
return True
return False
def on_task_metainfo(self, task, config):
log.info('Starting file_hash')
if self.__strict_boolean(config):
config = {True}
hash_portion = {
'algorithm': self.__get_algo(config),
'size': IECUnit.MiB * (config['size'] if 'size' in config else self.hash_size_default),
'start': IECUnit.MiB * (config['start'] if 'start' in config else self.hash_start_default),
'stop': IECUnit.MiB * (config['stop'] if 'stop' in config else -1),
}
hasher = hashlib.new(hash_portion['algorithm'])
log.verbose('Hasing with algorithm: %s', hash_portion['algorithm'])
log.debug('Hashing %s MiB of each file.', hash_portion['size'])
log.debug('Hashing starting %s MiB into file.', hash_portion['start'])
log.debug('Hashing ending at %s MiB.', hash_portion['stop'])
len_entries = len(task.entries)
idx = 0
for entry in task.entries:
idx += 1
file_size = os.path.getsize(entry['location'])
if self.compare_entry(entry, config):
log.verbose('This file seems to be unmodified, skipping')
continue
log.verbose('%s/%s: Hasing %s', idx, len_entries, entry['location'])
current_hasher = hasher.copy()
tmp_hash_portion_start = -1
if file_size < hash_portion['start']:
log.debug('The file is only %s MiB, adjusting start location.', float(file_size / IECUnit.MiB))
if file_size < hash_portion['size']:
log.debug('The file is less than the set size to to hash, setting start position to 0')
tmp_hash_portion_start = 0
else:
tmp_hash_portion_start = file_size - hash_portion['size']
log.debug('The size of the file is greater than the set size to hash, \
setting start position to %s MiB', tmp_hash_portion_start)
with open(entry['location'], 'rb') as to_hash:
to_hash.seek(tmp_hash_portion_start if tmp_hash_portion_start > -1 else hash_portion['start'])
piece = to_hash.read(hash_portion['size'])
current_hasher.update(piece)
file_digest = current_hasher.hexdigest()
file_modified = os.path.getmtime(entry['location'])
filehash = FileHash(hash_portion, file_digest, file_modified, file_size)
entry['file_hash'] = filehash
log.debug(filehash)
to_hash.close()
class FileHash(object):
algorithm = None
file_hash = None
modified = None
start = None
stop = None
chunk_size = None
size = None
def __init__(self, config_settings: Dict, file_hash, modified, size):
self.algorithm = config_settings['algorithm']
self.start = config_settings['start']
self.stop = config_settings['stop']
self.chunk_size = config_settings['size']
self.file_hash = file_hash
self.modified = modified
self.size = size
def __eq__(self, other):
return isinstance(other, FileHash) and\
self.algorithm == other.algorithm and\
self.file_hash == other.file_hash
def __repr__(self):
return """<FileHash: \
algorithm={0}, \
start={1}, stop={2}, \
chunk_size={3}, \
file_hash={4}, \
modified={5}, \
size={6}""".format(
self.algorithm,
self.start, self.stop,
self.chunk_size,
self.file_hash,
self.modified,
self.size)
@event('plugin.register')
def register_plugin():
plugin.register(FileHashPlugin, PLUGIN_ID, api_ver=2, interfaces=['task', 'series_metainfo', 'movie_metainfo'])
| true | true |
f72585050c810ea13194c609b96660990583ebbd | 2,046 | py | Python | Day-04/part2.py | archanpatkar/advent2021 | 8e0780cd28b5825af092e4ba8e3d9cd1059bce92 | [
"MIT"
] | null | null | null | Day-04/part2.py | archanpatkar/advent2021 | 8e0780cd28b5825af092e4ba8e3d9cd1059bce92 | [
"MIT"
] | null | null | null | Day-04/part2.py | archanpatkar/advent2021 | 8e0780cd28b5825af092e4ba8e3d9cd1059bce92 | [
"MIT"
] | null | null | null | import sys
sys.path.append("..")
from common import *
def parse(d):
temp = d.strip().split("\n")
first = tuple([int(n) for n in temp[0].strip().split(",")])
second = []
temp2 = []
print(temp)
for r in temp[1:]:
# print(r)
if(len(r) == 0):
second.append(tuple(temp2))
temp2 = []
else:
temp2.append(tuple([int(n) for n in r.split(" ") if(len(n) > 0)]))
second.append(tuple(temp2))
print(first)
print(second)
return (first,second)
data = aoci(parse)
boards = [b for b in data[1] if len(b) > 0]
print("boards")
print(boards)
print("-----------------------------")
pos = {b:{} for b in boards}
winners = []
winners2 = []
winner = None
i = 0
draw = None
found = None
for draw in data[0]:
print("Draw:",draw)
for board in boards:
for l in range(len(board)):
if draw in board[l]:
if not pos[board].get((l,board[l].index(draw))):
pos[board][(l,board[l].index(draw))] = []
pos[board][(l,board[l].index(draw))] = draw
nd = {b:{} for b in pos}
for b in boards:
if(not(b in winners2)):
for i in range(5):
nd[b][(i,0)] = 0
nd[b][(0,i)] = 0
nd[b][(0,0,"C")] = 0
for k in pos[b]:
nd[b][(k[0],0)] += 1
if(k[1] == 0):
nd[b][(0,0,"C")] += 1
else: nd[b][(0,k[1])] += 1
for s in nd[b]:
if(nd[b][s] >= 5):
found = (b,nd[b],pos[b],draw)
winners.append(found)
winners2.append(b)
break
if len(winners) == len(boards):
break
if(winner == None):
winner = winners[-1]
pprint(winner,indent=4)
sum = 0
for i in range(5):
for j in range(5):
if not((i,j) in winner[2]):
print(winner[0][i][j])
sum += winner[0][i][j]
print("output")
print(sum)
print(draw)
print(sum * winner[-1]) | 25.575 | 78 | 0.451124 | import sys
sys.path.append("..")
from common import *
def parse(d):
temp = d.strip().split("\n")
first = tuple([int(n) for n in temp[0].strip().split(",")])
second = []
temp2 = []
print(temp)
for r in temp[1:]:
if(len(r) == 0):
second.append(tuple(temp2))
temp2 = []
else:
temp2.append(tuple([int(n) for n in r.split(" ") if(len(n) > 0)]))
second.append(tuple(temp2))
print(first)
print(second)
return (first,second)
data = aoci(parse)
boards = [b for b in data[1] if len(b) > 0]
print("boards")
print(boards)
print("-----------------------------")
pos = {b:{} for b in boards}
winners = []
winners2 = []
winner = None
i = 0
draw = None
found = None
for draw in data[0]:
print("Draw:",draw)
for board in boards:
for l in range(len(board)):
if draw in board[l]:
if not pos[board].get((l,board[l].index(draw))):
pos[board][(l,board[l].index(draw))] = []
pos[board][(l,board[l].index(draw))] = draw
nd = {b:{} for b in pos}
for b in boards:
if(not(b in winners2)):
for i in range(5):
nd[b][(i,0)] = 0
nd[b][(0,i)] = 0
nd[b][(0,0,"C")] = 0
for k in pos[b]:
nd[b][(k[0],0)] += 1
if(k[1] == 0):
nd[b][(0,0,"C")] += 1
else: nd[b][(0,k[1])] += 1
for s in nd[b]:
if(nd[b][s] >= 5):
found = (b,nd[b],pos[b],draw)
winners.append(found)
winners2.append(b)
break
if len(winners) == len(boards):
break
if(winner == None):
winner = winners[-1]
pprint(winner,indent=4)
sum = 0
for i in range(5):
for j in range(5):
if not((i,j) in winner[2]):
print(winner[0][i][j])
sum += winner[0][i][j]
print("output")
print(sum)
print(draw)
print(sum * winner[-1]) | true | true |
f7258523558bf650489ce11f89d5a70fe2400656 | 1,042 | py | Python | dts_test_project/dts_test_app/migrations/0001_initial.py | pvandegeer/django-tenant-schemas | 20c72782cee51a33fd5c56a0af7b2c653c1b6770 | [
"MIT"
] | 2 | 2018-08-14T07:37:06.000Z | 2018-09-27T11:20:54.000Z | dts_test_project/dts_test_app/migrations/0001_initial.py | pvandegeer/django-tenant-schemas | 20c72782cee51a33fd5c56a0af7b2c653c1b6770 | [
"MIT"
] | 1 | 2021-01-25T09:48:27.000Z | 2021-01-25T09:48:27.000Z | dts_test_project/dts_test_app/migrations/0001_initial.py | pvandegeer/django-tenant-schemas | 20c72782cee51a33fd5c56a0af7b2c653c1b6770 | [
"MIT"
] | 3 | 2018-08-14T07:37:08.000Z | 2021-04-24T07:52:00.000Z | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import models, migrations
from django.conf import settings
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='DummyModel',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('name', models.CharField(max_length=100)),
],
options={
},
bases=(models.Model,),
),
migrations.CreateModel(
name='ModelWithFkToPublicUser',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('user', models.ForeignKey(to=settings.AUTH_USER_MODEL)),
],
options={
},
bases=(models.Model,),
),
]
| 28.944444 | 114 | 0.56238 |
from __future__ import unicode_literals
from django.db import models, migrations
from django.conf import settings
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
migrations.CreateModel(
name='DummyModel',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('name', models.CharField(max_length=100)),
],
options={
},
bases=(models.Model,),
),
migrations.CreateModel(
name='ModelWithFkToPublicUser',
fields=[
('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),
('user', models.ForeignKey(to=settings.AUTH_USER_MODEL)),
],
options={
},
bases=(models.Model,),
),
]
| true | true |
f7258581d63de21fec44899869d8d610223ca22b | 2,803 | py | Python | src/redgrease/cluster.py | jsam/redgrease | 245755b34bce287c63abb6624478cdf8189816b6 | [
"MIT"
] | null | null | null | src/redgrease/cluster.py | jsam/redgrease | 245755b34bce287c63abb6624478cdf8189816b6 | [
"MIT"
] | null | null | null | src/redgrease/cluster.py | jsam/redgrease | 245755b34bce287c63abb6624478cdf8189816b6 | [
"MIT"
] | null | null | null | from typing import Callable, Dict
import rediscluster
import rediscluster.exceptions
import redgrease.client
import redgrease.data
import redgrease.utils
class RedisCluster(rediscluster.RedisCluster):
"""RedisCluster client class, with support for gears features
Behaves exactly like the rediscluster.RedisCluster client, but is extended with
a 'gears' property fo executiong Gears commands.
Attributes:
gears (redgrease.client.Gears):
Gears command client.
"""
# States how target node is selected for cluster commands:
# - blocked : command is not allowed - Raises a RedisClusterException
# - random : excuted on one randomly selected node
# - all-masters : executed on all master node
# - all-nodes : executed on all nodes
# - slot-id : executed on the node defined by the second argument
NODES_FLAGS = {
**rediscluster.RedisCluster.NODES_FLAGS,
**{
"RG.INFOCLUSTER": "random",
"RG.PYSTATS": "all-nodes",
"RG.PYDUMPREQS": "random",
"RG.REFRESHCLUSTER": "all-nodes",
"RG.DUMPEXECUTIONS": "random",
"RG.DUMPREGISTRATIONS": "random",
},
}
# Not to be confused with redis.Redis.RESPONSE_CALLBACKS
# RESULT_CALLBACKS is special to rediscluster.RedisCluster.
# It decides how results of commands defined in `NODES_FLAGS` are aggregated into
# a final response, **after** redis.Redis.RESPONSE_CALLBACKS as been applied to
# each response individually.
RESULT_CALLBACKS = {
**rediscluster.RedisCluster.RESULT_CALLBACKS,
**{
"RG.INFOCLUSTER": lambda _, res: next(iter(res.values())),
"RG.PYSTATS": lambda _, res: res,
"RG.PYDUMPREQS": lambda _, res: next(iter(res.values())),
"RG.REFRESHCLUSTER": lambda _, res: all(res.values()),
},
}
RESPONSE_CALLBACKS: Dict[str, Callable] = {
**rediscluster.RedisCluster.RESPONSE_CALLBACKS,
**redgrease.client.Gears.RESPONSE_CALLBACKS,
}
def __init__(self, *args, **kwargs):
"""Instantiate a redis cluster client, with gears features"""
self._gears = None
self.connection = None
super().__init__(*args, **kwargs)
@property
def gears(self) -> redgrease.client.Gears:
"""Gears client, exposing gears commands
Returns:
Gears:
Gears client
"""
if not self._gears:
self._gears = redgrease.client.Gears(self)
return self._gears
def RedisGears(*args, **kwargs):
try:
return RedisCluster(*args, **kwargs)
except (AttributeError, rediscluster.exceptions.RedisClusterException):
return redgrease.client.Redis(*args, **kwargs)
| 32.593023 | 85 | 0.642526 | from typing import Callable, Dict
import rediscluster
import rediscluster.exceptions
import redgrease.client
import redgrease.data
import redgrease.utils
class RedisCluster(rediscluster.RedisCluster):
NODES_FLAGS = {
**rediscluster.RedisCluster.NODES_FLAGS,
**{
"RG.INFOCLUSTER": "random",
"RG.PYSTATS": "all-nodes",
"RG.PYDUMPREQS": "random",
"RG.REFRESHCLUSTER": "all-nodes",
"RG.DUMPEXECUTIONS": "random",
"RG.DUMPREGISTRATIONS": "random",
},
}
RESULT_CALLBACKS = {
**rediscluster.RedisCluster.RESULT_CALLBACKS,
**{
"RG.INFOCLUSTER": lambda _, res: next(iter(res.values())),
"RG.PYSTATS": lambda _, res: res,
"RG.PYDUMPREQS": lambda _, res: next(iter(res.values())),
"RG.REFRESHCLUSTER": lambda _, res: all(res.values()),
},
}
RESPONSE_CALLBACKS: Dict[str, Callable] = {
**rediscluster.RedisCluster.RESPONSE_CALLBACKS,
**redgrease.client.Gears.RESPONSE_CALLBACKS,
}
def __init__(self, *args, **kwargs):
self._gears = None
self.connection = None
super().__init__(*args, **kwargs)
@property
def gears(self) -> redgrease.client.Gears:
if not self._gears:
self._gears = redgrease.client.Gears(self)
return self._gears
def RedisGears(*args, **kwargs):
try:
return RedisCluster(*args, **kwargs)
except (AttributeError, rediscluster.exceptions.RedisClusterException):
return redgrease.client.Redis(*args, **kwargs)
| true | true |
f72587485c282a74d27d67de475830aad98af691 | 1,316 | py | Python | elpips/util.py | niopeng/elpips | 385012a2ee614c75a1631546c391039af85744f4 | [
"BSD-2-Clause"
] | 88 | 2019-06-13T10:42:26.000Z | 2022-03-30T07:36:20.000Z | elpips/util.py | niopeng/elpips | 385012a2ee614c75a1631546c391039af85744f4 | [
"BSD-2-Clause"
] | 4 | 2019-11-13T23:11:33.000Z | 2021-07-21T11:04:08.000Z | elpips/util.py | niopeng/elpips | 385012a2ee614c75a1631546c391039af85744f4 | [
"BSD-2-Clause"
] | 18 | 2019-06-10T16:31:10.000Z | 2022-01-04T03:48:57.000Z | import tensorflow as tf
import numpy as np
def switch_case_cond(cases, default_case):
if cases:
condition, effect = cases[0]
return tf.cond(condition, effect, lambda: switch_case_cond(cases[1:], default_case))
return default_case()
def switch_case_where(cases, default_case):
if cases:
condition, effect = cases[0]
return tf.where(condition, effect, switch_case_where(cases[1:], default_case))
return default_case
def np_dtype(tf_dtype):
if tf_dtype == tf.float32:
return np.float32
if tf_dtype == tf.float64:
return np.float64
raise Exception('Unsupported dtype')
def f32_to_dtype(x, dtype):
if dtype == tf.float32:
return x
return tf.cast(x, dtype)
def as_tuple(x):
'''Formats x as a tuple. If x is already a tuple returns it as is, otherwise returns a 1-tuple containing x.'''
if isinstance(x, tuple):
return x
else:
return (x,)
def for_each(x, func):
'''Runs 'func' for x, or each item of x if x is a tuple. Returns the results in the same format.'''
if isinstance(x, tuple):
return tuple((func(s) for s in x))
else:
return func(x)
def for_tuple(x, func):
'''Runs 'func' for as_tuple(x). Returns the results in the original format. Assumes 'func' returns tuple when given tuple.'''
if isinstance(x, tuple):
return func(x)
else:
return func(as_tuple(x))[0]
| 25.803922 | 126 | 0.715046 | import tensorflow as tf
import numpy as np
def switch_case_cond(cases, default_case):
if cases:
condition, effect = cases[0]
return tf.cond(condition, effect, lambda: switch_case_cond(cases[1:], default_case))
return default_case()
def switch_case_where(cases, default_case):
if cases:
condition, effect = cases[0]
return tf.where(condition, effect, switch_case_where(cases[1:], default_case))
return default_case
def np_dtype(tf_dtype):
if tf_dtype == tf.float32:
return np.float32
if tf_dtype == tf.float64:
return np.float64
raise Exception('Unsupported dtype')
def f32_to_dtype(x, dtype):
if dtype == tf.float32:
return x
return tf.cast(x, dtype)
def as_tuple(x):
if isinstance(x, tuple):
return x
else:
return (x,)
def for_each(x, func):
if isinstance(x, tuple):
return tuple((func(s) for s in x))
else:
return func(x)
def for_tuple(x, func):
if isinstance(x, tuple):
return func(x)
else:
return func(as_tuple(x))[0]
| true | true |
f72587bcc4673fe9fd1a8a3ffec34ab4d833f88b | 5,113 | py | Python | src/estimagic/benchmarking/run_benchmark.py | PaulBehler/estimagic | c14f743986262d508e55738c90737cb504fe987b | [
"MIT"
] | 7 | 2019-05-11T07:19:46.000Z | 2019-05-31T07:03:13.000Z | src/estimagic/benchmarking/run_benchmark.py | PaulBehler/estimagic | c14f743986262d508e55738c90737cb504fe987b | [
"MIT"
] | 14 | 2019-05-04T14:15:52.000Z | 2019-06-10T11:45:27.000Z | src/estimagic/benchmarking/run_benchmark.py | PaulBehler/estimagic | c14f743986262d508e55738c90737cb504fe987b | [
"MIT"
] | 1 | 2019-05-21T08:44:37.000Z | 2019-05-21T08:44:37.000Z | """Functions to create, run and visualize optimization benchmarks.
TO-DO:
- Add other benchmark sets:
- finish medium scale problems from https://arxiv.org/pdf/1710.11005.pdf, Page 34.
- add scalar problems from https://github.com/AxelThevenot
- Add option for deterministic noise or wiggle.
"""
from pathlib import Path
import numpy as np
from estimagic import batch_evaluators
from estimagic.logging.read_log import read_optimization_histories
from estimagic.optimization.optimize import minimize
def run_benchmark(
problems,
optimize_options,
logging_directory,
*,
batch_evaluator="joblib",
n_cores=1,
error_handling="continue",
fast_logging=True,
seed=None,
):
"""Run problems with different optimize options.
Args:
problems (dict): Nested dictionary with benchmark problems of the structure:
{"name": {"inputs": {...}, "solution": {...}, "info": {...}}}
where "inputs" are keyword arguments for ``minimize`` such as the criterion
function and start parameters. "solution" contains the entries "params" and
"value" and "info" might contain information about the test problem.
optimize_options (list or dict): Either a list of algorithms or a Nested
dictionary that maps a name for optimizer settings
(e.g. ``"lbfgsb_strict_criterion"``) to a dictionary of keyword arguments
for arguments for ``minimize`` (e.g. ``{"algorithm": "scipy_lbfgsb",
"algo_options": {"convergence.relative_criterion_tolerance": 1e-12}}``).
Alternatively, the values can just be an algorithm which is then benchmarked
at default settings.
batch_evaluator (str or callable): See :ref:`batch_evaluators`.
logging_directory (pathlib.Path): Directory in which the log databases are
saved.
n_cores (int): Number of optimizations that is run in parallel. Note that in
addition to that an optimizer might parallelize.
error_handling (str): One of "raise", "continue".
fast_logging (bool): Whether the slightly unsafe but much faster database
configuration is chosen.
Returns:
dict: Nested Dictionary with information on the benchmark run. The outer keys
are tuples where the first entry is the name of the problem and the second
the name of the optimize options. The values are dicts with the entries:
"runtime", "params_history", "criterion_history", "solution"
"""
np.random.seed(seed)
logging_directory = Path(logging_directory)
logging_directory.mkdir(parents=True, exist_ok=True)
if isinstance(batch_evaluator, str):
batch_evaluator = getattr(
batch_evaluators, f"{batch_evaluator}_batch_evaluator"
)
opt_options = _process_optimize_options(optimize_options)
log_options = {"fast_logging": fast_logging, "if_table_exists": "replace"}
kwargs_list = []
names = []
for prob_name, problem in problems.items():
for option_name, options in opt_options.items():
kwargs = {
**options,
**problem["inputs"],
"logging": logging_directory / f"{prob_name}_{option_name}.db",
"log_options": log_options,
}
kwargs_list.append(kwargs)
names.append((prob_name, option_name))
log_paths = [kwargs["logging"] for kwargs in kwargs_list]
raw_results = batch_evaluator(
func=minimize,
arguments=kwargs_list,
n_cores=n_cores,
error_handling=error_handling,
unpack_symbol="**",
)
results = {}
for name, result, log_path in zip(names, raw_results, log_paths):
histories = read_optimization_histories(log_path)
stop = histories["metadata"]["timestamps"].max()
start = histories["metadata"]["timestamps"].min()
runtime = (stop - start).total_seconds()
results[name] = {
"params_history": histories["params"],
"criterion_history": histories["values"],
"time_history": histories["metadata"]["timestamps"] - start,
"solution": result,
"runtime": runtime,
}
return results
def _process_optimize_options(raw_options):
if not isinstance(raw_options, dict):
dict_options = {}
for option in raw_options:
if isinstance(option, str):
dict_options[option] = option
else:
dict_options[option.__name__] = option
else:
dict_options = raw_options
out_options = {}
for name, option in dict_options.items():
if not isinstance(option, dict):
option = {"algorithm": option}
if "log_options" in option:
raise ValueError(
"Log options cannot be specified as part of optimize_options. Logging "
"behavior is configured by the run_benchmark function."
)
out_options[name] = option
return out_options
| 37.050725 | 88 | 0.643067 | from pathlib import Path
import numpy as np
from estimagic import batch_evaluators
from estimagic.logging.read_log import read_optimization_histories
from estimagic.optimization.optimize import minimize
def run_benchmark(
problems,
optimize_options,
logging_directory,
*,
batch_evaluator="joblib",
n_cores=1,
error_handling="continue",
fast_logging=True,
seed=None,
):
np.random.seed(seed)
logging_directory = Path(logging_directory)
logging_directory.mkdir(parents=True, exist_ok=True)
if isinstance(batch_evaluator, str):
batch_evaluator = getattr(
batch_evaluators, f"{batch_evaluator}_batch_evaluator"
)
opt_options = _process_optimize_options(optimize_options)
log_options = {"fast_logging": fast_logging, "if_table_exists": "replace"}
kwargs_list = []
names = []
for prob_name, problem in problems.items():
for option_name, options in opt_options.items():
kwargs = {
**options,
**problem["inputs"],
"logging": logging_directory / f"{prob_name}_{option_name}.db",
"log_options": log_options,
}
kwargs_list.append(kwargs)
names.append((prob_name, option_name))
log_paths = [kwargs["logging"] for kwargs in kwargs_list]
raw_results = batch_evaluator(
func=minimize,
arguments=kwargs_list,
n_cores=n_cores,
error_handling=error_handling,
unpack_symbol="**",
)
results = {}
for name, result, log_path in zip(names, raw_results, log_paths):
histories = read_optimization_histories(log_path)
stop = histories["metadata"]["timestamps"].max()
start = histories["metadata"]["timestamps"].min()
runtime = (stop - start).total_seconds()
results[name] = {
"params_history": histories["params"],
"criterion_history": histories["values"],
"time_history": histories["metadata"]["timestamps"] - start,
"solution": result,
"runtime": runtime,
}
return results
def _process_optimize_options(raw_options):
if not isinstance(raw_options, dict):
dict_options = {}
for option in raw_options:
if isinstance(option, str):
dict_options[option] = option
else:
dict_options[option.__name__] = option
else:
dict_options = raw_options
out_options = {}
for name, option in dict_options.items():
if not isinstance(option, dict):
option = {"algorithm": option}
if "log_options" in option:
raise ValueError(
"Log options cannot be specified as part of optimize_options. Logging "
"behavior is configured by the run_benchmark function."
)
out_options[name] = option
return out_options
| true | true |
f725895b361675e864ed1a5ce6a8bc79a831c085 | 6,172 | py | Python | Data_Handling/Data_analyze.py | KristofferLM96/TsetlinMachine-GO | 926091fc70042abe5a67230932398bdab2c46328 | [
"MIT"
] | 2 | 2020-02-27T16:22:08.000Z | 2020-03-22T11:04:35.000Z | Data_Handling/Data_analyze.py | KristofferLM96/TsetlinMachine-GO | 926091fc70042abe5a67230932398bdab2c46328 | [
"MIT"
] | null | null | null | Data_Handling/Data_analyze.py | KristofferLM96/TsetlinMachine-GO | 926091fc70042abe5a67230932398bdab2c46328 | [
"MIT"
] | null | null | null | # -----------------------------------------------
# ................. LIBRARIES ...................
# -----------------------------------------------
import glob
import os
import time
import numpy as np
# -----------------------------------------------
# ............. GLOBAL VARIABLES ................
# -----------------------------------------------
name = "100_9x9Aya" # 9x9Natsukaze || 9x9Aya || x_9x9Aya .. x = amount moves
file_name = name + "_binary.txt"
binary_path = "Data/Binary/" + file_name
original_path = "/home/kristoffer/Documents/Data/Original/9x9_10k_r104_144x20k/*"
encoding = "UTF-8" # ISO-8859-1 / UTF-8
multiple_files = True
unique_list = []
original_list = []
# [check_handicap(), check_duplication(), get_result_ratio(), check_moves(), remove_empty_lines()]
run_programs = [0, 0, 1, 0, 0]
# -----------------------------------------------
# ................. FUNCTIONS ...................
# -----------------------------------------------
def remove_empty_lines():
output_file = open("Data/Binary/" + name + "_binary_1.txt", "w+")
with open(binary_path, "r") as file:
for line in file:
if not line.isspace():
output_file.write(line)
def check_handicap():
file = open(original_path, 'r', encoding=encoding)
file_lines = file.readlines()
_handicap = file_lines[0].split("HA[")
print(_handicap)
handicap = _handicap[1][0]
print(handicap)
file.close()
def check_duplication():
file = open(binary_path, 'r', encoding=encoding)
global original_list
global unique_list
original_list = [line.strip() for line in file]
print("Original List Length:", len(original_list))
original_length = len(original_list)
unique_list = np.unique(original_list)
unique_length = len(unique_list)
print("Unique List Length:", unique_length)
print("Original - Unique:", original_length - unique_length, "\n")
file.close()
def get_result_ratio():
win = open("Data/Results-Split/" + name + "_win.txt", 'r')
loss = open("Data/Results-Split/" + name + "_loss.txt", 'r')
draw = open("Data/Results-Split/" + name + "_draw.txt", 'r')
win_amount = len(win.readlines())
loss_amount = len(loss.readlines())
draw_amount = len(draw.readlines())
total_amount = win_amount + loss_amount + draw_amount
print("Total Amount:", total_amount)
print("Amount of wins:", win_amount, ",", round(((win_amount * 100) / total_amount), 2), "%")
print("Amount of loss:", loss_amount, ",", round(((loss_amount * 100) / total_amount), 2), "%")
print("Amount of draw:", draw_amount, ",", round(((draw_amount * 100) / total_amount), 2), "%")
win.close()
loss.close()
draw.close()
def check_moves():
total_pos = 19
moves_list = []
def get_moves(_game_lines):
if "HA[" in _game_lines[0]:
handicap = int(_game_lines[0].split("HA[")[1][0])
else:
handicap = 0
_move_list = []
const = 4
for row in _game_lines[1:-1]:
x = translate(row[3])
y = translate(row[4])
if row[1] + row[2] == "AB":
for i in range(handicap):
x = translate(row[4 + (i * const)])
y = translate(row[5 + (i * const)])
_move = ["b", x, y]
if x != total_pos and y != total_pos:
_move_list.append(_move)
else:
if row[1] == "B":
_move = ["b", x, y]
if row[1] == "W":
_move = ["w", x, y]
if x != total_pos and y != total_pos:
_move_list.append(_move)
return _move_list
def translate(i):
if i == "a":
return 0
if i == "b":
return 1
if i == "c":
return 2
if i == "d":
return 3
if i == "e":
return 4
if i == "f":
return 5
if i == "g":
return 6
if i == "h":
return 7
if i == "i":
return 8
if i == "j":
return 9
if i == "k":
return 10
if i == "l":
return 11
if i == "m":
return 12
if i == "n":
return 13
if i == "o":
return 14
if i == "p":
return 15
if i == "q":
return 16
if i == "r":
return 17
if i == "s":
return 18
if i == "t":
return 19
counter = 1
total_files = len(glob.glob(os.path.join(original_path, '*.sgf')))
for infile in glob.glob(os.path.join(original_path, '*.sgf')):
start_time = time.time()
file = open(infile, 'r', encoding="ISO-8859-1")
file_lines = file.readlines()
moves_list.append(len(get_moves(file_lines)))
print(infile)
print("Getting moves from file ", counter, "out of", total_files,
"files. ............................................... ",
round((counter / total_files * 100), 2), "% ............................................... ",
round((time.time() - start_time) * 1000, 2), "ms", "\n")
counter = counter + 1
file.close()
unique_moves_list, unique_moves_list_count = np.unique(moves_list, return_counts=True)
print(unique_moves_list, "\n")
print(unique_moves_list_count, "\n")
total_data = sum(unique_moves_list_count)
for x, y in np.nditer([unique_moves_list, unique_moves_list_count]):
print("Moves: %d : Amount: %d, %d %%" % (int(x), int(y), ((int(y)*100)/total_data)))
print("\n")
print("Unique Move lengths:", len(unique_moves_list))
# -----------------------------------------------
# .................. MAIN .......................
# -----------------------------------------------
if run_programs[0]:
check_handicap()
if run_programs[1]:
check_duplication()
if run_programs[2]:
get_result_ratio()
if run_programs[3]:
check_moves()
if run_programs[4]:
remove_empty_lines()
| 31.814433 | 108 | 0.487362 |
import glob
import os
import time
import numpy as np
name = "100_9x9Aya"
file_name = name + "_binary.txt"
binary_path = "Data/Binary/" + file_name
original_path = "/home/kristoffer/Documents/Data/Original/9x9_10k_r104_144x20k/*"
encoding = "UTF-8"
multiple_files = True
unique_list = []
original_list = []
run_programs = [0, 0, 1, 0, 0]
def remove_empty_lines():
output_file = open("Data/Binary/" + name + "_binary_1.txt", "w+")
with open(binary_path, "r") as file:
for line in file:
if not line.isspace():
output_file.write(line)
def check_handicap():
file = open(original_path, 'r', encoding=encoding)
file_lines = file.readlines()
_handicap = file_lines[0].split("HA[")
print(_handicap)
handicap = _handicap[1][0]
print(handicap)
file.close()
def check_duplication():
file = open(binary_path, 'r', encoding=encoding)
global original_list
global unique_list
original_list = [line.strip() for line in file]
print("Original List Length:", len(original_list))
original_length = len(original_list)
unique_list = np.unique(original_list)
unique_length = len(unique_list)
print("Unique List Length:", unique_length)
print("Original - Unique:", original_length - unique_length, "\n")
file.close()
def get_result_ratio():
win = open("Data/Results-Split/" + name + "_win.txt", 'r')
loss = open("Data/Results-Split/" + name + "_loss.txt", 'r')
draw = open("Data/Results-Split/" + name + "_draw.txt", 'r')
win_amount = len(win.readlines())
loss_amount = len(loss.readlines())
draw_amount = len(draw.readlines())
total_amount = win_amount + loss_amount + draw_amount
print("Total Amount:", total_amount)
print("Amount of wins:", win_amount, ",", round(((win_amount * 100) / total_amount), 2), "%")
print("Amount of loss:", loss_amount, ",", round(((loss_amount * 100) / total_amount), 2), "%")
print("Amount of draw:", draw_amount, ",", round(((draw_amount * 100) / total_amount), 2), "%")
win.close()
loss.close()
draw.close()
def check_moves():
total_pos = 19
moves_list = []
def get_moves(_game_lines):
if "HA[" in _game_lines[0]:
handicap = int(_game_lines[0].split("HA[")[1][0])
else:
handicap = 0
_move_list = []
const = 4
for row in _game_lines[1:-1]:
x = translate(row[3])
y = translate(row[4])
if row[1] + row[2] == "AB":
for i in range(handicap):
x = translate(row[4 + (i * const)])
y = translate(row[5 + (i * const)])
_move = ["b", x, y]
if x != total_pos and y != total_pos:
_move_list.append(_move)
else:
if row[1] == "B":
_move = ["b", x, y]
if row[1] == "W":
_move = ["w", x, y]
if x != total_pos and y != total_pos:
_move_list.append(_move)
return _move_list
def translate(i):
if i == "a":
return 0
if i == "b":
return 1
if i == "c":
return 2
if i == "d":
return 3
if i == "e":
return 4
if i == "f":
return 5
if i == "g":
return 6
if i == "h":
return 7
if i == "i":
return 8
if i == "j":
return 9
if i == "k":
return 10
if i == "l":
return 11
if i == "m":
return 12
if i == "n":
return 13
if i == "o":
return 14
if i == "p":
return 15
if i == "q":
return 16
if i == "r":
return 17
if i == "s":
return 18
if i == "t":
return 19
counter = 1
total_files = len(glob.glob(os.path.join(original_path, '*.sgf')))
for infile in glob.glob(os.path.join(original_path, '*.sgf')):
start_time = time.time()
file = open(infile, 'r', encoding="ISO-8859-1")
file_lines = file.readlines()
moves_list.append(len(get_moves(file_lines)))
print(infile)
print("Getting moves from file ", counter, "out of", total_files,
"files. ............................................... ",
round((counter / total_files * 100), 2), "% ............................................... ",
round((time.time() - start_time) * 1000, 2), "ms", "\n")
counter = counter + 1
file.close()
unique_moves_list, unique_moves_list_count = np.unique(moves_list, return_counts=True)
print(unique_moves_list, "\n")
print(unique_moves_list_count, "\n")
total_data = sum(unique_moves_list_count)
for x, y in np.nditer([unique_moves_list, unique_moves_list_count]):
print("Moves: %d : Amount: %d, %d %%" % (int(x), int(y), ((int(y)*100)/total_data)))
print("\n")
print("Unique Move lengths:", len(unique_moves_list))
if run_programs[0]:
check_handicap()
if run_programs[1]:
check_duplication()
if run_programs[2]:
get_result_ratio()
if run_programs[3]:
check_moves()
if run_programs[4]:
remove_empty_lines()
| true | true |
f725898cfbcf257a6620878c85d91d75a4f7b6bd | 11,001 | py | Python | SBaaS_rnasequencing/stage01_rnasequencing_genesCountTable_io.py | dmccloskey/SBaaS_rnasequencing | 521ad0b671b0bca02e9cebfc1b372f2265955418 | [
"MIT"
] | null | null | null | SBaaS_rnasequencing/stage01_rnasequencing_genesCountTable_io.py | dmccloskey/SBaaS_rnasequencing | 521ad0b671b0bca02e9cebfc1b372f2265955418 | [
"MIT"
] | null | null | null | SBaaS_rnasequencing/stage01_rnasequencing_genesCountTable_io.py | dmccloskey/SBaaS_rnasequencing | 521ad0b671b0bca02e9cebfc1b372f2265955418 | [
"MIT"
] | null | null | null | #system
import json
#sbaas
from .stage01_rnasequencing_genesCountTable_query import stage01_rnasequencing_genesCountTable_query
from .stage01_rnasequencing_analysis_query import stage01_rnasequencing_analysis_query
from SBaaS_base.sbaas_template_io import sbaas_template_io
# Resources
from io_utilities.base_importData import base_importData
from io_utilities.base_exportData import base_exportData
from sequencing_analysis.genes_countFPKMattr_table import genes_countFPKMattr_table
from ddt_python.ddt_container_filterMenuAndChart2dAndTable import ddt_container_filterMenuAndChart2dAndTable
from ddt_python.ddt_container import ddt_container
from listDict.listDict import listDict
from math import log2
class stage01_rnasequencing_genesCountTable_io(
stage01_rnasequencing_genesCountTable_query,
stage01_rnasequencing_analysis_query,
sbaas_template_io):
def import_dataStage01RNASequencingGenesCountTable_add(
self,genes_count_table_dir,genes_fpkm_table_dir,
genes_attr_table_dir,
analysis_id_I,experiment_ids_I,samples_host_dirs_I,sample_names_I):
'''table adds'''
countFPKMattr = genes_countFPKMattr_table();
countFPKMattr.import_countTable(
filename_I=genes_count_table_dir,);
countFPKMattr.import_fpkmTable(
filename_I=genes_fpkm_table_dir,);
countFPKMattr.import_attrTable(
filename_I=genes_attr_table_dir,);
#parse the filenames and samplenames
sna2sns_I={};
sna2experimentID_I={};
sample_names_lst = sample_names_I.split(',');
experiment_ids_lst = experiment_ids_I.split(',');
for cnt,sample_replicates in enumerate(samples_host_dirs_I.split('|')):
sna2sns_I[sample_names_lst[cnt]] = [];
sna2experimentID_I[sample_names_lst[cnt]] = experiment_ids_lst[cnt];
for sample in sample_replicates.split(','):
filename = sample.split('/')[-1].replace('.bam','').replace('.fastq','');
sna2sns_I[sample_names_lst[cnt]].append(filename);
genesCountTable = countFPKMattr.alignAndReformat_countFPKMattrTables(
analysis_id_I = analysis_id_I,
sna2experimentID_I = sna2experimentID_I,
sna2sns_I = sna2sns_I)
self.add_dataStage01RNASequencingGenesCountTable(genesCountTable);
def import_dataStage01RNASequencingGenesCountTable_update(self, filename):
'''table adds'''
data = base_importData();
data.read_csv(filename);
data.format_data();
self.update_dataStage01RNASequencingGenesCountTable(data.data);
data.clear_data();
def export_dataStage01RNASequencingGenesCountTable_js(self,analysis_id_I,data_dir_I='tmp'):
'''Export data for a box and whiskers plot'''
# get the analysis information
experiment_ids,sample_names = [],[];
experiment_ids,sample_names = self.get_experimentIDAndSampleName_analysisID_dataStage01RNASequencingAnalysis(analysis_id_I);
data_O = [];
for sample_name_cnt,sample_name in enumerate(sample_names):
# query fpkm data:
fpkms = [];
fpkms = self.get_rows_experimentIDAndSampleName_dataStage01RNASequencingGenesCountTable(experiment_ids[sample_name_cnt],sample_name);
data_O.extend(fpkms);
# dump chart parameters to a js files
data1_keys = ['experiment_id','sample_name','gene_short_name'
];
data1_nestkeys = ['gene_short_name'];
data1_keymap = {'xdata':'gene_short_name',
'ydatamean':'FPKM',
'ydatalb':'FPKM_conf_lo',
'ydataub':'FPKM_conf_hi',
'serieslabel':'sample_name',
'featureslabel':'gene_short_name'};
# make the data object
dataobject_O = [{"data":data_O,"datakeys":data1_keys,"datanestkeys":data1_nestkeys}];
# make the tile parameter objects
formtileparameters_O = {'tileheader':'Filter menu','tiletype':'html','tileid':"filtermenu1",'rowid':"row1",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-4"};
formparameters_O = {'htmlid':'filtermenuform1',"htmltype":'form_01',"formsubmitbuttonidtext":{'id':'submit1','text':'submit'},"formresetbuttonidtext":{'id':'reset1','text':'reset'},"formupdatebuttonidtext":{'id':'update1','text':'update'}};
formtileparameters_O.update(formparameters_O);
svgparameters_O = {"svgtype":'boxandwhiskersplot2d_02',"svgkeymap":[data1_keymap],
'svgid':'svg1',
"svgmargin":{ 'top': 50, 'right': 150, 'bottom': 50, 'left': 50 },
"svgwidth":500,"svgheight":350,
"svgx1axislabel":"gene","svgy1axislabel":"FPKM",
'svgformtileid':'filtermenu1','svgresetbuttonid':'reset1','svgsubmitbuttonid':'submit1'};
svgtileparameters_O = {'tileheader':'Custom box and whiskers plot','tiletype':'svg','tileid':"tile2",'rowid':"row1",'colid':"col2",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-8"};
svgtileparameters_O.update(svgparameters_O);
tableparameters_O = {"tabletype":'responsivetable_01',
'tableid':'table1',
"tablefilters":None,
"tableclass":"table table-condensed table-hover",
'tableformtileid':'filtermenu1','tableresetbuttonid':'reset1','tablesubmitbuttonid':'submit1'};
tabletileparameters_O = {'tileheader':'FPKM','tiletype':'table','tileid':"tile3",'rowid':"row2",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-12"};
tabletileparameters_O.update(tableparameters_O);
parametersobject_O = [formtileparameters_O,svgtileparameters_O,tabletileparameters_O];
tile2datamap_O = {"filtermenu1":[0],"tile2":[0],"tile3":[0]};
# dump the data to a json file
ddtutilities = ddt_container(parameters_I = parametersobject_O,data_I = dataobject_O,tile2datamap_I = tile2datamap_O,filtermenu_I = None);
if data_dir_I=='tmp':
filename_str = self.settings['visualization_data'] + '/tmp/ddt_data.js'
elif data_dir_I=='data_json':
data_json_O = ddtutilities.get_allObjects_js();
return data_json_O;
with open(filename_str,'w') as file:
file.write(ddtutilities.get_allObjects());
def export_dataStage01RNASequencingGenesCountTable_pairWisePlot_js(self,analysis_id_I,log2normalization_I=True,data_dir_I='tmp'):
'''Export data for a pairwise scatter plot
INPUT:
analysis_id = String, analysis_id
log2normalization_I = Boolean, apply a log2 normalization the FPKM values (default: True)
data_dir_I = string, data directory
OUTPUT:
'''
# get the analysis information
experiment_ids,sample_names = [],[];
experiment_ids,sample_names = self.get_experimentIDAndSampleName_analysisID_dataStage01RNASequencingAnalysis(analysis_id_I);
data_O = [];
for sample_name_cnt,sample_name in enumerate(sample_names):
# query fpkm data:
fpkms = [];
fpkms = self.get_rows_experimentIDAndSampleName_dataStage01RNASequencingGenesCountTable(experiment_ids[sample_name_cnt],sample_name);
if log2normalization_I:
for f in fpkms:
if f['FPKM'] == 0.0: f['FPKM'] = 0.0;
else: f['FPKM'] = log2(f['FPKM']);
data_O.extend(fpkms);
# reorganize the data
listdict = listDict(data_O);
data_O,columnValueHeader_O = listdict.convert_listDict2ColumnGroupListDict(
#value_labels_I = ['FPKM','FPKM_conf_lo','FPKM_conf_hi'],
value_labels_I = ['FPKM',],
column_labels_I = ['experiment_id','sample_name'],
feature_labels_I = ['gene_id','gene_short_name'],
na_str_I=0.0,
columnValueConnector_str_I='_',
);
# make the tile object
#data1 = filtermenu/table
data1_keymap_table = {
'xdata':'svd_method',
'ydata':'singular_value_index',
'zdata':'d_vector',
'rowslabel':'svd_method',
'columnslabel':'singular_value_index',
};
#data2 = svg
#if single plot, data2 = filter menu, data2, and table
data1_keys = ['gene_id','gene_short_name'
];
data1_nestkeys = ['gene_short_name'];
data1_keymap_svg = [];
svgtype = [];
svgtile2datamap = [];
data_svg_keymap = [];
for cnt1,column1 in enumerate(columnValueHeader_O):
for cnt2,column2 in enumerate(columnValueHeader_O[cnt1+1:]):
keymap = {
'xdata':column1,
'ydata':column2,
'serieslabel':'',
'featureslabel':'gene_short_name',
'tooltipdata':'gene_short_name',
};
data1_keymap_svg.append([keymap]);
data_svg_keymap.append(keymap);
svgtype.append('pcaplot2d_scores_01');
svgtile2datamap.append([0]);
nsvgtable = ddt_container_filterMenuAndChart2dAndTable();
nsvgtable.make_filterMenuAndChart2dAndTable(
data_filtermenu=data_O,
data_filtermenu_keys=data1_keys,
data_filtermenu_nestkeys=data1_nestkeys,
data_filtermenu_keymap=data1_keymap_table,
data_svg_keys=data1_keys,
data_svg_nestkeys=data1_nestkeys,
data_svg_keymap=data_svg_keymap,
data_table_keys=data1_keys,
data_table_nestkeys=data1_nestkeys,
data_table_keymap=data1_keymap_table,
data_svg=None,
data_table=None,
svgtype=svgtype,
tabletype='responsivetable_01',
svgx1axislabel='',
svgy1axislabel='',
tablekeymap = [data1_keymap_table],
svgkeymap = data1_keymap_svg,
formtile2datamap=[0],
tabletile2datamap=[0],
svgtile2datamap=svgtile2datamap,
svgfilters=None,
svgtileheader='Pair-wise scatter plot',
tablefilters=None,
tableheaders=None
);
if data_dir_I=='tmp':
filename_str = self.settings['visualization_data'] + '/tmp/ddt_data.js'
elif data_dir_I=='data_json':
data_json_O = nsvgtable.get_allObjects_js();
return data_json_O;
with open(filename_str,'w') as file:
file.write(nsvgtable.get_allObjects()); | 50.463303 | 248 | 0.634942 |
import json
from .stage01_rnasequencing_genesCountTable_query import stage01_rnasequencing_genesCountTable_query
from .stage01_rnasequencing_analysis_query import stage01_rnasequencing_analysis_query
from SBaaS_base.sbaas_template_io import sbaas_template_io
from io_utilities.base_importData import base_importData
from io_utilities.base_exportData import base_exportData
from sequencing_analysis.genes_countFPKMattr_table import genes_countFPKMattr_table
from ddt_python.ddt_container_filterMenuAndChart2dAndTable import ddt_container_filterMenuAndChart2dAndTable
from ddt_python.ddt_container import ddt_container
from listDict.listDict import listDict
from math import log2
class stage01_rnasequencing_genesCountTable_io(
stage01_rnasequencing_genesCountTable_query,
stage01_rnasequencing_analysis_query,
sbaas_template_io):
def import_dataStage01RNASequencingGenesCountTable_add(
self,genes_count_table_dir,genes_fpkm_table_dir,
genes_attr_table_dir,
analysis_id_I,experiment_ids_I,samples_host_dirs_I,sample_names_I):
countFPKMattr = genes_countFPKMattr_table();
countFPKMattr.import_countTable(
filename_I=genes_count_table_dir,);
countFPKMattr.import_fpkmTable(
filename_I=genes_fpkm_table_dir,);
countFPKMattr.import_attrTable(
filename_I=genes_attr_table_dir,);
sna2sns_I={};
sna2experimentID_I={};
sample_names_lst = sample_names_I.split(',');
experiment_ids_lst = experiment_ids_I.split(',');
for cnt,sample_replicates in enumerate(samples_host_dirs_I.split('|')):
sna2sns_I[sample_names_lst[cnt]] = [];
sna2experimentID_I[sample_names_lst[cnt]] = experiment_ids_lst[cnt];
for sample in sample_replicates.split(','):
filename = sample.split('/')[-1].replace('.bam','').replace('.fastq','');
sna2sns_I[sample_names_lst[cnt]].append(filename);
genesCountTable = countFPKMattr.alignAndReformat_countFPKMattrTables(
analysis_id_I = analysis_id_I,
sna2experimentID_I = sna2experimentID_I,
sna2sns_I = sna2sns_I)
self.add_dataStage01RNASequencingGenesCountTable(genesCountTable);
def import_dataStage01RNASequencingGenesCountTable_update(self, filename):
data = base_importData();
data.read_csv(filename);
data.format_data();
self.update_dataStage01RNASequencingGenesCountTable(data.data);
data.clear_data();
def export_dataStage01RNASequencingGenesCountTable_js(self,analysis_id_I,data_dir_I='tmp'):
experiment_ids,sample_names = [],[];
experiment_ids,sample_names = self.get_experimentIDAndSampleName_analysisID_dataStage01RNASequencingAnalysis(analysis_id_I);
data_O = [];
for sample_name_cnt,sample_name in enumerate(sample_names):
fpkms = [];
fpkms = self.get_rows_experimentIDAndSampleName_dataStage01RNASequencingGenesCountTable(experiment_ids[sample_name_cnt],sample_name);
data_O.extend(fpkms);
data1_keys = ['experiment_id','sample_name','gene_short_name'
];
data1_nestkeys = ['gene_short_name'];
data1_keymap = {'xdata':'gene_short_name',
'ydatamean':'FPKM',
'ydatalb':'FPKM_conf_lo',
'ydataub':'FPKM_conf_hi',
'serieslabel':'sample_name',
'featureslabel':'gene_short_name'};
dataobject_O = [{"data":data_O,"datakeys":data1_keys,"datanestkeys":data1_nestkeys}];
formtileparameters_O = {'tileheader':'Filter menu','tiletype':'html','tileid':"filtermenu1",'rowid':"row1",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-4"};
formparameters_O = {'htmlid':'filtermenuform1',"htmltype":'form_01',"formsubmitbuttonidtext":{'id':'submit1','text':'submit'},"formresetbuttonidtext":{'id':'reset1','text':'reset'},"formupdatebuttonidtext":{'id':'update1','text':'update'}};
formtileparameters_O.update(formparameters_O);
svgparameters_O = {"svgtype":'boxandwhiskersplot2d_02',"svgkeymap":[data1_keymap],
'svgid':'svg1',
"svgmargin":{ 'top': 50, 'right': 150, 'bottom': 50, 'left': 50 },
"svgwidth":500,"svgheight":350,
"svgx1axislabel":"gene","svgy1axislabel":"FPKM",
'svgformtileid':'filtermenu1','svgresetbuttonid':'reset1','svgsubmitbuttonid':'submit1'};
svgtileparameters_O = {'tileheader':'Custom box and whiskers plot','tiletype':'svg','tileid':"tile2",'rowid':"row1",'colid':"col2",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-8"};
svgtileparameters_O.update(svgparameters_O);
tableparameters_O = {"tabletype":'responsivetable_01',
'tableid':'table1',
"tablefilters":None,
"tableclass":"table table-condensed table-hover",
'tableformtileid':'filtermenu1','tableresetbuttonid':'reset1','tablesubmitbuttonid':'submit1'};
tabletileparameters_O = {'tileheader':'FPKM','tiletype':'table','tileid':"tile3",'rowid':"row2",'colid':"col1",
'tileclass':"panel panel-default",'rowclass':"row",'colclass':"col-sm-12"};
tabletileparameters_O.update(tableparameters_O);
parametersobject_O = [formtileparameters_O,svgtileparameters_O,tabletileparameters_O];
tile2datamap_O = {"filtermenu1":[0],"tile2":[0],"tile3":[0]};
ddtutilities = ddt_container(parameters_I = parametersobject_O,data_I = dataobject_O,tile2datamap_I = tile2datamap_O,filtermenu_I = None);
if data_dir_I=='tmp':
filename_str = self.settings['visualization_data'] + '/tmp/ddt_data.js'
elif data_dir_I=='data_json':
data_json_O = ddtutilities.get_allObjects_js();
return data_json_O;
with open(filename_str,'w') as file:
file.write(ddtutilities.get_allObjects());
def export_dataStage01RNASequencingGenesCountTable_pairWisePlot_js(self,analysis_id_I,log2normalization_I=True,data_dir_I='tmp'):
experiment_ids,sample_names = [],[];
experiment_ids,sample_names = self.get_experimentIDAndSampleName_analysisID_dataStage01RNASequencingAnalysis(analysis_id_I);
data_O = [];
for sample_name_cnt,sample_name in enumerate(sample_names):
fpkms = [];
fpkms = self.get_rows_experimentIDAndSampleName_dataStage01RNASequencingGenesCountTable(experiment_ids[sample_name_cnt],sample_name);
if log2normalization_I:
for f in fpkms:
if f['FPKM'] == 0.0: f['FPKM'] = 0.0;
else: f['FPKM'] = log2(f['FPKM']);
data_O.extend(fpkms);
listdict = listDict(data_O);
data_O,columnValueHeader_O = listdict.convert_listDict2ColumnGroupListDict(
value_labels_I = ['FPKM',],
column_labels_I = ['experiment_id','sample_name'],
feature_labels_I = ['gene_id','gene_short_name'],
na_str_I=0.0,
columnValueConnector_str_I='_',
);
data1_keymap_table = {
'xdata':'svd_method',
'ydata':'singular_value_index',
'zdata':'d_vector',
'rowslabel':'svd_method',
'columnslabel':'singular_value_index',
};
data1_keys = ['gene_id','gene_short_name'
];
data1_nestkeys = ['gene_short_name'];
data1_keymap_svg = [];
svgtype = [];
svgtile2datamap = [];
data_svg_keymap = [];
for cnt1,column1 in enumerate(columnValueHeader_O):
for cnt2,column2 in enumerate(columnValueHeader_O[cnt1+1:]):
keymap = {
'xdata':column1,
'ydata':column2,
'serieslabel':'',
'featureslabel':'gene_short_name',
'tooltipdata':'gene_short_name',
};
data1_keymap_svg.append([keymap]);
data_svg_keymap.append(keymap);
svgtype.append('pcaplot2d_scores_01');
svgtile2datamap.append([0]);
nsvgtable = ddt_container_filterMenuAndChart2dAndTable();
nsvgtable.make_filterMenuAndChart2dAndTable(
data_filtermenu=data_O,
data_filtermenu_keys=data1_keys,
data_filtermenu_nestkeys=data1_nestkeys,
data_filtermenu_keymap=data1_keymap_table,
data_svg_keys=data1_keys,
data_svg_nestkeys=data1_nestkeys,
data_svg_keymap=data_svg_keymap,
data_table_keys=data1_keys,
data_table_nestkeys=data1_nestkeys,
data_table_keymap=data1_keymap_table,
data_svg=None,
data_table=None,
svgtype=svgtype,
tabletype='responsivetable_01',
svgx1axislabel='',
svgy1axislabel='',
tablekeymap = [data1_keymap_table],
svgkeymap = data1_keymap_svg,
formtile2datamap=[0],
tabletile2datamap=[0],
svgtile2datamap=svgtile2datamap,
svgfilters=None,
svgtileheader='Pair-wise scatter plot',
tablefilters=None,
tableheaders=None
);
if data_dir_I=='tmp':
filename_str = self.settings['visualization_data'] + '/tmp/ddt_data.js'
elif data_dir_I=='data_json':
data_json_O = nsvgtable.get_allObjects_js();
return data_json_O;
with open(filename_str,'w') as file:
file.write(nsvgtable.get_allObjects()); | true | true |
f7258a45bcc7583012e19070d9419ded10110a6e | 2,480 | py | Python | redunlive/utils.py | nmaekawa/redunlive | 3f1830d605c46a300d028a32b564d803964f2384 | [
"Apache-2.0"
] | null | null | null | redunlive/utils.py | nmaekawa/redunlive | 3f1830d605c46a300d028a32b564d803964f2384 | [
"Apache-2.0"
] | null | null | null | redunlive/utils.py | nmaekawa/redunlive | 3f1830d605c46a300d028a32b564d803964f2384 | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
import re
import sys
import platform
import requests
from requests.auth import HTTPBasicAuth
from . import __version__
from . import log
def clean_name( name ):
""" replaces non-alpha with underscores '_'
and set the string to lower case
"""
return re.sub( '[^0-9a-zA-Z]+', '_', name ).lower()
def pull_data( url, creds=None):
""" reads a text file from given url
if basic auth needed, pass args creds['user'] and creds['pwd']
"""
headers = {
'User-Agent': default_useragent(),
'Accept-Encoding': 'gzip, deflate',
'Accept': 'text/html, text/*'
}
au = None
if not creds is None:
if 'user' in creds.keys() and 'pwd' in creds.keys():
au = HTTPBasicAuth( creds['user'], creds['pwd'] )
headers.update( {'X-REQUESTED-AUTH': 'Basic'} )
try:
response = requests.get( url, headers=headers, auth=au )
except requests.HTTPError as e:
log.warning("data from url(%s) is unavailable. Error: %s" % ( url, e ) )
return None
else:
return response.text
def default_useragent():
"""Return a string representing the default user agent."""
_implementation = platform.python_implementation()
if _implementation == 'CPython':
_implementation_version = platform.python_version()
elif _implementation == 'PyPy':
_implementation_version = '%s.%s.%s' % (sys.pypy_version_info.major,
sys.pypy_version_info.minor,
sys.pypy_version_info.micro)
if sys.pypy_version_info.releaselevel != 'final':
_implementation_version = ''.join([_implementation_version, \
sys.pypy_version_info.releaselevel])
elif _implementation == 'Jython':
_implementation_version = platform.python_version() # Complete Guess
elif _implementation == 'IronPython':
_implementation_version = platform.python_version() # Complete Guess
else:
_implementation_version = 'Unknown'
try:
p_system = platform.system()
p_release = platform.release()
except IOError:
p_system = 'Unknown'
p_release = 'Unknown'
return " ".join(['%s/%s' % (__name__, __version__),
'%s/%s' % (_implementation, _implementation_version),
'%s/%s' % (p_system, p_release)])
| 32.207792 | 80 | 0.594355 |
import re
import sys
import platform
import requests
from requests.auth import HTTPBasicAuth
from . import __version__
from . import log
def clean_name( name ):
return re.sub( '[^0-9a-zA-Z]+', '_', name ).lower()
def pull_data( url, creds=None):
headers = {
'User-Agent': default_useragent(),
'Accept-Encoding': 'gzip, deflate',
'Accept': 'text/html, text/*'
}
au = None
if not creds is None:
if 'user' in creds.keys() and 'pwd' in creds.keys():
au = HTTPBasicAuth( creds['user'], creds['pwd'] )
headers.update( {'X-REQUESTED-AUTH': 'Basic'} )
try:
response = requests.get( url, headers=headers, auth=au )
except requests.HTTPError as e:
log.warning("data from url(%s) is unavailable. Error: %s" % ( url, e ) )
return None
else:
return response.text
def default_useragent():
_implementation = platform.python_implementation()
if _implementation == 'CPython':
_implementation_version = platform.python_version()
elif _implementation == 'PyPy':
_implementation_version = '%s.%s.%s' % (sys.pypy_version_info.major,
sys.pypy_version_info.minor,
sys.pypy_version_info.micro)
if sys.pypy_version_info.releaselevel != 'final':
_implementation_version = ''.join([_implementation_version, \
sys.pypy_version_info.releaselevel])
elif _implementation == 'Jython':
_implementation_version = platform.python_version()
elif _implementation == 'IronPython':
_implementation_version = platform.python_version()
else:
_implementation_version = 'Unknown'
try:
p_system = platform.system()
p_release = platform.release()
except IOError:
p_system = 'Unknown'
p_release = 'Unknown'
return " ".join(['%s/%s' % (__name__, __version__),
'%s/%s' % (_implementation, _implementation_version),
'%s/%s' % (p_system, p_release)])
| true | true |
f7258af14dcf0fa82efc690560fcea365ab3ef09 | 4,999 | py | Python | cameras_to_albums.py | brandoconnor/flickr-album-per-camera | 358c90cded12c6c9ecb9f8ae289e005f172d30e2 | [
"Apache-2.0"
] | null | null | null | cameras_to_albums.py | brandoconnor/flickr-album-per-camera | 358c90cded12c6c9ecb9f8ae289e005f172d30e2 | [
"Apache-2.0"
] | 1 | 2016-02-12T02:25:19.000Z | 2016-02-13T00:07:46.000Z | cameras_to_albums.py | brandoconnor/flickr-album-per-camera | 358c90cded12c6c9ecb9f8ae289e005f172d30e2 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
"""
A simple script to organize photos into albums named by their camera source.
For me, this is useful for cycling through only high quality photos on my TV
hooked up to a chromecast.
"""
import argparse
import flickrapi
def auth_flickr(api_key, api_secret):
"""Authenticate user to flickr API."""
flickr = flickrapi.FlickrAPI(api_key, api_secret, format='parsed-json')
flickr.authenticate_via_browser(perms='write')
return flickr
def get_user_id(flickr, user):
"""Get the user_id from the username."""
user_data = flickr.people.findByUsername(username=user)
return user_data['user']['id']
def get_photoset_dict(flickr, user_id):
"""construct a photoset dict of album name to album/set id."""
print('Gathering dictionary of all photos in all photosets...')
init_photoset = flickr.photosets.getList(user_id=user_id)
photoset_dict = dict()
set_num = 1
for set_page in range(1, init_photoset['photosets']['pages'] + 1):
photoset = flickr.photosets.getList(user_id=user_id, page=set_page)
for pset in photoset['photosets']['photoset']:
print('processing photoset %s of %s' %
(set_num, init_photoset['photosets']['total']))
set_num += 1
# a_dict = {'thing': {'this': ['list, 'values']} }
photoset_dict[pset['title']['_content']] = {pset['id']: []}
init_photoset_object = flickr.photosets.getPhotos(
photoset_id=pset['id'])
for photoset_page in range(1, init_photoset_object['photoset']['pages'] + 1):
photoset_object = flickr.photosets.getPhotos(
user_id=user_id, photoset_id=pset['id'], page=photoset_page)
photoset_dict[pset['title']['_content']][
pset['id']] += [p['id'] for p in photoset_object['photoset']['photo']]
return photoset_dict
def main(args):
"""Main code block. Do all the things."""
flickr = auth_flickr(args.api_key, args.api_secret)
user_id = get_user_id(flickr, user=args.username)
album_dict = get_photoset_dict(flickr, user_id)
init_photos = flickr.people.getPhotos(user_id=user_id)
total = init_photos['photos']['total']
photo_num = args.initial_photo / 100 * 100 # TODO ensure less than total photos
if photo_num > total:
raise('Trying to start at photo %s but only %s total. Exiting.' %
(args.initial_photo, total))
init_page = args.initial_photo / 100 + 1 # 100 photos per page
for page_num in range(init_page, init_photos['photos']['pages'] + 1):
photo_batch = flickr.people.getPhotos(user_id=user_id, page=page_num)
for photo in photo_batch['photos']['photo']:
photo_num += 1
photo_id = photo['id']
print('processing photo %s of %s: %s' %
(photo_num, total, photo_id))
photo_data = flickr.photos.getExif(photo_id=photo_id)
camera_name = photo_data['photo']['camera']
if len(camera_name) > 0:
if camera_name not in album_dict.keys():
print('adding camera album "%s"' % camera_name)
new_set = flickr.photosets.create(title=camera_name,
primary_photo_id=photo_id,
description='All photos taken behind a %s' % camera_name)
album_dict[camera_name] = {
new_set['photoset']['id']: [photo_id]}
continue
elif photo_id not in [p for p in album_dict[camera_name].values()[0]]:
# if this photo is not in the appropriate album, add it
print('Adding photo to camera album.')
flickr.photosets.addPhoto(photoset_id=album_dict[
camera_name].keys()[0], photo_id=photo_id)
album_dict[camera_name].values()[0].append(photo_id)
else:
print('Photo is already in the appropriate set')
continue
else:
print('Skipping photo with insufficient metadata.')
if __name__ in '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--dry_run', action='store_true',
default=False, help="Verbose minus action. Default=False")
parser.add_argument('-i', '--initial_photo', help='approximate initial photo. Rounds down to nearest hundred',
type=int, default=0)
parser.add_argument('-k', '--api_key',
help='flickr API key', required=True)
parser.add_argument('-s', '--api_secret',
help='flickr API secret', required=True)
parser.add_argument('-u', '--username',
help='your flickr username', required=True)
exit(main(parser.parse_args()))
| 46.719626 | 114 | 0.595319 |
import argparse
import flickrapi
def auth_flickr(api_key, api_secret):
flickr = flickrapi.FlickrAPI(api_key, api_secret, format='parsed-json')
flickr.authenticate_via_browser(perms='write')
return flickr
def get_user_id(flickr, user):
user_data = flickr.people.findByUsername(username=user)
return user_data['user']['id']
def get_photoset_dict(flickr, user_id):
print('Gathering dictionary of all photos in all photosets...')
init_photoset = flickr.photosets.getList(user_id=user_id)
photoset_dict = dict()
set_num = 1
for set_page in range(1, init_photoset['photosets']['pages'] + 1):
photoset = flickr.photosets.getList(user_id=user_id, page=set_page)
for pset in photoset['photosets']['photoset']:
print('processing photoset %s of %s' %
(set_num, init_photoset['photosets']['total']))
set_num += 1
photoset_dict[pset['title']['_content']] = {pset['id']: []}
init_photoset_object = flickr.photosets.getPhotos(
photoset_id=pset['id'])
for photoset_page in range(1, init_photoset_object['photoset']['pages'] + 1):
photoset_object = flickr.photosets.getPhotos(
user_id=user_id, photoset_id=pset['id'], page=photoset_page)
photoset_dict[pset['title']['_content']][
pset['id']] += [p['id'] for p in photoset_object['photoset']['photo']]
return photoset_dict
def main(args):
flickr = auth_flickr(args.api_key, args.api_secret)
user_id = get_user_id(flickr, user=args.username)
album_dict = get_photoset_dict(flickr, user_id)
init_photos = flickr.people.getPhotos(user_id=user_id)
total = init_photos['photos']['total']
photo_num = args.initial_photo / 100 * 100 # TODO ensure less than total photos
if photo_num > total:
raise('Trying to start at photo %s but only %s total. Exiting.' %
(args.initial_photo, total))
init_page = args.initial_photo / 100 + 1 # 100 photos per page
for page_num in range(init_page, init_photos['photos']['pages'] + 1):
photo_batch = flickr.people.getPhotos(user_id=user_id, page=page_num)
for photo in photo_batch['photos']['photo']:
photo_num += 1
photo_id = photo['id']
print('processing photo %s of %s: %s' %
(photo_num, total, photo_id))
photo_data = flickr.photos.getExif(photo_id=photo_id)
camera_name = photo_data['photo']['camera']
if len(camera_name) > 0:
if camera_name not in album_dict.keys():
print('adding camera album "%s"' % camera_name)
new_set = flickr.photosets.create(title=camera_name,
primary_photo_id=photo_id,
description='All photos taken behind a %s' % camera_name)
album_dict[camera_name] = {
new_set['photoset']['id']: [photo_id]}
continue
elif photo_id not in [p for p in album_dict[camera_name].values()[0]]:
# if this photo is not in the appropriate album, add it
print('Adding photo to camera album.')
flickr.photosets.addPhoto(photoset_id=album_dict[
camera_name].keys()[0], photo_id=photo_id)
album_dict[camera_name].values()[0].append(photo_id)
else:
print('Photo is already in the appropriate set')
continue
else:
print('Skipping photo with insufficient metadata.')
if __name__ in '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-d', '--dry_run', action='store_true',
default=False, help="Verbose minus action. Default=False")
parser.add_argument('-i', '--initial_photo', help='approximate initial photo. Rounds down to nearest hundred',
type=int, default=0)
parser.add_argument('-k', '--api_key',
help='flickr API key', required=True)
parser.add_argument('-s', '--api_secret',
help='flickr API secret', required=True)
parser.add_argument('-u', '--username',
help='your flickr username', required=True)
exit(main(parser.parse_args()))
| true | true |
f7258c968558839fb8aa4aaba0600d037324ede1 | 2,204 | py | Python | cheritest/trunk/tests/alu/test_msub_ex.py | tupipa/beri | cef1b41d52592cfa7454ddf59f9f2994e447cd66 | [
"Apache-2.0"
] | 36 | 2015-05-29T16:47:19.000Z | 2022-02-08T21:16:26.000Z | cheritest/trunk/tests/alu/test_msub_ex.py | tupipa/beri | cef1b41d52592cfa7454ddf59f9f2994e447cd66 | [
"Apache-2.0"
] | 1 | 2015-10-14T13:05:21.000Z | 2015-10-19T20:34:03.000Z | cheritest/trunk/tests/alu/test_msub_ex.py | tupipa/beri | cef1b41d52592cfa7454ddf59f9f2994e447cd66 | [
"Apache-2.0"
] | 15 | 2015-06-11T07:10:58.000Z | 2021-06-18T05:14:54.000Z | #-
# Copyright (c) 2015 Michael Roe
# All rights reserved.
#
# This software was developed by the University of Cambridge Computer
# Laboratory as part of the Rigorous Engineering of Mainstream Systems (REMS)
# project, funded by EPSRC grant EP/K008528/1.
#
# @BERI_LICENSE_HEADER_START@
#
# Licensed to BERI Open Systems C.I.C. (BERI) under one or more contributor
# license agreements. See the NOTICE file distributed with this work for
# additional information regarding copyright ownership. BERI licenses this
# file to you under the BERI Hardware-Software License, Version 1.0 (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.beri-open-systems.org/legal/license-1-0.txt
#
# Unless required by applicable law or agreed to in writing, Work 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.
#
# @BERI_LICENSE_HEADER_END@
#
from beritest_tools import BaseBERITestCase
from nose.plugins.attrib import attr
class test_msub_ex(BaseBERITestCase):
@attr('ignorebadex')
@attr('madd')
def test_msub_ex_1(self):
self.assertRegisterEqual(self.MIPS.a0, 0xfffffffffffffffa, "MSUB of a value that was not a valid sign extention of a 32-bit value gave an unexpected result.")
@attr('ignorebadex')
@attr('madd')
def test_msub_ex_2(self):
self.assertRegisterEqual(self.MIPS.a1, 0xffffffffffffffff, "MSUB of a value that was not a valid sign extention of a 32-bit value gave an unexpected result.")
@attr('ignorebadex')
@attr('madd')
def test_msub_ex_3(self):
self.assertRegisterEqual(self.MIPS.a2, 0xfffffffffffffffa, "MSUB of a value that was not a valid sign extention of a 32-bit value gave an unexpected result.")
@attr('ignorebadex')
@attr('madd')
def test_msub_ex_4(self):
self.assertRegisterEqual(self.MIPS.a3, 0xffffffffffffffff, "MSUB of a value that was not a valid sign extention of a 32-bit value gave an unexpected result.")
| 41.584906 | 166 | 0.748185 |
from beritest_tools import BaseBERITestCase
from nose.plugins.attrib import attr
class test_msub_ex(BaseBERITestCase):
@attr('ignorebadex')
@attr('madd')
def test_msub_ex_1(self):
self.assertRegisterEqual(self.MIPS.a0, 0xfffffffffffffffa, "MSUB of a value that was not a valid sign extention of a 32-bit value gave an unexpected result.")
@attr('ignorebadex')
@attr('madd')
def test_msub_ex_2(self):
self.assertRegisterEqual(self.MIPS.a1, 0xffffffffffffffff, "MSUB of a value that was not a valid sign extention of a 32-bit value gave an unexpected result.")
@attr('ignorebadex')
@attr('madd')
def test_msub_ex_3(self):
self.assertRegisterEqual(self.MIPS.a2, 0xfffffffffffffffa, "MSUB of a value that was not a valid sign extention of a 32-bit value gave an unexpected result.")
@attr('ignorebadex')
@attr('madd')
def test_msub_ex_4(self):
self.assertRegisterEqual(self.MIPS.a3, 0xffffffffffffffff, "MSUB of a value that was not a valid sign extention of a 32-bit value gave an unexpected result.")
| true | true |
f7258cce35cf6a9ce718c2c71ffc2151fcba6ee2 | 498 | py | Python | contentsummary/urls.py | Bobstin/itcsummary | 259d8f64e415a1c7cbc926752c717e307c09953f | [
"MIT"
] | null | null | null | contentsummary/urls.py | Bobstin/itcsummary | 259d8f64e415a1c7cbc926752c717e307c09953f | [
"MIT"
] | 5 | 2021-02-27T13:23:58.000Z | 2021-09-22T17:39:19.000Z | contentsummary/urls.py | Bobstin/itcsummary | 259d8f64e415a1c7cbc926752c717e307c09953f | [
"MIT"
] | null | null | null | from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^next/(?P<priornumber>[0-9]+)/$', views.nextSession, name='nextSession'),
url(r'^all/$', views.allSessions, name='allSessions'),
url(r'^allpt1/$', views.allSessionspt1, name='allSessionspt1'),
url(r'^allpt2/$', views.allSessionspt2, name='allSessionspt2'),
url(r'^singleSession/(?P<session_number>[0-9]+)/$', views.singleSession, name='singleSession'),
url(r'^$', views.example, name='example'),
] | 41.5 | 99 | 0.670683 | from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^next/(?P<priornumber>[0-9]+)/$', views.nextSession, name='nextSession'),
url(r'^all/$', views.allSessions, name='allSessions'),
url(r'^allpt1/$', views.allSessionspt1, name='allSessionspt1'),
url(r'^allpt2/$', views.allSessionspt2, name='allSessionspt2'),
url(r'^singleSession/(?P<session_number>[0-9]+)/$', views.singleSession, name='singleSession'),
url(r'^$', views.example, name='example'),
] | true | true |
f7258cd44fa3033315e86b9b007b80c34183601d | 71,980 | py | Python | nova/compute/resource_tracker.py | aspiers/nova | e8b6b0bc78ec229803d1d27f8a4706e2c425bd77 | [
"Apache-2.0"
] | 1 | 2021-12-27T00:47:30.000Z | 2021-12-27T00:47:30.000Z | nova/compute/resource_tracker.py | aspiers/nova | e8b6b0bc78ec229803d1d27f8a4706e2c425bd77 | [
"Apache-2.0"
] | null | null | null | nova/compute/resource_tracker.py | aspiers/nova | e8b6b0bc78ec229803d1d27f8a4706e2c425bd77 | [
"Apache-2.0"
] | null | null | null | # Copyright (c) 2012 OpenStack Foundation
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
# License for the specific language governing permissions and limitations
# under the License.
"""
Track resources like memory and disk for a compute host. Provides the
scheduler with useful information about availability through the ComputeNode
model.
"""
import collections
import copy
from keystoneauth1 import exceptions as ks_exc
import os_resource_classes as orc
from oslo_log import log as logging
from oslo_serialization import jsonutils
import retrying
from nova.compute import claims
from nova.compute import monitors
from nova.compute import stats as compute_stats
from nova.compute import task_states
from nova.compute import utils as compute_utils
from nova.compute import vm_states
import nova.conf
from nova import exception
from nova.i18n import _
from nova import objects
from nova.objects import base as obj_base
from nova.objects import migration as migration_obj
from nova.pci import manager as pci_manager
from nova.pci import request as pci_request
from nova import rpc
from nova.scheduler.client import report
from nova import utils
from nova.virt import hardware
CONF = nova.conf.CONF
LOG = logging.getLogger(__name__)
COMPUTE_RESOURCE_SEMAPHORE = "compute_resources"
def _instance_in_resize_state(instance):
"""Returns True if the instance is in one of the resizing states.
:param instance: `nova.objects.Instance` object
"""
vm = instance.vm_state
task = instance.task_state
if vm == vm_states.RESIZED:
return True
if (vm in [vm_states.ACTIVE, vm_states.STOPPED]
and task in (
task_states.resizing_states + task_states.rebuild_states)):
return True
return False
def _is_trackable_migration(migration):
# Only look at resize/migrate migration and evacuation records
# NOTE(danms): RT should probably examine live migration
# records as well and do something smart. However, ignore
# those for now to avoid them being included in below calculations.
return migration.migration_type in ('resize', 'migration',
'evacuation')
def _normalize_inventory_from_cn_obj(inv_data, cn):
"""Helper function that injects various information from a compute node
object into the inventory dict returned from the virt driver's
get_inventory() method. This function allows us to marry information like
*_allocation_ratio and reserved memory amounts that are in the
compute_nodes DB table and that the virt driver doesn't know about with the
information the virt driver *does* know about.
Note that if the supplied inv_data contains allocation_ratio, reserved or
other fields, we DO NOT override the value with that of the compute node.
This is to ensure that the virt driver is the single source of truth
regarding inventory information. For instance, the Ironic virt driver will
always return a very specific inventory with allocation_ratios pinned to
1.0.
:param inv_data: Dict, keyed by resource class, of inventory information
returned from virt driver's get_inventory() method
:param compute_node: `objects.ComputeNode` describing the compute node
"""
if orc.VCPU in inv_data:
cpu_inv = inv_data[orc.VCPU]
if 'allocation_ratio' not in cpu_inv:
cpu_inv['allocation_ratio'] = cn.cpu_allocation_ratio
if 'reserved' not in cpu_inv:
cpu_inv['reserved'] = CONF.reserved_host_cpus
if orc.MEMORY_MB in inv_data:
mem_inv = inv_data[orc.MEMORY_MB]
if 'allocation_ratio' not in mem_inv:
mem_inv['allocation_ratio'] = cn.ram_allocation_ratio
if 'reserved' not in mem_inv:
mem_inv['reserved'] = CONF.reserved_host_memory_mb
if orc.DISK_GB in inv_data:
disk_inv = inv_data[orc.DISK_GB]
if 'allocation_ratio' not in disk_inv:
disk_inv['allocation_ratio'] = cn.disk_allocation_ratio
if 'reserved' not in disk_inv:
# TODO(johngarbutt) We should either move to reserved_host_disk_gb
# or start tracking DISK_MB.
reserved_mb = CONF.reserved_host_disk_mb
reserved_gb = compute_utils.convert_mb_to_ceil_gb(reserved_mb)
disk_inv['reserved'] = reserved_gb
class ResourceTracker(object):
"""Compute helper class for keeping track of resource usage as instances
are built and destroyed.
"""
def __init__(self, host, driver):
self.host = host
self.driver = driver
self.pci_tracker = None
# Dict of objects.ComputeNode objects, keyed by nodename
self.compute_nodes = {}
# Dict of Stats objects, keyed by nodename
self.stats = collections.defaultdict(compute_stats.Stats)
# Set of UUIDs of instances tracked on this host.
self.tracked_instances = set()
self.tracked_migrations = {}
self.is_bfv = {} # dict, keyed by instance uuid, to is_bfv boolean
monitor_handler = monitors.MonitorHandler(self)
self.monitors = monitor_handler.monitors
self.old_resources = collections.defaultdict(objects.ComputeNode)
self.reportclient = report.SchedulerReportClient()
self.ram_allocation_ratio = CONF.ram_allocation_ratio
self.cpu_allocation_ratio = CONF.cpu_allocation_ratio
self.disk_allocation_ratio = CONF.disk_allocation_ratio
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def instance_claim(self, context, instance, nodename, limits=None):
"""Indicate that some resources are needed for an upcoming compute
instance build operation.
This should be called before the compute node is about to perform
an instance build operation that will consume additional resources.
:param context: security context
:param instance: instance to reserve resources for.
:type instance: nova.objects.instance.Instance object
:param nodename: The Ironic nodename selected by the scheduler
:param limits: Dict of oversubscription limits for memory, disk,
and CPUs.
:returns: A Claim ticket representing the reserved resources. It can
be used to revert the resource usage if an error occurs
during the instance build.
"""
if self.disabled(nodename):
# instance_claim() was called before update_available_resource()
# (which ensures that a compute node exists for nodename). We
# shouldn't get here but in case we do, just set the instance's
# host and nodename attribute (probably incorrect) and return a
# NoopClaim.
# TODO(jaypipes): Remove all the disabled junk from the resource
# tracker. Servicegroup API-level active-checking belongs in the
# nova-compute manager.
self._set_instance_host_and_node(instance, nodename)
return claims.NopClaim()
# sanity checks:
if instance.host:
LOG.warning("Host field should not be set on the instance "
"until resources have been claimed.",
instance=instance)
if instance.node:
LOG.warning("Node field should not be set on the instance "
"until resources have been claimed.",
instance=instance)
# get the overhead required to build this instance:
overhead = self.driver.estimate_instance_overhead(instance)
LOG.debug("Memory overhead for %(flavor)d MB instance; %(overhead)d "
"MB", {'flavor': instance.flavor.memory_mb,
'overhead': overhead['memory_mb']})
LOG.debug("Disk overhead for %(flavor)d GB instance; %(overhead)d "
"GB", {'flavor': instance.flavor.root_gb,
'overhead': overhead.get('disk_gb', 0)})
LOG.debug("CPU overhead for %(flavor)d vCPUs instance; %(overhead)d "
"vCPU(s)", {'flavor': instance.flavor.vcpus,
'overhead': overhead.get('vcpus', 0)})
cn = self.compute_nodes[nodename]
pci_requests = objects.InstancePCIRequests.get_by_instance_uuid(
context, instance.uuid)
claim = claims.Claim(context, instance, nodename, self, cn,
pci_requests, overhead=overhead, limits=limits)
# self._set_instance_host_and_node() will save instance to the DB
# so set instance.numa_topology first. We need to make sure
# that numa_topology is saved while under COMPUTE_RESOURCE_SEMAPHORE
# so that the resource audit knows about any cpus we've pinned.
instance_numa_topology = claim.claimed_numa_topology
instance.numa_topology = instance_numa_topology
self._set_instance_host_and_node(instance, nodename)
if self.pci_tracker:
# NOTE(jaypipes): ComputeNode.pci_device_pools is set below
# in _update_usage_from_instance().
self.pci_tracker.claim_instance(context, pci_requests,
instance_numa_topology)
# Mark resources in-use and update stats
self._update_usage_from_instance(context, instance, nodename)
elevated = context.elevated()
# persist changes to the compute node:
self._update(elevated, cn)
return claim
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def rebuild_claim(self, context, instance, nodename, limits=None,
image_meta=None, migration=None):
"""Create a claim for a rebuild operation."""
instance_type = instance.flavor
return self._move_claim(context, instance, instance_type, nodename,
migration, move_type='evacuation',
limits=limits, image_meta=image_meta)
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def resize_claim(self, context, instance, instance_type, nodename,
migration, image_meta=None, limits=None):
"""Create a claim for a resize or cold-migration move.
Note that this code assumes ``instance.new_flavor`` is set when
resizing with a new flavor.
"""
return self._move_claim(context, instance, instance_type, nodename,
migration, image_meta=image_meta,
limits=limits)
def _move_claim(self, context, instance, new_instance_type, nodename,
migration, move_type=None, image_meta=None, limits=None):
"""Indicate that resources are needed for a move to this host.
Move can be either a migrate/resize, live-migrate or an
evacuate/rebuild operation.
:param context: security context
:param instance: instance object to reserve resources for
:param new_instance_type: new instance_type being resized to
:param nodename: The Ironic nodename selected by the scheduler
:param image_meta: instance image metadata
:param move_type: move type - can be one of 'migration', 'resize',
'live-migration', 'evacuate'
:param limits: Dict of oversubscription limits for memory, disk,
and CPUs
:param migration: A migration object if one was already created
elsewhere for this operation (otherwise None)
:returns: A Claim ticket representing the reserved resources. This
should be turned into finalize a resource claim or free
resources after the compute operation is finished.
"""
image_meta = image_meta or {}
if migration:
self._claim_existing_migration(migration, nodename)
else:
migration = self._create_migration(context, instance,
new_instance_type,
nodename, move_type)
if self.disabled(nodename):
# compute_driver doesn't support resource tracking, just
# generate the migration record and continue the resize:
return claims.NopClaim(migration=migration)
# get memory overhead required to build this instance:
overhead = self.driver.estimate_instance_overhead(new_instance_type)
LOG.debug("Memory overhead for %(flavor)d MB instance; %(overhead)d "
"MB", {'flavor': new_instance_type.memory_mb,
'overhead': overhead['memory_mb']})
LOG.debug("Disk overhead for %(flavor)d GB instance; %(overhead)d "
"GB", {'flavor': instance.flavor.root_gb,
'overhead': overhead.get('disk_gb', 0)})
LOG.debug("CPU overhead for %(flavor)d vCPUs instance; %(overhead)d "
"vCPU(s)", {'flavor': instance.flavor.vcpus,
'overhead': overhead.get('vcpus', 0)})
cn = self.compute_nodes[nodename]
# TODO(moshele): we are recreating the pci requests even if
# there was no change on resize. This will cause allocating
# the old/new pci device in the resize phase. In the future
# we would like to optimise this.
new_pci_requests = pci_request.get_pci_requests_from_flavor(
new_instance_type)
new_pci_requests.instance_uuid = instance.uuid
# PCI requests come from two sources: instance flavor and
# SR-IOV ports. SR-IOV ports pci_request don't have an alias_name.
# On resize merge the SR-IOV ports pci_requests with the new
# instance flavor pci_requests.
if instance.pci_requests:
for request in instance.pci_requests.requests:
if request.alias_name is None:
new_pci_requests.requests.append(request)
claim = claims.MoveClaim(context, instance, nodename,
new_instance_type, image_meta, self, cn,
new_pci_requests, overhead=overhead,
limits=limits)
claim.migration = migration
claimed_pci_devices_objs = []
if self.pci_tracker:
# NOTE(jaypipes): ComputeNode.pci_device_pools is set below
# in _update_usage_from_instance().
claimed_pci_devices_objs = self.pci_tracker.claim_instance(
context, new_pci_requests, claim.claimed_numa_topology)
claimed_pci_devices = objects.PciDeviceList(
objects=claimed_pci_devices_objs)
# TODO(jaypipes): Move claimed_numa_topology out of the Claim's
# constructor flow so the Claim constructor only tests whether
# resources can be claimed, not consume the resources directly.
mig_context = objects.MigrationContext(
context=context, instance_uuid=instance.uuid,
migration_id=migration.id,
old_numa_topology=instance.numa_topology,
new_numa_topology=claim.claimed_numa_topology,
old_pci_devices=instance.pci_devices,
new_pci_devices=claimed_pci_devices,
old_pci_requests=instance.pci_requests,
new_pci_requests=new_pci_requests)
instance.migration_context = mig_context
instance.save()
# Mark the resources in-use for the resize landing on this
# compute host:
self._update_usage_from_migration(context, instance, migration,
nodename)
elevated = context.elevated()
self._update(elevated, cn)
return claim
def _create_migration(self, context, instance, new_instance_type,
nodename, move_type=None):
"""Create a migration record for the upcoming resize. This should
be done while the COMPUTE_RESOURCES_SEMAPHORE is held so the resource
claim will not be lost if the audit process starts.
"""
migration = objects.Migration(context=context.elevated())
migration.dest_compute = self.host
migration.dest_node = nodename
migration.dest_host = self.driver.get_host_ip_addr()
migration.old_instance_type_id = instance.flavor.id
migration.new_instance_type_id = new_instance_type.id
migration.status = 'pre-migrating'
migration.instance_uuid = instance.uuid
migration.source_compute = instance.host
migration.source_node = instance.node
if move_type:
migration.migration_type = move_type
else:
migration.migration_type = migration_obj.determine_migration_type(
migration)
migration.create()
return migration
def _claim_existing_migration(self, migration, nodename):
"""Make an existing migration record count for resource tracking.
If a migration record was created already before the request made
it to this compute host, only set up the migration so it's included in
resource tracking. This should be done while the
COMPUTE_RESOURCES_SEMAPHORE is held.
"""
migration.dest_compute = self.host
migration.dest_node = nodename
migration.dest_host = self.driver.get_host_ip_addr()
migration.status = 'pre-migrating'
migration.save()
def _set_instance_host_and_node(self, instance, nodename):
"""Tag the instance as belonging to this host. This should be done
while the COMPUTE_RESOURCES_SEMAPHORE is held so the resource claim
will not be lost if the audit process starts.
"""
instance.host = self.host
instance.launched_on = self.host
instance.node = nodename
instance.save()
def _unset_instance_host_and_node(self, instance):
"""Untag the instance so it no longer belongs to the host.
This should be done while the COMPUTE_RESOURCES_SEMAPHORE is held so
the resource claim will not be lost if the audit process starts.
"""
instance.host = None
instance.node = None
instance.save()
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def abort_instance_claim(self, context, instance, nodename):
"""Remove usage from the given instance."""
self._update_usage_from_instance(context, instance, nodename,
is_removed=True)
instance.clear_numa_topology()
self._unset_instance_host_and_node(instance)
self._update(context.elevated(), self.compute_nodes[nodename])
def _drop_pci_devices(self, instance, nodename, prefix):
if self.pci_tracker:
# free old/new allocated pci devices
pci_devices = self._get_migration_context_resource(
'pci_devices', instance, prefix=prefix)
if pci_devices:
for pci_device in pci_devices:
self.pci_tracker.free_device(pci_device, instance)
dev_pools_obj = self.pci_tracker.stats.to_device_pools_obj()
self.compute_nodes[nodename].pci_device_pools = dev_pools_obj
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def drop_move_claim(self, context, instance, nodename,
instance_type=None, prefix='new_'):
"""Remove usage for an incoming/outgoing migration.
:param context: Security context.
:param instance: The instance whose usage is to be removed.
:param nodename: Host on which to remove usage. If the migration
completed successfully, this is normally the source.
If it did not complete successfully (failed or
reverted), this is normally the destination.
:param instance_type: The flavor that determines the usage to remove.
If the migration completed successfully, this is
the old flavor to be removed from the source. If
the migration did not complete successfully, this
is the new flavor to be removed from the
destination.
:param prefix: Prefix to use when accessing migration context
attributes. 'old_' or 'new_', with 'new_' being the
default.
"""
# Remove usage for an instance that is tracked in migrations, such as
# on the dest node during revert resize.
if instance['uuid'] in self.tracked_migrations:
migration = self.tracked_migrations.pop(instance['uuid'])
if not instance_type:
instance_type = self._get_instance_type(instance, prefix,
migration)
# Remove usage for an instance that is not tracked in migrations (such
# as on the source node after a migration).
# NOTE(lbeliveau): On resize on the same node, the instance is
# included in both tracked_migrations and tracked_instances.
elif instance['uuid'] in self.tracked_instances:
self.tracked_instances.remove(instance['uuid'])
if instance_type is not None:
numa_topology = self._get_migration_context_resource(
'numa_topology', instance, prefix=prefix)
usage = self._get_usage_dict(
instance_type, instance, numa_topology=numa_topology)
self._drop_pci_devices(instance, nodename, prefix)
self._update_usage(usage, nodename, sign=-1)
ctxt = context.elevated()
self._update(ctxt, self.compute_nodes[nodename])
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def update_usage(self, context, instance, nodename):
"""Update the resource usage and stats after a change in an
instance
"""
if self.disabled(nodename):
return
uuid = instance['uuid']
# don't update usage for this instance unless it submitted a resource
# claim first:
if uuid in self.tracked_instances:
self._update_usage_from_instance(context, instance, nodename)
self._update(context.elevated(), self.compute_nodes[nodename])
def disabled(self, nodename):
return (nodename not in self.compute_nodes or
not self.driver.node_is_available(nodename))
def _check_for_nodes_rebalance(self, context, resources, nodename):
"""Check if nodes rebalance has happened.
The ironic driver maintains a hash ring mapping bare metal nodes
to compute nodes. If a compute dies, the hash ring is rebuilt, and
some of its bare metal nodes (more precisely, those not in ACTIVE
state) are assigned to other computes.
This method checks for this condition and adjusts the database
accordingly.
:param context: security context
:param resources: initial values
:param nodename: node name
:returns: True if a suitable compute node record was found, else False
"""
if not self.driver.rebalances_nodes:
return False
# Its possible ironic just did a node re-balance, so let's
# check if there is a compute node that already has the correct
# hypervisor_hostname. We can re-use that rather than create a
# new one and have to move existing placement allocations
cn_candidates = objects.ComputeNodeList.get_by_hypervisor(
context, nodename)
if len(cn_candidates) == 1:
cn = cn_candidates[0]
LOG.info("ComputeNode %(name)s moving from %(old)s to %(new)s",
{"name": nodename, "old": cn.host, "new": self.host})
cn.host = self.host
self.compute_nodes[nodename] = cn
self._copy_resources(cn, resources)
self._setup_pci_tracker(context, cn, resources)
self._update(context, cn)
return True
elif len(cn_candidates) > 1:
LOG.error(
"Found more than one ComputeNode for nodename %s. "
"Please clean up the orphaned ComputeNode records in your DB.",
nodename)
return False
def _init_compute_node(self, context, resources):
"""Initialize the compute node if it does not already exist.
The resource tracker will be inoperable if compute_node
is not defined. The compute_node will remain undefined if
we fail to create it or if there is no associated service
registered.
If this method has to create a compute node it needs initial
values - these come from resources.
:param context: security context
:param resources: initial values
:returns: True if a new compute_nodes table record was created,
False otherwise
"""
nodename = resources['hypervisor_hostname']
# if there is already a compute node just use resources
# to initialize
if nodename in self.compute_nodes:
cn = self.compute_nodes[nodename]
self._copy_resources(cn, resources)
self._setup_pci_tracker(context, cn, resources)
return False
# now try to get the compute node record from the
# database. If we get one we use resources to initialize
cn = self._get_compute_node(context, nodename)
if cn:
self.compute_nodes[nodename] = cn
self._copy_resources(cn, resources)
self._setup_pci_tracker(context, cn, resources)
return False
if self._check_for_nodes_rebalance(context, resources, nodename):
return False
# there was no local copy and none in the database
# so we need to create a new compute node. This needs
# to be initialized with resource values.
cn = objects.ComputeNode(context)
cn.host = self.host
self._copy_resources(cn, resources, initial=True)
self.compute_nodes[nodename] = cn
cn.create()
LOG.info('Compute node record created for '
'%(host)s:%(node)s with uuid: %(uuid)s',
{'host': self.host, 'node': nodename, 'uuid': cn.uuid})
self._setup_pci_tracker(context, cn, resources)
return True
def _setup_pci_tracker(self, context, compute_node, resources):
if not self.pci_tracker:
n_id = compute_node.id
self.pci_tracker = pci_manager.PciDevTracker(context, node_id=n_id)
if 'pci_passthrough_devices' in resources:
dev_json = resources.pop('pci_passthrough_devices')
self.pci_tracker.update_devices_from_hypervisor_resources(
dev_json)
dev_pools_obj = self.pci_tracker.stats.to_device_pools_obj()
compute_node.pci_device_pools = dev_pools_obj
def _copy_resources(self, compute_node, resources, initial=False):
"""Copy resource values to supplied compute_node."""
nodename = resources['hypervisor_hostname']
stats = self.stats[nodename]
# purge old stats and init with anything passed in by the driver
# NOTE(danms): Preserve 'failed_builds' across the stats clearing,
# as that is not part of resources
# TODO(danms): Stop doing this when we get a column to store this
# directly
prev_failed_builds = stats.get('failed_builds', 0)
stats.clear()
stats['failed_builds'] = prev_failed_builds
stats.digest_stats(resources.get('stats'))
compute_node.stats = stats
# Update the allocation ratios for the related ComputeNode object
# but only if the configured values are not the default; the
# ComputeNode._from_db_object method takes care of providing default
# allocation ratios when the config is left at the default, so
# we'll really end up with something like a
# ComputeNode.cpu_allocation_ratio of 16.0. We want to avoid
# resetting the ComputeNode fields to None because that will make
# the _resource_change method think something changed when really it
# didn't.
# NOTE(yikun): The CONF.initial_(cpu|ram|disk)_allocation_ratio would
# be used when we initialize the compute node object, that means the
# ComputeNode.(cpu|ram|disk)_allocation_ratio will be set to
# CONF.initial_(cpu|ram|disk)_allocation_ratio when initial flag is
# True.
for res in ('cpu', 'disk', 'ram'):
attr = '%s_allocation_ratio' % res
if initial:
conf_alloc_ratio = getattr(CONF, 'initial_%s' % attr)
else:
conf_alloc_ratio = getattr(self, attr)
# NOTE(yikun): In Stein version, we change the default value of
# (cpu|ram|disk)_allocation_ratio from 0.0 to None, but we still
# should allow 0.0 to keep compatibility, and this 0.0 condition
# will be removed in the next version (T version).
if conf_alloc_ratio not in (0.0, None):
setattr(compute_node, attr, conf_alloc_ratio)
# now copy rest to compute_node
compute_node.update_from_virt_driver(resources)
def remove_node(self, nodename):
"""Handle node removal/rebalance.
Clean up any stored data about a compute node no longer
managed by this host.
"""
self.stats.pop(nodename, None)
self.compute_nodes.pop(nodename, None)
self.old_resources.pop(nodename, None)
def _get_host_metrics(self, context, nodename):
"""Get the metrics from monitors and
notify information to message bus.
"""
metrics = objects.MonitorMetricList()
metrics_info = {}
for monitor in self.monitors:
try:
monitor.populate_metrics(metrics)
except NotImplementedError:
LOG.debug("The compute driver doesn't support host "
"metrics for %(mon)s", {'mon': monitor})
except Exception as exc:
LOG.warning("Cannot get the metrics from %(mon)s; "
"error: %(exc)s",
{'mon': monitor, 'exc': exc})
# TODO(jaypipes): Remove this when compute_node.metrics doesn't need
# to be populated as a JSONified string.
metric_list = metrics.to_list()
if len(metric_list):
metrics_info['nodename'] = nodename
metrics_info['metrics'] = metric_list
metrics_info['host'] = self.host
metrics_info['host_ip'] = CONF.my_ip
notifier = rpc.get_notifier(service='compute', host=nodename)
notifier.info(context, 'compute.metrics.update', metrics_info)
compute_utils.notify_about_metrics_update(
context, self.host, CONF.my_ip, nodename, metrics)
return metric_list
def update_available_resource(self, context, nodename, startup=False):
"""Override in-memory calculations of compute node resource usage based
on data audited from the hypervisor layer.
Add in resource claims in progress to account for operations that have
declared a need for resources, but not necessarily retrieved them from
the hypervisor layer yet.
:param nodename: Temporary parameter representing the Ironic resource
node. This parameter will be removed once Ironic
baremetal resource nodes are handled like any other
resource in the system.
:param startup: Boolean indicating whether we're running this on
on startup (True) or periodic (False).
"""
LOG.debug("Auditing locally available compute resources for "
"%(host)s (node: %(node)s)",
{'node': nodename,
'host': self.host})
resources = self.driver.get_available_resource(nodename)
# NOTE(jaypipes): The resources['hypervisor_hostname'] field now
# contains a non-None value, even for non-Ironic nova-compute hosts. It
# is this value that will be populated in the compute_nodes table.
resources['host_ip'] = CONF.my_ip
# We want the 'cpu_info' to be None from the POV of the
# virt driver, but the DB requires it to be non-null so
# just force it to empty string
if "cpu_info" not in resources or resources["cpu_info"] is None:
resources["cpu_info"] = ''
self._verify_resources(resources)
self._report_hypervisor_resource_view(resources)
self._update_available_resource(context, resources, startup=startup)
def _pair_instances_to_migrations(self, migrations, instance_by_uuid):
for migration in migrations:
try:
migration.instance = instance_by_uuid[migration.instance_uuid]
except KeyError:
# NOTE(danms): If this happens, we don't set it here, and
# let the code either fail or lazy-load the instance later
# which is what happened before we added this optimization.
# NOTE(tdurakov) this situation is possible for resize/cold
# migration when migration is finished but haven't yet
# confirmed/reverted in that case instance already changed host
# to destination and no matching happens
LOG.debug('Migration for instance %(uuid)s refers to '
'another host\'s instance!',
{'uuid': migration.instance_uuid})
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def _update_available_resource(self, context, resources, startup=False):
# initialize the compute node object, creating it
# if it does not already exist.
is_new_compute_node = self._init_compute_node(context, resources)
nodename = resources['hypervisor_hostname']
# if we could not init the compute node the tracker will be
# disabled and we should quit now
if self.disabled(nodename):
return
# Grab all instances assigned to this node:
instances = objects.InstanceList.get_by_host_and_node(
context, self.host, nodename,
expected_attrs=['system_metadata',
'numa_topology',
'flavor', 'migration_context'])
# Now calculate usage based on instance utilization:
instance_by_uuid = self._update_usage_from_instances(
context, instances, nodename)
# Grab all in-progress migrations:
migrations = objects.MigrationList.get_in_progress_by_host_and_node(
context, self.host, nodename)
self._pair_instances_to_migrations(migrations, instance_by_uuid)
self._update_usage_from_migrations(context, migrations, nodename)
# A new compute node means there won't be a resource provider yet since
# that would be created via the _update() call below, and if there is
# no resource provider then there are no allocations against it.
if not is_new_compute_node:
self._remove_deleted_instances_allocations(
context, self.compute_nodes[nodename], migrations,
instance_by_uuid)
# Detect and account for orphaned instances that may exist on the
# hypervisor, but are not in the DB:
orphans = self._find_orphaned_instances()
self._update_usage_from_orphans(orphans, nodename)
cn = self.compute_nodes[nodename]
# NOTE(yjiang5): Because pci device tracker status is not cleared in
# this periodic task, and also because the resource tracker is not
# notified when instances are deleted, we need remove all usages
# from deleted instances.
self.pci_tracker.clean_usage(instances, migrations, orphans)
dev_pools_obj = self.pci_tracker.stats.to_device_pools_obj()
cn.pci_device_pools = dev_pools_obj
self._report_final_resource_view(nodename)
metrics = self._get_host_metrics(context, nodename)
# TODO(pmurray): metrics should not be a json string in ComputeNode,
# but it is. This should be changed in ComputeNode
cn.metrics = jsonutils.dumps(metrics)
# update the compute_node
self._update(context, cn, startup=startup)
LOG.debug('Compute_service record updated for %(host)s:%(node)s',
{'host': self.host, 'node': nodename})
def _get_compute_node(self, context, nodename):
"""Returns compute node for the host and nodename."""
try:
return objects.ComputeNode.get_by_host_and_nodename(
context, self.host, nodename)
except exception.NotFound:
LOG.warning("No compute node record for %(host)s:%(node)s",
{'host': self.host, 'node': nodename})
def _report_hypervisor_resource_view(self, resources):
"""Log the hypervisor's view of free resources.
This is just a snapshot of resource usage recorded by the
virt driver.
The following resources are logged:
- free memory
- free disk
- free CPUs
- assignable PCI devices
"""
nodename = resources['hypervisor_hostname']
free_ram_mb = resources['memory_mb'] - resources['memory_mb_used']
free_disk_gb = resources['local_gb'] - resources['local_gb_used']
vcpus = resources['vcpus']
if vcpus:
free_vcpus = vcpus - resources['vcpus_used']
else:
free_vcpus = 'unknown'
pci_devices = resources.get('pci_passthrough_devices')
LOG.debug("Hypervisor/Node resource view: "
"name=%(node)s "
"free_ram=%(free_ram)sMB "
"free_disk=%(free_disk)sGB "
"free_vcpus=%(free_vcpus)s "
"pci_devices=%(pci_devices)s",
{'node': nodename,
'free_ram': free_ram_mb,
'free_disk': free_disk_gb,
'free_vcpus': free_vcpus,
'pci_devices': pci_devices})
def _report_final_resource_view(self, nodename):
"""Report final calculate of physical memory, used virtual memory,
disk, usable vCPUs, used virtual CPUs and PCI devices,
including instance calculations and in-progress resource claims. These
values will be exposed via the compute node table to the scheduler.
"""
cn = self.compute_nodes[nodename]
vcpus = cn.vcpus
if vcpus:
tcpu = vcpus
ucpu = cn.vcpus_used
LOG.debug("Total usable vcpus: %(tcpu)s, "
"total allocated vcpus: %(ucpu)s",
{'tcpu': vcpus,
'ucpu': ucpu})
else:
tcpu = 0
ucpu = 0
pci_stats = (list(cn.pci_device_pools) if
cn.pci_device_pools else [])
LOG.debug("Final resource view: "
"name=%(node)s "
"phys_ram=%(phys_ram)sMB "
"used_ram=%(used_ram)sMB "
"phys_disk=%(phys_disk)sGB "
"used_disk=%(used_disk)sGB "
"total_vcpus=%(total_vcpus)s "
"used_vcpus=%(used_vcpus)s "
"pci_stats=%(pci_stats)s",
{'node': nodename,
'phys_ram': cn.memory_mb,
'used_ram': cn.memory_mb_used,
'phys_disk': cn.local_gb,
'used_disk': cn.local_gb_used,
'total_vcpus': tcpu,
'used_vcpus': ucpu,
'pci_stats': pci_stats})
def _resource_change(self, compute_node):
"""Check to see if any resources have changed."""
nodename = compute_node.hypervisor_hostname
old_compute = self.old_resources[nodename]
if not obj_base.obj_equal_prims(
compute_node, old_compute, ['updated_at']):
self.old_resources[nodename] = copy.deepcopy(compute_node)
return True
return False
def _get_traits(self, nodename, provider_tree):
# Get the traits from the ProviderTree which will be the set
# of virt-owned traits plus any externally defined traits set
# on the provider that aren't owned by the virt driver.
traits = provider_tree.data(nodename).traits
# Now get the driver's capabilities and add any supported
# traits that are missing, and remove any existing set traits
# that are not currently supported.
for trait, supported in self.driver.capabilities_as_traits().items():
if supported:
traits.add(trait)
elif trait in traits:
traits.remove(trait)
return list(traits)
@retrying.retry(stop_max_attempt_number=4,
retry_on_exception=lambda e: isinstance(
e, exception.ResourceProviderUpdateConflict))
def _update_to_placement(self, context, compute_node, startup):
"""Send resource and inventory changes to placement."""
# NOTE(jianghuaw): Some resources(e.g. VGPU) are not saved in the
# object of compute_node; instead the inventory data for these
# resource is reported by driver's get_inventory(). So even there
# is no resource change for compute_node as above, we need proceed
# to get inventory and use report client interfaces to update
# inventory to placement. It's report client's responsibility to
# ensure the update request to placement only happens when inventory
# is changed.
nodename = compute_node.hypervisor_hostname
# Persist the stats to the Scheduler
# First try update_provider_tree
# Retrieve the provider tree associated with this compute node. If
# it doesn't exist yet, this will create it with a (single, root)
# provider corresponding to the compute node.
prov_tree = self.reportclient.get_provider_tree_and_ensure_root(
context, compute_node.uuid, name=compute_node.hypervisor_hostname)
# Let the virt driver rearrange the provider tree and set/update
# the inventory, traits, and aggregates throughout.
allocs = None
try:
try:
self.driver.update_provider_tree(prov_tree, nodename)
except exception.ReshapeNeeded:
if not startup:
# This isn't supposed to happen during periodic, so raise
# it up; the compute manager will treat it specially.
raise
LOG.info("Performing resource provider inventory and "
"allocation data migration during compute service "
"startup or fast-forward upgrade.")
allocs = self.reportclient.get_allocations_for_provider_tree(
context, nodename)
self.driver.update_provider_tree(prov_tree, nodename,
allocations=allocs)
# Inject driver capabilities traits into the provider
# tree. We need to determine the traits that the virt
# driver owns - so those that come from the tree itself
# (via the virt driver) plus the compute capabilities
# traits, and then merge those with the traits set
# externally that the driver does not own - and remove any
# set on the provider externally that the virt owns but
# aren't in the current list of supported traits. For
# example, let's say we reported multiattach support as a
# trait at t1 and then at t2 it's not, so we need to
# remove it. But at both t1 and t2 there is a
# CUSTOM_VENDOR_TRAIT_X which we can't touch because it
# was set externally on the provider.
traits = self._get_traits(nodename, provider_tree=prov_tree)
prov_tree.update_traits(nodename, traits)
except NotImplementedError:
# update_provider_tree isn't implemented yet - try get_inventory
try:
inv_data = self.driver.get_inventory(nodename)
_normalize_inventory_from_cn_obj(inv_data, compute_node)
except NotImplementedError:
# Eventually all virt drivers will return an inventory dict in
# the format that the placement API expects and we'll be able
# to remove this code branch
inv_data = compute_utils.compute_node_to_inventory_dict(
compute_node)
prov_tree.update_inventory(nodename, inv_data)
# Flush any changes. If we processed ReshapeNeeded above, allocs is not
# None, and this will hit placement's POST /reshaper route.
self.reportclient.update_from_provider_tree(context, prov_tree,
allocations=allocs)
def _update(self, context, compute_node, startup=False):
"""Update partial stats locally and populate them to Scheduler."""
if self._resource_change(compute_node):
# If the compute_node's resource changed, update to DB.
# NOTE(jianghuaw): Once we completely move to use get_inventory()
# for all resource provider's inv data. We can remove this check.
# At the moment we still need this check and save compute_node.
compute_node.save()
self._update_to_placement(context, compute_node, startup)
if self.pci_tracker:
self.pci_tracker.save(context)
def _update_usage(self, usage, nodename, sign=1):
mem_usage = usage['memory_mb']
disk_usage = usage.get('root_gb', 0)
vcpus_usage = usage.get('vcpus', 0)
overhead = self.driver.estimate_instance_overhead(usage)
mem_usage += overhead['memory_mb']
disk_usage += overhead.get('disk_gb', 0)
vcpus_usage += overhead.get('vcpus', 0)
cn = self.compute_nodes[nodename]
cn.memory_mb_used += sign * mem_usage
cn.local_gb_used += sign * disk_usage
cn.local_gb_used += sign * usage.get('ephemeral_gb', 0)
cn.local_gb_used += sign * usage.get('swap', 0) / 1024
cn.vcpus_used += sign * vcpus_usage
# free ram and disk may be negative, depending on policy:
cn.free_ram_mb = cn.memory_mb - cn.memory_mb_used
cn.free_disk_gb = cn.local_gb - cn.local_gb_used
stats = self.stats[nodename]
cn.running_vms = stats.num_instances
# Calculate the numa usage
free = sign == -1
updated_numa_topology = hardware.get_host_numa_usage_from_instance(
cn, usage, free)
cn.numa_topology = updated_numa_topology
def _get_migration_context_resource(self, resource, instance,
prefix='new_'):
migration_context = instance.migration_context
resource = prefix + resource
if migration_context and resource in migration_context:
return getattr(migration_context, resource)
return None
def _update_usage_from_migration(self, context, instance, migration,
nodename):
"""Update usage for a single migration. The record may
represent an incoming or outbound migration.
"""
if not _is_trackable_migration(migration):
return
uuid = migration.instance_uuid
LOG.info("Updating resource usage from migration %s", migration.uuid,
instance_uuid=uuid)
incoming = (migration.dest_compute == self.host and
migration.dest_node == nodename)
outbound = (migration.source_compute == self.host and
migration.source_node == nodename)
same_node = (incoming and outbound)
tracked = uuid in self.tracked_instances
itype = None
numa_topology = None
sign = 0
if same_node:
# Same node resize. Record usage for the 'new_' resources. This
# is executed on resize_claim().
if (instance['instance_type_id'] ==
migration.old_instance_type_id):
itype = self._get_instance_type(instance, 'new_', migration)
numa_topology = self._get_migration_context_resource(
'numa_topology', instance)
# Allocate pci device(s) for the instance.
sign = 1
else:
# The instance is already set to the new flavor (this is done
# by the compute manager on finish_resize()), hold space for a
# possible revert to the 'old_' resources.
# NOTE(lbeliveau): When the periodic audit timer gets
# triggered, the compute usage gets reset. The usage for an
# instance that is migrated to the new flavor but not yet
# confirmed/reverted will first get accounted for by
# _update_usage_from_instances(). This method will then be
# called, and we need to account for the '_old' resources
# (just in case).
itype = self._get_instance_type(instance, 'old_', migration)
numa_topology = self._get_migration_context_resource(
'numa_topology', instance, prefix='old_')
elif incoming and not tracked:
# instance has not yet migrated here:
itype = self._get_instance_type(instance, 'new_', migration)
numa_topology = self._get_migration_context_resource(
'numa_topology', instance)
# Allocate pci device(s) for the instance.
sign = 1
LOG.debug('Starting to track incoming migration %s with flavor %s',
migration.uuid, itype.flavorid, instance=instance)
elif outbound and not tracked:
# instance migrated, but record usage for a possible revert:
itype = self._get_instance_type(instance, 'old_', migration)
numa_topology = self._get_migration_context_resource(
'numa_topology', instance, prefix='old_')
LOG.debug('Starting to track outgoing migration %s with flavor %s',
migration.uuid, itype.flavorid, instance=instance)
if itype:
cn = self.compute_nodes[nodename]
usage = self._get_usage_dict(
itype, instance, numa_topology=numa_topology)
if self.pci_tracker and sign:
self.pci_tracker.update_pci_for_instance(
context, instance, sign=sign)
self._update_usage(usage, nodename)
if self.pci_tracker:
obj = self.pci_tracker.stats.to_device_pools_obj()
cn.pci_device_pools = obj
else:
obj = objects.PciDevicePoolList()
cn.pci_device_pools = obj
self.tracked_migrations[uuid] = migration
def _update_usage_from_migrations(self, context, migrations, nodename):
filtered = {}
instances = {}
self.tracked_migrations.clear()
# do some defensive filtering against bad migrations records in the
# database:
for migration in migrations:
uuid = migration.instance_uuid
try:
if uuid not in instances:
instances[uuid] = migration.instance
except exception.InstanceNotFound as e:
# migration referencing deleted instance
LOG.debug('Migration instance not found: %s', e)
continue
# skip migration if instance isn't in a resize state:
if not _instance_in_resize_state(instances[uuid]):
LOG.warning("Instance not resizing, skipping migration.",
instance_uuid=uuid)
continue
# filter to most recently updated migration for each instance:
other_migration = filtered.get(uuid, None)
# NOTE(claudiub): In Python 3, you cannot compare NoneTypes.
if other_migration:
om = other_migration
other_time = om.updated_at or om.created_at
migration_time = migration.updated_at or migration.created_at
if migration_time > other_time:
filtered[uuid] = migration
else:
filtered[uuid] = migration
for migration in filtered.values():
instance = instances[migration.instance_uuid]
# Skip migration (and mark it as error) if it doesn't match the
# instance migration id.
# This can happen if we have a stale migration record.
# We want to proceed if instance.migration_context is None
if (instance.migration_context is not None and
instance.migration_context.migration_id != migration.id):
LOG.info("Current instance migration %(im)s doesn't match "
"migration %(m)s, marking migration as error. "
"This can occur if a previous migration for this "
"instance did not complete.",
{'im': instance.migration_context.migration_id,
'm': migration.id})
migration.status = "error"
migration.save()
continue
try:
self._update_usage_from_migration(context, instance, migration,
nodename)
except exception.FlavorNotFound:
LOG.warning("Flavor could not be found, skipping migration.",
instance_uuid=instance.uuid)
continue
def _update_usage_from_instance(self, context, instance, nodename,
is_removed=False):
"""Update usage for a single instance."""
uuid = instance['uuid']
is_new_instance = uuid not in self.tracked_instances
# NOTE(sfinucan): Both brand new instances as well as instances that
# are being unshelved will have is_new_instance == True
is_removed_instance = not is_new_instance and (is_removed or
instance['vm_state'] in vm_states.ALLOW_RESOURCE_REMOVAL)
if is_new_instance:
self.tracked_instances.add(uuid)
sign = 1
if is_removed_instance:
self.tracked_instances.remove(uuid)
sign = -1
cn = self.compute_nodes[nodename]
stats = self.stats[nodename]
stats.update_stats_for_instance(instance, is_removed_instance)
cn.stats = stats
# if it's a new or deleted instance:
if is_new_instance or is_removed_instance:
if self.pci_tracker:
self.pci_tracker.update_pci_for_instance(context,
instance,
sign=sign)
# new instance, update compute node resource usage:
self._update_usage(self._get_usage_dict(instance, instance),
nodename, sign=sign)
# Stop tracking removed instances in the is_bfv cache. This needs to
# happen *after* calling _get_usage_dict() since that relies on the
# is_bfv cache.
if is_removed_instance and uuid in self.is_bfv:
del self.is_bfv[uuid]
cn.current_workload = stats.calculate_workload()
if self.pci_tracker:
obj = self.pci_tracker.stats.to_device_pools_obj()
cn.pci_device_pools = obj
else:
cn.pci_device_pools = objects.PciDevicePoolList()
def _update_usage_from_instances(self, context, instances, nodename):
"""Calculate resource usage based on instance utilization. This is
different than the hypervisor's view as it will account for all
instances assigned to the local compute host, even if they are not
currently powered on.
"""
self.tracked_instances.clear()
cn = self.compute_nodes[nodename]
# set some initial values, reserve room for host/hypervisor:
cn.local_gb_used = CONF.reserved_host_disk_mb / 1024
cn.memory_mb_used = CONF.reserved_host_memory_mb
cn.vcpus_used = CONF.reserved_host_cpus
cn.free_ram_mb = (cn.memory_mb - cn.memory_mb_used)
cn.free_disk_gb = (cn.local_gb - cn.local_gb_used)
cn.current_workload = 0
cn.running_vms = 0
instance_by_uuid = {}
for instance in instances:
if instance.vm_state not in vm_states.ALLOW_RESOURCE_REMOVAL:
self._update_usage_from_instance(context, instance, nodename)
instance_by_uuid[instance.uuid] = instance
return instance_by_uuid
def _remove_deleted_instances_allocations(self, context, cn,
migrations, instance_by_uuid):
migration_uuids = [migration.uuid for migration in migrations
if 'uuid' in migration]
# NOTE(jaypipes): All of this code sucks. It's basically dealing with
# all the corner cases in move, local delete, unshelve and rebuild
# operations for when allocations should be deleted when things didn't
# happen according to the normal flow of events where the scheduler
# always creates allocations for an instance
try:
# pai: report.ProviderAllocInfo namedtuple
pai = self.reportclient.get_allocations_for_resource_provider(
context, cn.uuid)
except (exception.ResourceProviderAllocationRetrievalFailed,
ks_exc.ClientException) as e:
LOG.error("Skipping removal of allocations for deleted instances: "
"%s", e)
return
allocations = pai.allocations
if not allocations:
# The main loop below would short-circuit anyway, but this saves us
# the (potentially expensive) context.elevated construction below.
return
read_deleted_context = context.elevated(read_deleted='yes')
for consumer_uuid, alloc in allocations.items():
if consumer_uuid in self.tracked_instances:
LOG.debug("Instance %s actively managed on this compute host "
"and has allocations in placement: %s.",
consumer_uuid, alloc)
continue
if consumer_uuid in migration_uuids:
LOG.debug("Migration %s is active on this compute host "
"and has allocations in placement: %s.",
consumer_uuid, alloc)
continue
# We know these are instances now, so proceed
instance_uuid = consumer_uuid
instance = instance_by_uuid.get(instance_uuid)
if not instance:
try:
instance = objects.Instance.get_by_uuid(
read_deleted_context, consumer_uuid,
expected_attrs=[])
except exception.InstanceNotFound:
# The instance isn't even in the database. Either the
# scheduler _just_ created an allocation for it and we're
# racing with the creation in the cell database, or the
# instance was deleted and fully archived before we got a
# chance to run this. The former is far more likely than
# the latter. Avoid deleting allocations for a building
# instance here.
LOG.info("Instance %(uuid)s has allocations against this "
"compute host but is not found in the database.",
{'uuid': instance_uuid},
exc_info=False)
continue
if instance.deleted:
# The instance is gone, so we definitely want to remove
# allocations associated with it.
# NOTE(jaypipes): This will not be true if/when we support
# cross-cell migrations...
LOG.debug("Instance %s has been deleted (perhaps locally). "
"Deleting allocations that remained for this "
"instance against this compute host: %s.",
instance_uuid, alloc)
self.reportclient.delete_allocation_for_instance(context,
instance_uuid)
continue
if not instance.host:
# Allocations related to instances being scheduled should not
# be deleted if we already wrote the allocation previously.
LOG.debug("Instance %s has been scheduled to this compute "
"host, the scheduler has made an allocation "
"against this compute node but the instance has "
"yet to start. Skipping heal of allocation: %s.",
instance_uuid, alloc)
continue
if (instance.host == cn.host and
instance.node == cn.hypervisor_hostname):
# The instance is supposed to be on this compute host but is
# not in the list of actively managed instances.
LOG.warning("Instance %s is not being actively managed by "
"this compute host but has allocations "
"referencing this compute host: %s. Skipping "
"heal of allocation because we do not know "
"what to do.", instance_uuid, alloc)
continue
if instance.host != cn.host:
# The instance has been moved to another host either via a
# migration, evacuation or unshelve in between the time when we
# ran InstanceList.get_by_host_and_node(), added those
# instances to RT.tracked_instances and the above
# Instance.get_by_uuid() call. We SHOULD attempt to remove any
# allocations that reference this compute host if the VM is in
# a stable terminal state (i.e. it isn't in a state of waiting
# for resize to confirm/revert), however if the destination
# host is an Ocata compute host, it will delete the allocation
# that contains this source compute host information anyway and
# recreate an allocation that only refers to itself. So we
# don't need to do anything in that case. Just log the
# situation here for information but don't attempt to delete or
# change the allocation.
LOG.warning("Instance %s has been moved to another host "
"%s(%s). There are allocations remaining against "
"the source host that might need to be removed: "
"%s.",
instance_uuid, instance.host, instance.node, alloc)
def delete_allocation_for_evacuated_instance(self, context, instance, node,
node_type='source'):
# Clean up the instance allocation from this node in placement
cn_uuid = self.compute_nodes[node].uuid
if not self.reportclient.remove_provider_tree_from_instance_allocation(
context, instance.uuid, cn_uuid):
LOG.error("Failed to clean allocation of evacuated "
"instance on the %s node %s",
node_type, cn_uuid, instance=instance)
def _find_orphaned_instances(self):
"""Given the set of instances and migrations already account for
by resource tracker, sanity check the hypervisor to determine
if there are any "orphaned" instances left hanging around.
Orphans could be consuming memory and should be accounted for in
usage calculations to guard against potential out of memory
errors.
"""
uuids1 = frozenset(self.tracked_instances)
uuids2 = frozenset(self.tracked_migrations.keys())
uuids = uuids1 | uuids2
usage = self.driver.get_per_instance_usage()
vuuids = frozenset(usage.keys())
orphan_uuids = vuuids - uuids
orphans = [usage[uuid] for uuid in orphan_uuids]
return orphans
def _update_usage_from_orphans(self, orphans, nodename):
"""Include orphaned instances in usage."""
for orphan in orphans:
memory_mb = orphan['memory_mb']
LOG.warning("Detected running orphan instance: %(uuid)s "
"(consuming %(memory_mb)s MB memory)",
{'uuid': orphan['uuid'], 'memory_mb': memory_mb})
# just record memory usage for the orphan
usage = {'memory_mb': memory_mb}
self._update_usage(usage, nodename)
def delete_allocation_for_shelve_offloaded_instance(self, context,
instance):
self.reportclient.delete_allocation_for_instance(context,
instance.uuid)
def _verify_resources(self, resources):
resource_keys = ["vcpus", "memory_mb", "local_gb", "cpu_info",
"vcpus_used", "memory_mb_used", "local_gb_used",
"numa_topology"]
missing_keys = [k for k in resource_keys if k not in resources]
if missing_keys:
reason = _("Missing keys: %s") % missing_keys
raise exception.InvalidInput(reason=reason)
def _get_instance_type(self, instance, prefix, migration):
"""Get the instance type from instance."""
stashed_flavors = migration.migration_type in ('resize',)
if stashed_flavors:
return getattr(instance, '%sflavor' % prefix)
else:
# NOTE(ndipanov): Certain migration types (all but resize)
# do not change flavors so there is no need to stash
# them. In that case - just get the instance flavor.
return instance.flavor
def _get_usage_dict(self, object_or_dict, instance, **updates):
"""Make a usage dict _update methods expect.
Accepts a dict or an Instance or Flavor object, and a set of updates.
Converts the object to a dict and applies the updates.
:param object_or_dict: instance or flavor as an object or just a dict
:param instance: nova.objects.Instance for the related operation; this
is needed to determine if the instance is
volume-backed
:param updates: key-value pairs to update the passed object.
Currently only considers 'numa_topology', all other
keys are ignored.
:returns: a dict with all the information from object_or_dict updated
with updates
"""
def _is_bfv():
# Check to see if we have the is_bfv value cached.
if instance.uuid in self.is_bfv:
is_bfv = self.is_bfv[instance.uuid]
else:
is_bfv = compute_utils.is_volume_backed_instance(
instance._context, instance)
self.is_bfv[instance.uuid] = is_bfv
return is_bfv
usage = {}
if isinstance(object_or_dict, objects.Instance):
is_bfv = _is_bfv()
usage = {'memory_mb': object_or_dict.flavor.memory_mb,
'swap': object_or_dict.flavor.swap,
'vcpus': object_or_dict.flavor.vcpus,
'root_gb': (0 if is_bfv else
object_or_dict.flavor.root_gb),
'ephemeral_gb': object_or_dict.flavor.ephemeral_gb,
'numa_topology': object_or_dict.numa_topology}
elif isinstance(object_or_dict, objects.Flavor):
usage = obj_base.obj_to_primitive(object_or_dict)
if _is_bfv():
usage['root_gb'] = 0
else:
usage.update(object_or_dict)
for key in ('numa_topology',):
if key in updates:
usage[key] = updates[key]
return usage
def build_failed(self, nodename):
"""Increments the failed_builds stats for the given node."""
self.stats[nodename].build_failed()
def build_succeeded(self, nodename):
"""Resets the failed_builds stats for the given node."""
self.stats[nodename].build_succeeded()
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def claim_pci_devices(self, context, pci_requests):
"""Claim instance PCI resources
:param context: security context
:param pci_requests: a list of nova.objects.InstancePCIRequests
:returns: a list of nova.objects.PciDevice objects
"""
result = self.pci_tracker.claim_instance(
context, pci_requests, None)
self.pci_tracker.save(context)
return result
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def allocate_pci_devices_for_instance(self, context, instance):
"""Allocate instance claimed PCI resources
:param context: security context
:param instance: instance object
"""
self.pci_tracker.allocate_instance(instance)
self.pci_tracker.save(context)
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def free_pci_device_allocations_for_instance(self, context, instance):
"""Free instance allocated PCI resources
:param context: security context
:param instance: instance object
"""
self.pci_tracker.free_instance_allocations(context, instance)
self.pci_tracker.save(context)
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def free_pci_device_claims_for_instance(self, context, instance):
"""Free instance claimed PCI resources
:param context: security context
:param instance: instance object
"""
self.pci_tracker.free_instance_claims(context, instance)
self.pci_tracker.save(context)
| 46.408769 | 79 | 0.621367 |
import collections
import copy
from keystoneauth1 import exceptions as ks_exc
import os_resource_classes as orc
from oslo_log import log as logging
from oslo_serialization import jsonutils
import retrying
from nova.compute import claims
from nova.compute import monitors
from nova.compute import stats as compute_stats
from nova.compute import task_states
from nova.compute import utils as compute_utils
from nova.compute import vm_states
import nova.conf
from nova import exception
from nova.i18n import _
from nova import objects
from nova.objects import base as obj_base
from nova.objects import migration as migration_obj
from nova.pci import manager as pci_manager
from nova.pci import request as pci_request
from nova import rpc
from nova.scheduler.client import report
from nova import utils
from nova.virt import hardware
CONF = nova.conf.CONF
LOG = logging.getLogger(__name__)
COMPUTE_RESOURCE_SEMAPHORE = "compute_resources"
def _instance_in_resize_state(instance):
vm = instance.vm_state
task = instance.task_state
if vm == vm_states.RESIZED:
return True
if (vm in [vm_states.ACTIVE, vm_states.STOPPED]
and task in (
task_states.resizing_states + task_states.rebuild_states)):
return True
return False
def _is_trackable_migration(migration):
return migration.migration_type in ('resize', 'migration',
'evacuation')
def _normalize_inventory_from_cn_obj(inv_data, cn):
if orc.VCPU in inv_data:
cpu_inv = inv_data[orc.VCPU]
if 'allocation_ratio' not in cpu_inv:
cpu_inv['allocation_ratio'] = cn.cpu_allocation_ratio
if 'reserved' not in cpu_inv:
cpu_inv['reserved'] = CONF.reserved_host_cpus
if orc.MEMORY_MB in inv_data:
mem_inv = inv_data[orc.MEMORY_MB]
if 'allocation_ratio' not in mem_inv:
mem_inv['allocation_ratio'] = cn.ram_allocation_ratio
if 'reserved' not in mem_inv:
mem_inv['reserved'] = CONF.reserved_host_memory_mb
if orc.DISK_GB in inv_data:
disk_inv = inv_data[orc.DISK_GB]
if 'allocation_ratio' not in disk_inv:
disk_inv['allocation_ratio'] = cn.disk_allocation_ratio
if 'reserved' not in disk_inv:
reserved_mb = CONF.reserved_host_disk_mb
reserved_gb = compute_utils.convert_mb_to_ceil_gb(reserved_mb)
disk_inv['reserved'] = reserved_gb
class ResourceTracker(object):
def __init__(self, host, driver):
self.host = host
self.driver = driver
self.pci_tracker = None
self.compute_nodes = {}
self.stats = collections.defaultdict(compute_stats.Stats)
self.tracked_instances = set()
self.tracked_migrations = {}
self.is_bfv = {}
monitor_handler = monitors.MonitorHandler(self)
self.monitors = monitor_handler.monitors
self.old_resources = collections.defaultdict(objects.ComputeNode)
self.reportclient = report.SchedulerReportClient()
self.ram_allocation_ratio = CONF.ram_allocation_ratio
self.cpu_allocation_ratio = CONF.cpu_allocation_ratio
self.disk_allocation_ratio = CONF.disk_allocation_ratio
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def instance_claim(self, context, instance, nodename, limits=None):
if self.disabled(nodename):
self._set_instance_host_and_node(instance, nodename)
return claims.NopClaim()
if instance.host:
LOG.warning("Host field should not be set on the instance "
"until resources have been claimed.",
instance=instance)
if instance.node:
LOG.warning("Node field should not be set on the instance "
"until resources have been claimed.",
instance=instance)
overhead = self.driver.estimate_instance_overhead(instance)
LOG.debug("Memory overhead for %(flavor)d MB instance; %(overhead)d "
"MB", {'flavor': instance.flavor.memory_mb,
'overhead': overhead['memory_mb']})
LOG.debug("Disk overhead for %(flavor)d GB instance; %(overhead)d "
"GB", {'flavor': instance.flavor.root_gb,
'overhead': overhead.get('disk_gb', 0)})
LOG.debug("CPU overhead for %(flavor)d vCPUs instance; %(overhead)d "
"vCPU(s)", {'flavor': instance.flavor.vcpus,
'overhead': overhead.get('vcpus', 0)})
cn = self.compute_nodes[nodename]
pci_requests = objects.InstancePCIRequests.get_by_instance_uuid(
context, instance.uuid)
claim = claims.Claim(context, instance, nodename, self, cn,
pci_requests, overhead=overhead, limits=limits)
instance_numa_topology = claim.claimed_numa_topology
instance.numa_topology = instance_numa_topology
self._set_instance_host_and_node(instance, nodename)
if self.pci_tracker:
# NOTE(jaypipes): ComputeNode.pci_device_pools is set below
# in _update_usage_from_instance().
self.pci_tracker.claim_instance(context, pci_requests,
instance_numa_topology)
# Mark resources in-use and update stats
self._update_usage_from_instance(context, instance, nodename)
elevated = context.elevated()
# persist changes to the compute node:
self._update(elevated, cn)
return claim
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def rebuild_claim(self, context, instance, nodename, limits=None,
image_meta=None, migration=None):
instance_type = instance.flavor
return self._move_claim(context, instance, instance_type, nodename,
migration, move_type='evacuation',
limits=limits, image_meta=image_meta)
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def resize_claim(self, context, instance, instance_type, nodename,
migration, image_meta=None, limits=None):
return self._move_claim(context, instance, instance_type, nodename,
migration, image_meta=image_meta,
limits=limits)
def _move_claim(self, context, instance, new_instance_type, nodename,
migration, move_type=None, image_meta=None, limits=None):
image_meta = image_meta or {}
if migration:
self._claim_existing_migration(migration, nodename)
else:
migration = self._create_migration(context, instance,
new_instance_type,
nodename, move_type)
if self.disabled(nodename):
# compute_driver doesn't support resource tracking, just
return claims.NopClaim(migration=migration)
overhead = self.driver.estimate_instance_overhead(new_instance_type)
LOG.debug("Memory overhead for %(flavor)d MB instance; %(overhead)d "
"MB", {'flavor': new_instance_type.memory_mb,
'overhead': overhead['memory_mb']})
LOG.debug("Disk overhead for %(flavor)d GB instance; %(overhead)d "
"GB", {'flavor': instance.flavor.root_gb,
'overhead': overhead.get('disk_gb', 0)})
LOG.debug("CPU overhead for %(flavor)d vCPUs instance; %(overhead)d "
"vCPU(s)", {'flavor': instance.flavor.vcpus,
'overhead': overhead.get('vcpus', 0)})
cn = self.compute_nodes[nodename]
new_pci_requests = pci_request.get_pci_requests_from_flavor(
new_instance_type)
new_pci_requests.instance_uuid = instance.uuid
# On resize merge the SR-IOV ports pci_requests with the new
# instance flavor pci_requests.
if instance.pci_requests:
for request in instance.pci_requests.requests:
if request.alias_name is None:
new_pci_requests.requests.append(request)
claim = claims.MoveClaim(context, instance, nodename,
new_instance_type, image_meta, self, cn,
new_pci_requests, overhead=overhead,
limits=limits)
claim.migration = migration
claimed_pci_devices_objs = []
if self.pci_tracker:
# NOTE(jaypipes): ComputeNode.pci_device_pools is set below
# in _update_usage_from_instance().
claimed_pci_devices_objs = self.pci_tracker.claim_instance(
context, new_pci_requests, claim.claimed_numa_topology)
claimed_pci_devices = objects.PciDeviceList(
objects=claimed_pci_devices_objs)
# TODO(jaypipes): Move claimed_numa_topology out of the Claim's
mig_context = objects.MigrationContext(
context=context, instance_uuid=instance.uuid,
migration_id=migration.id,
old_numa_topology=instance.numa_topology,
new_numa_topology=claim.claimed_numa_topology,
old_pci_devices=instance.pci_devices,
new_pci_devices=claimed_pci_devices,
old_pci_requests=instance.pci_requests,
new_pci_requests=new_pci_requests)
instance.migration_context = mig_context
instance.save()
self._update_usage_from_migration(context, instance, migration,
nodename)
elevated = context.elevated()
self._update(elevated, cn)
return claim
def _create_migration(self, context, instance, new_instance_type,
nodename, move_type=None):
migration = objects.Migration(context=context.elevated())
migration.dest_compute = self.host
migration.dest_node = nodename
migration.dest_host = self.driver.get_host_ip_addr()
migration.old_instance_type_id = instance.flavor.id
migration.new_instance_type_id = new_instance_type.id
migration.status = 'pre-migrating'
migration.instance_uuid = instance.uuid
migration.source_compute = instance.host
migration.source_node = instance.node
if move_type:
migration.migration_type = move_type
else:
migration.migration_type = migration_obj.determine_migration_type(
migration)
migration.create()
return migration
def _claim_existing_migration(self, migration, nodename):
migration.dest_compute = self.host
migration.dest_node = nodename
migration.dest_host = self.driver.get_host_ip_addr()
migration.status = 'pre-migrating'
migration.save()
def _set_instance_host_and_node(self, instance, nodename):
instance.host = self.host
instance.launched_on = self.host
instance.node = nodename
instance.save()
def _unset_instance_host_and_node(self, instance):
instance.host = None
instance.node = None
instance.save()
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def abort_instance_claim(self, context, instance, nodename):
self._update_usage_from_instance(context, instance, nodename,
is_removed=True)
instance.clear_numa_topology()
self._unset_instance_host_and_node(instance)
self._update(context.elevated(), self.compute_nodes[nodename])
def _drop_pci_devices(self, instance, nodename, prefix):
if self.pci_tracker:
pci_devices = self._get_migration_context_resource(
'pci_devices', instance, prefix=prefix)
if pci_devices:
for pci_device in pci_devices:
self.pci_tracker.free_device(pci_device, instance)
dev_pools_obj = self.pci_tracker.stats.to_device_pools_obj()
self.compute_nodes[nodename].pci_device_pools = dev_pools_obj
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def drop_move_claim(self, context, instance, nodename,
instance_type=None, prefix='new_'):
if instance['uuid'] in self.tracked_migrations:
migration = self.tracked_migrations.pop(instance['uuid'])
if not instance_type:
instance_type = self._get_instance_type(instance, prefix,
migration)
elif instance['uuid'] in self.tracked_instances:
self.tracked_instances.remove(instance['uuid'])
if instance_type is not None:
numa_topology = self._get_migration_context_resource(
'numa_topology', instance, prefix=prefix)
usage = self._get_usage_dict(
instance_type, instance, numa_topology=numa_topology)
self._drop_pci_devices(instance, nodename, prefix)
self._update_usage(usage, nodename, sign=-1)
ctxt = context.elevated()
self._update(ctxt, self.compute_nodes[nodename])
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def update_usage(self, context, instance, nodename):
if self.disabled(nodename):
return
uuid = instance['uuid']
# claim first:
if uuid in self.tracked_instances:
self._update_usage_from_instance(context, instance, nodename)
self._update(context.elevated(), self.compute_nodes[nodename])
def disabled(self, nodename):
return (nodename not in self.compute_nodes or
not self.driver.node_is_available(nodename))
def _check_for_nodes_rebalance(self, context, resources, nodename):
if not self.driver.rebalances_nodes:
return False
# Its possible ironic just did a node re-balance, so let's
cn_candidates = objects.ComputeNodeList.get_by_hypervisor(
context, nodename)
if len(cn_candidates) == 1:
cn = cn_candidates[0]
LOG.info("ComputeNode %(name)s moving from %(old)s to %(new)s",
{"name": nodename, "old": cn.host, "new": self.host})
cn.host = self.host
self.compute_nodes[nodename] = cn
self._copy_resources(cn, resources)
self._setup_pci_tracker(context, cn, resources)
self._update(context, cn)
return True
elif len(cn_candidates) > 1:
LOG.error(
"Found more than one ComputeNode for nodename %s. "
"Please clean up the orphaned ComputeNode records in your DB.",
nodename)
return False
def _init_compute_node(self, context, resources):
nodename = resources['hypervisor_hostname']
if nodename in self.compute_nodes:
cn = self.compute_nodes[nodename]
self._copy_resources(cn, resources)
self._setup_pci_tracker(context, cn, resources)
return False
cn = self._get_compute_node(context, nodename)
if cn:
self.compute_nodes[nodename] = cn
self._copy_resources(cn, resources)
self._setup_pci_tracker(context, cn, resources)
return False
if self._check_for_nodes_rebalance(context, resources, nodename):
return False
cn = objects.ComputeNode(context)
cn.host = self.host
self._copy_resources(cn, resources, initial=True)
self.compute_nodes[nodename] = cn
cn.create()
LOG.info('Compute node record created for '
'%(host)s:%(node)s with uuid: %(uuid)s',
{'host': self.host, 'node': nodename, 'uuid': cn.uuid})
self._setup_pci_tracker(context, cn, resources)
return True
def _setup_pci_tracker(self, context, compute_node, resources):
if not self.pci_tracker:
n_id = compute_node.id
self.pci_tracker = pci_manager.PciDevTracker(context, node_id=n_id)
if 'pci_passthrough_devices' in resources:
dev_json = resources.pop('pci_passthrough_devices')
self.pci_tracker.update_devices_from_hypervisor_resources(
dev_json)
dev_pools_obj = self.pci_tracker.stats.to_device_pools_obj()
compute_node.pci_device_pools = dev_pools_obj
def _copy_resources(self, compute_node, resources, initial=False):
nodename = resources['hypervisor_hostname']
stats = self.stats[nodename]
prev_failed_builds = stats.get('failed_builds', 0)
stats.clear()
stats['failed_builds'] = prev_failed_builds
stats.digest_stats(resources.get('stats'))
compute_node.stats = stats
# ComputeNode.cpu_allocation_ratio of 16.0. We want to avoid
# resetting the ComputeNode fields to None because that will make
# the _resource_change method think something changed when really it
# didn't.
for res in ('cpu', 'disk', 'ram'):
attr = '%s_allocation_ratio' % res
if initial:
conf_alloc_ratio = getattr(CONF, 'initial_%s' % attr)
else:
conf_alloc_ratio = getattr(self, attr)
if conf_alloc_ratio not in (0.0, None):
setattr(compute_node, attr, conf_alloc_ratio)
compute_node.update_from_virt_driver(resources)
def remove_node(self, nodename):
self.stats.pop(nodename, None)
self.compute_nodes.pop(nodename, None)
self.old_resources.pop(nodename, None)
def _get_host_metrics(self, context, nodename):
metrics = objects.MonitorMetricList()
metrics_info = {}
for monitor in self.monitors:
try:
monitor.populate_metrics(metrics)
except NotImplementedError:
LOG.debug("The compute driver doesn't support host "
"metrics for %(mon)s", {'mon': monitor})
except Exception as exc:
LOG.warning("Cannot get the metrics from %(mon)s; "
"error: %(exc)s",
{'mon': monitor, 'exc': exc})
# TODO(jaypipes): Remove this when compute_node.metrics doesn't need
metric_list = metrics.to_list()
if len(metric_list):
metrics_info['nodename'] = nodename
metrics_info['metrics'] = metric_list
metrics_info['host'] = self.host
metrics_info['host_ip'] = CONF.my_ip
notifier = rpc.get_notifier(service='compute', host=nodename)
notifier.info(context, 'compute.metrics.update', metrics_info)
compute_utils.notify_about_metrics_update(
context, self.host, CONF.my_ip, nodename, metrics)
return metric_list
def update_available_resource(self, context, nodename, startup=False):
LOG.debug("Auditing locally available compute resources for "
"%(host)s (node: %(node)s)",
{'node': nodename,
'host': self.host})
resources = self.driver.get_available_resource(nodename)
resources['host_ip'] = CONF.my_ip
if "cpu_info" not in resources or resources["cpu_info"] is None:
resources["cpu_info"] = ''
self._verify_resources(resources)
self._report_hypervisor_resource_view(resources)
self._update_available_resource(context, resources, startup=startup)
def _pair_instances_to_migrations(self, migrations, instance_by_uuid):
for migration in migrations:
try:
migration.instance = instance_by_uuid[migration.instance_uuid]
except KeyError:
# let the code either fail or lazy-load the instance later
# which is what happened before we added this optimization.
# NOTE(tdurakov) this situation is possible for resize/cold
# migration when migration is finished but haven't yet
LOG.debug('Migration for instance %(uuid)s refers to '
'another host\'s instance!',
{'uuid': migration.instance_uuid})
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def _update_available_resource(self, context, resources, startup=False):
# initialize the compute node object, creating it
# if it does not already exist.
is_new_compute_node = self._init_compute_node(context, resources)
nodename = resources['hypervisor_hostname']
# if we could not init the compute node the tracker will be
# disabled and we should quit now
if self.disabled(nodename):
return
# Grab all instances assigned to this node:
instances = objects.InstanceList.get_by_host_and_node(
context, self.host, nodename,
expected_attrs=['system_metadata',
'numa_topology',
'flavor', 'migration_context'])
# Now calculate usage based on instance utilization:
instance_by_uuid = self._update_usage_from_instances(
context, instances, nodename)
# Grab all in-progress migrations:
migrations = objects.MigrationList.get_in_progress_by_host_and_node(
context, self.host, nodename)
self._pair_instances_to_migrations(migrations, instance_by_uuid)
self._update_usage_from_migrations(context, migrations, nodename)
# A new compute node means there won't be a resource provider yet since
if not is_new_compute_node:
self._remove_deleted_instances_allocations(
context, self.compute_nodes[nodename], migrations,
instance_by_uuid)
orphans = self._find_orphaned_instances()
self._update_usage_from_orphans(orphans, nodename)
cn = self.compute_nodes[nodename]
self.pci_tracker.clean_usage(instances, migrations, orphans)
dev_pools_obj = self.pci_tracker.stats.to_device_pools_obj()
cn.pci_device_pools = dev_pools_obj
self._report_final_resource_view(nodename)
metrics = self._get_host_metrics(context, nodename)
cn.metrics = jsonutils.dumps(metrics)
self._update(context, cn, startup=startup)
LOG.debug('Compute_service record updated for %(host)s:%(node)s',
{'host': self.host, 'node': nodename})
def _get_compute_node(self, context, nodename):
try:
return objects.ComputeNode.get_by_host_and_nodename(
context, self.host, nodename)
except exception.NotFound:
LOG.warning("No compute node record for %(host)s:%(node)s",
{'host': self.host, 'node': nodename})
def _report_hypervisor_resource_view(self, resources):
nodename = resources['hypervisor_hostname']
free_ram_mb = resources['memory_mb'] - resources['memory_mb_used']
free_disk_gb = resources['local_gb'] - resources['local_gb_used']
vcpus = resources['vcpus']
if vcpus:
free_vcpus = vcpus - resources['vcpus_used']
else:
free_vcpus = 'unknown'
pci_devices = resources.get('pci_passthrough_devices')
LOG.debug("Hypervisor/Node resource view: "
"name=%(node)s "
"free_ram=%(free_ram)sMB "
"free_disk=%(free_disk)sGB "
"free_vcpus=%(free_vcpus)s "
"pci_devices=%(pci_devices)s",
{'node': nodename,
'free_ram': free_ram_mb,
'free_disk': free_disk_gb,
'free_vcpus': free_vcpus,
'pci_devices': pci_devices})
def _report_final_resource_view(self, nodename):
cn = self.compute_nodes[nodename]
vcpus = cn.vcpus
if vcpus:
tcpu = vcpus
ucpu = cn.vcpus_used
LOG.debug("Total usable vcpus: %(tcpu)s, "
"total allocated vcpus: %(ucpu)s",
{'tcpu': vcpus,
'ucpu': ucpu})
else:
tcpu = 0
ucpu = 0
pci_stats = (list(cn.pci_device_pools) if
cn.pci_device_pools else [])
LOG.debug("Final resource view: "
"name=%(node)s "
"phys_ram=%(phys_ram)sMB "
"used_ram=%(used_ram)sMB "
"phys_disk=%(phys_disk)sGB "
"used_disk=%(used_disk)sGB "
"total_vcpus=%(total_vcpus)s "
"used_vcpus=%(used_vcpus)s "
"pci_stats=%(pci_stats)s",
{'node': nodename,
'phys_ram': cn.memory_mb,
'used_ram': cn.memory_mb_used,
'phys_disk': cn.local_gb,
'used_disk': cn.local_gb_used,
'total_vcpus': tcpu,
'used_vcpus': ucpu,
'pci_stats': pci_stats})
def _resource_change(self, compute_node):
nodename = compute_node.hypervisor_hostname
old_compute = self.old_resources[nodename]
if not obj_base.obj_equal_prims(
compute_node, old_compute, ['updated_at']):
self.old_resources[nodename] = copy.deepcopy(compute_node)
return True
return False
def _get_traits(self, nodename, provider_tree):
traits = provider_tree.data(nodename).traits
# Now get the driver's capabilities and add any supported
for trait, supported in self.driver.capabilities_as_traits().items():
if supported:
traits.add(trait)
elif trait in traits:
traits.remove(trait)
return list(traits)
@retrying.retry(stop_max_attempt_number=4,
retry_on_exception=lambda e: isinstance(
e, exception.ResourceProviderUpdateConflict))
def _update_to_placement(self, context, compute_node, startup):
# is no resource change for compute_node as above, we need proceed
# to get inventory and use report client interfaces to update
# inventory to placement. It's report client's responsibility to
# ensure the update request to placement only happens when inventory
# is changed.
nodename = compute_node.hypervisor_hostname
# Persist the stats to the Scheduler
# First try update_provider_tree
# Retrieve the provider tree associated with this compute node. If
# it doesn't exist yet, this will create it with a (single, root)
prov_tree = self.reportclient.get_provider_tree_and_ensure_root(
context, compute_node.uuid, name=compute_node.hypervisor_hostname)
allocs = None
try:
try:
self.driver.update_provider_tree(prov_tree, nodename)
except exception.ReshapeNeeded:
if not startup:
# it up; the compute manager will treat it specially.
raise
LOG.info("Performing resource provider inventory and "
"allocation data migration during compute service "
"startup or fast-forward upgrade.")
allocs = self.reportclient.get_allocations_for_provider_tree(
context, nodename)
self.driver.update_provider_tree(prov_tree, nodename,
allocations=allocs)
# Inject driver capabilities traits into the provider
# tree. We need to determine the traits that the virt
# driver owns - so those that come from the tree itself
# (via the virt driver) plus the compute capabilities
# traits, and then merge those with the traits set
# externally that the driver does not own - and remove any
# set on the provider externally that the virt owns but
# aren't in the current list of supported traits. For
# trait at t1 and then at t2 it's not, so we need to
# was set externally on the provider.
traits = self._get_traits(nodename, provider_tree=prov_tree)
prov_tree.update_traits(nodename, traits)
except NotImplementedError:
# update_provider_tree isn't implemented yet - try get_inventory
try:
inv_data = self.driver.get_inventory(nodename)
_normalize_inventory_from_cn_obj(inv_data, compute_node)
except NotImplementedError:
# to remove this code branch
inv_data = compute_utils.compute_node_to_inventory_dict(
compute_node)
prov_tree.update_inventory(nodename, inv_data)
# Flush any changes. If we processed ReshapeNeeded above, allocs is not
# None, and this will hit placement's POST /reshaper route.
self.reportclient.update_from_provider_tree(context, prov_tree,
allocations=allocs)
def _update(self, context, compute_node, startup=False):
if self._resource_change(compute_node):
# NOTE(jianghuaw): Once we completely move to use get_inventory()
# for all resource provider's inv data. We can remove this check.
compute_node.save()
self._update_to_placement(context, compute_node, startup)
if self.pci_tracker:
self.pci_tracker.save(context)
def _update_usage(self, usage, nodename, sign=1):
mem_usage = usage['memory_mb']
disk_usage = usage.get('root_gb', 0)
vcpus_usage = usage.get('vcpus', 0)
overhead = self.driver.estimate_instance_overhead(usage)
mem_usage += overhead['memory_mb']
disk_usage += overhead.get('disk_gb', 0)
vcpus_usage += overhead.get('vcpus', 0)
cn = self.compute_nodes[nodename]
cn.memory_mb_used += sign * mem_usage
cn.local_gb_used += sign * disk_usage
cn.local_gb_used += sign * usage.get('ephemeral_gb', 0)
cn.local_gb_used += sign * usage.get('swap', 0) / 1024
cn.vcpus_used += sign * vcpus_usage
cn.free_ram_mb = cn.memory_mb - cn.memory_mb_used
cn.free_disk_gb = cn.local_gb - cn.local_gb_used
stats = self.stats[nodename]
cn.running_vms = stats.num_instances
free = sign == -1
updated_numa_topology = hardware.get_host_numa_usage_from_instance(
cn, usage, free)
cn.numa_topology = updated_numa_topology
def _get_migration_context_resource(self, resource, instance,
prefix='new_'):
migration_context = instance.migration_context
resource = prefix + resource
if migration_context and resource in migration_context:
return getattr(migration_context, resource)
return None
def _update_usage_from_migration(self, context, instance, migration,
nodename):
if not _is_trackable_migration(migration):
return
uuid = migration.instance_uuid
LOG.info("Updating resource usage from migration %s", migration.uuid,
instance_uuid=uuid)
incoming = (migration.dest_compute == self.host and
migration.dest_node == nodename)
outbound = (migration.source_compute == self.host and
migration.source_node == nodename)
same_node = (incoming and outbound)
tracked = uuid in self.tracked_instances
itype = None
numa_topology = None
sign = 0
if same_node:
if (instance['instance_type_id'] ==
migration.old_instance_type_id):
itype = self._get_instance_type(instance, 'new_', migration)
numa_topology = self._get_migration_context_resource(
'numa_topology', instance)
sign = 1
else:
itype = self._get_instance_type(instance, 'old_', migration)
numa_topology = self._get_migration_context_resource(
'numa_topology', instance, prefix='old_')
elif incoming and not tracked:
itype = self._get_instance_type(instance, 'new_', migration)
numa_topology = self._get_migration_context_resource(
'numa_topology', instance)
sign = 1
LOG.debug('Starting to track incoming migration %s with flavor %s',
migration.uuid, itype.flavorid, instance=instance)
elif outbound and not tracked:
itype = self._get_instance_type(instance, 'old_', migration)
numa_topology = self._get_migration_context_resource(
'numa_topology', instance, prefix='old_')
LOG.debug('Starting to track outgoing migration %s with flavor %s',
migration.uuid, itype.flavorid, instance=instance)
if itype:
cn = self.compute_nodes[nodename]
usage = self._get_usage_dict(
itype, instance, numa_topology=numa_topology)
if self.pci_tracker and sign:
self.pci_tracker.update_pci_for_instance(
context, instance, sign=sign)
self._update_usage(usage, nodename)
if self.pci_tracker:
obj = self.pci_tracker.stats.to_device_pools_obj()
cn.pci_device_pools = obj
else:
obj = objects.PciDevicePoolList()
cn.pci_device_pools = obj
self.tracked_migrations[uuid] = migration
def _update_usage_from_migrations(self, context, migrations, nodename):
filtered = {}
instances = {}
self.tracked_migrations.clear()
for migration in migrations:
uuid = migration.instance_uuid
try:
if uuid not in instances:
instances[uuid] = migration.instance
except exception.InstanceNotFound as e:
LOG.debug('Migration instance not found: %s', e)
continue
if not _instance_in_resize_state(instances[uuid]):
LOG.warning("Instance not resizing, skipping migration.",
instance_uuid=uuid)
continue
# filter to most recently updated migration for each instance:
other_migration = filtered.get(uuid, None)
# NOTE(claudiub): In Python 3, you cannot compare NoneTypes.
if other_migration:
om = other_migration
other_time = om.updated_at or om.created_at
migration_time = migration.updated_at or migration.created_at
if migration_time > other_time:
filtered[uuid] = migration
else:
filtered[uuid] = migration
for migration in filtered.values():
instance = instances[migration.instance_uuid]
# Skip migration (and mark it as error) if it doesn't match the
if (instance.migration_context is not None and
instance.migration_context.migration_id != migration.id):
LOG.info("Current instance migration %(im)s doesn't match "
"migration %(m)s, marking migration as error. "
"This can occur if a previous migration for this "
"instance did not complete.",
{'im': instance.migration_context.migration_id,
'm': migration.id})
migration.status = "error"
migration.save()
continue
try:
self._update_usage_from_migration(context, instance, migration,
nodename)
except exception.FlavorNotFound:
LOG.warning("Flavor could not be found, skipping migration.",
instance_uuid=instance.uuid)
continue
def _update_usage_from_instance(self, context, instance, nodename,
is_removed=False):
uuid = instance['uuid']
is_new_instance = uuid not in self.tracked_instances
# NOTE(sfinucan): Both brand new instances as well as instances that
# are being unshelved will have is_new_instance == True
is_removed_instance = not is_new_instance and (is_removed or
instance['vm_state'] in vm_states.ALLOW_RESOURCE_REMOVAL)
if is_new_instance:
self.tracked_instances.add(uuid)
sign = 1
if is_removed_instance:
self.tracked_instances.remove(uuid)
sign = -1
cn = self.compute_nodes[nodename]
stats = self.stats[nodename]
stats.update_stats_for_instance(instance, is_removed_instance)
cn.stats = stats
# if it's a new or deleted instance:
if is_new_instance or is_removed_instance:
if self.pci_tracker:
self.pci_tracker.update_pci_for_instance(context,
instance,
sign=sign)
self._update_usage(self._get_usage_dict(instance, instance),
nodename, sign=sign)
if is_removed_instance and uuid in self.is_bfv:
del self.is_bfv[uuid]
cn.current_workload = stats.calculate_workload()
if self.pci_tracker:
obj = self.pci_tracker.stats.to_device_pools_obj()
cn.pci_device_pools = obj
else:
cn.pci_device_pools = objects.PciDevicePoolList()
def _update_usage_from_instances(self, context, instances, nodename):
self.tracked_instances.clear()
cn = self.compute_nodes[nodename]
cn.local_gb_used = CONF.reserved_host_disk_mb / 1024
cn.memory_mb_used = CONF.reserved_host_memory_mb
cn.vcpus_used = CONF.reserved_host_cpus
cn.free_ram_mb = (cn.memory_mb - cn.memory_mb_used)
cn.free_disk_gb = (cn.local_gb - cn.local_gb_used)
cn.current_workload = 0
cn.running_vms = 0
instance_by_uuid = {}
for instance in instances:
if instance.vm_state not in vm_states.ALLOW_RESOURCE_REMOVAL:
self._update_usage_from_instance(context, instance, nodename)
instance_by_uuid[instance.uuid] = instance
return instance_by_uuid
def _remove_deleted_instances_allocations(self, context, cn,
migrations, instance_by_uuid):
migration_uuids = [migration.uuid for migration in migrations
if 'uuid' in migration]
# all the corner cases in move, local delete, unshelve and rebuild
# operations for when allocations should be deleted when things didn't
try:
pai = self.reportclient.get_allocations_for_resource_provider(
context, cn.uuid)
except (exception.ResourceProviderAllocationRetrievalFailed,
ks_exc.ClientException) as e:
LOG.error("Skipping removal of allocations for deleted instances: "
"%s", e)
return
allocations = pai.allocations
if not allocations:
return
read_deleted_context = context.elevated(read_deleted='yes')
for consumer_uuid, alloc in allocations.items():
if consumer_uuid in self.tracked_instances:
LOG.debug("Instance %s actively managed on this compute host "
"and has allocations in placement: %s.",
consumer_uuid, alloc)
continue
if consumer_uuid in migration_uuids:
LOG.debug("Migration %s is active on this compute host "
"and has allocations in placement: %s.",
consumer_uuid, alloc)
continue
instance_uuid = consumer_uuid
instance = instance_by_uuid.get(instance_uuid)
if not instance:
try:
instance = objects.Instance.get_by_uuid(
read_deleted_context, consumer_uuid,
expected_attrs=[])
except exception.InstanceNotFound:
# scheduler _just_ created an allocation for it and we're
LOG.info("Instance %(uuid)s has allocations against this "
"compute host but is not found in the database.",
{'uuid': instance_uuid},
exc_info=False)
continue
if instance.deleted:
LOG.debug("Instance %s has been deleted (perhaps locally). "
"Deleting allocations that remained for this "
"instance against this compute host: %s.",
instance_uuid, alloc)
self.reportclient.delete_allocation_for_instance(context,
instance_uuid)
continue
if not instance.host:
LOG.debug("Instance %s has been scheduled to this compute "
"host, the scheduler has made an allocation "
"against this compute node but the instance has "
"yet to start. Skipping heal of allocation: %s.",
instance_uuid, alloc)
continue
if (instance.host == cn.host and
instance.node == cn.hypervisor_hostname):
LOG.warning("Instance %s is not being actively managed by "
"this compute host but has allocations "
"referencing this compute host: %s. Skipping "
"heal of allocation because we do not know "
"what to do.", instance_uuid, alloc)
continue
if instance.host != cn.host:
# for resize to confirm/revert), however if the destination
# host is an Ocata compute host, it will delete the allocation
# that contains this source compute host information anyway and
# recreate an allocation that only refers to itself. So we
# don't need to do anything in that case. Just log the
# change the allocation.
LOG.warning("Instance %s has been moved to another host "
"%s(%s). There are allocations remaining against "
"the source host that might need to be removed: "
"%s.",
instance_uuid, instance.host, instance.node, alloc)
def delete_allocation_for_evacuated_instance(self, context, instance, node,
node_type='source'):
# Clean up the instance allocation from this node in placement
cn_uuid = self.compute_nodes[node].uuid
if not self.reportclient.remove_provider_tree_from_instance_allocation(
context, instance.uuid, cn_uuid):
LOG.error("Failed to clean allocation of evacuated "
"instance on the %s node %s",
node_type, cn_uuid, instance=instance)
def _find_orphaned_instances(self):
uuids1 = frozenset(self.tracked_instances)
uuids2 = frozenset(self.tracked_migrations.keys())
uuids = uuids1 | uuids2
usage = self.driver.get_per_instance_usage()
vuuids = frozenset(usage.keys())
orphan_uuids = vuuids - uuids
orphans = [usage[uuid] for uuid in orphan_uuids]
return orphans
def _update_usage_from_orphans(self, orphans, nodename):
for orphan in orphans:
memory_mb = orphan['memory_mb']
LOG.warning("Detected running orphan instance: %(uuid)s "
"(consuming %(memory_mb)s MB memory)",
{'uuid': orphan['uuid'], 'memory_mb': memory_mb})
# just record memory usage for the orphan
usage = {'memory_mb': memory_mb}
self._update_usage(usage, nodename)
def delete_allocation_for_shelve_offloaded_instance(self, context,
instance):
self.reportclient.delete_allocation_for_instance(context,
instance.uuid)
def _verify_resources(self, resources):
resource_keys = ["vcpus", "memory_mb", "local_gb", "cpu_info",
"vcpus_used", "memory_mb_used", "local_gb_used",
"numa_topology"]
missing_keys = [k for k in resource_keys if k not in resources]
if missing_keys:
reason = _("Missing keys: %s") % missing_keys
raise exception.InvalidInput(reason=reason)
def _get_instance_type(self, instance, prefix, migration):
stashed_flavors = migration.migration_type in ('resize',)
if stashed_flavors:
return getattr(instance, '%sflavor' % prefix)
else:
# NOTE(ndipanov): Certain migration types (all but resize)
# do not change flavors so there is no need to stash
# them. In that case - just get the instance flavor.
return instance.flavor
def _get_usage_dict(self, object_or_dict, instance, **updates):
def _is_bfv():
# Check to see if we have the is_bfv value cached.
if instance.uuid in self.is_bfv:
is_bfv = self.is_bfv[instance.uuid]
else:
is_bfv = compute_utils.is_volume_backed_instance(
instance._context, instance)
self.is_bfv[instance.uuid] = is_bfv
return is_bfv
usage = {}
if isinstance(object_or_dict, objects.Instance):
is_bfv = _is_bfv()
usage = {'memory_mb': object_or_dict.flavor.memory_mb,
'swap': object_or_dict.flavor.swap,
'vcpus': object_or_dict.flavor.vcpus,
'root_gb': (0 if is_bfv else
object_or_dict.flavor.root_gb),
'ephemeral_gb': object_or_dict.flavor.ephemeral_gb,
'numa_topology': object_or_dict.numa_topology}
elif isinstance(object_or_dict, objects.Flavor):
usage = obj_base.obj_to_primitive(object_or_dict)
if _is_bfv():
usage['root_gb'] = 0
else:
usage.update(object_or_dict)
for key in ('numa_topology',):
if key in updates:
usage[key] = updates[key]
return usage
def build_failed(self, nodename):
self.stats[nodename].build_failed()
def build_succeeded(self, nodename):
self.stats[nodename].build_succeeded()
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def claim_pci_devices(self, context, pci_requests):
result = self.pci_tracker.claim_instance(
context, pci_requests, None)
self.pci_tracker.save(context)
return result
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def allocate_pci_devices_for_instance(self, context, instance):
self.pci_tracker.allocate_instance(instance)
self.pci_tracker.save(context)
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def free_pci_device_allocations_for_instance(self, context, instance):
self.pci_tracker.free_instance_allocations(context, instance)
self.pci_tracker.save(context)
@utils.synchronized(COMPUTE_RESOURCE_SEMAPHORE)
def free_pci_device_claims_for_instance(self, context, instance):
self.pci_tracker.free_instance_claims(context, instance)
self.pci_tracker.save(context)
| true | true |
f7258d9ad8ec1ffdd9a1d4a476dabdc315bbf560 | 5,448 | py | Python | synthesizer/models/custom_decoder.py | Khizar-Ali/Lip2Wav | 07f056b3468ca660823830680bf25bdd42034f9e | [
"MIT"
] | 541 | 2020-05-14T05:56:31.000Z | 2022-03-30T03:34:55.000Z | synthesizer/models/custom_decoder.py | Khizar-Ali/Lip2Wav | 07f056b3468ca660823830680bf25bdd42034f9e | [
"MIT"
] | 36 | 2020-05-14T06:00:31.000Z | 2022-03-10T06:13:44.000Z | synthesizer/models/custom_decoder.py | Khizar-Ali/Lip2Wav | 07f056b3468ca660823830680bf25bdd42034f9e | [
"MIT"
] | 123 | 2020-05-19T02:43:47.000Z | 2022-03-26T11:28:13.000Z | from __future__ import absolute_import, division, print_function
import collections
import tensorflow as tf
from synthesizer.models.helpers import TacoTestHelper, TacoTrainingHelper
from tensorflow.contrib.seq2seq.python.ops import decoder
from tensorflow.contrib.seq2seq.python.ops import helper as helper_py
from tensorflow.python.framework import ops, tensor_shape
from tensorflow.python.layers import base as layers_base
from tensorflow.python.ops import rnn_cell_impl
from tensorflow.python.util import nest
class CustomDecoderOutput(
#collections.namedtuple("CustomDecoderOutput", ("rnn_output", "token_output", "sample_id"))):
collections.namedtuple("CustomDecoderOutput", ("rnn_output", "sample_id"))):
pass
class CustomDecoder(decoder.Decoder):
"""Custom sampling decoder.
Allows for stop token prediction at inference time
and returns equivalent loss in training time.
Note:
Only use this decoder with Tacotron 2 as it only accepts tacotron custom helpers
"""
def __init__(self, cell, helper, initial_state, output_layer=None):
"""Initialize CustomDecoder.
Args:
cell: An `RNNCell` instance.
helper: A `Helper` instance.
initial_state: A (possibly nested tuple of...) tensors and TensorArrays.
The initial state of the RNNCell.
output_layer: (Optional) An instance of `tf.layers.Layer`, i.e.,
`tf.layers.Dense`. Optional layer to apply to the RNN output prior
to storing the result or sampling.
Raises:
TypeError: if `cell`, `helper` or `output_layer` have an incorrect type.
"""
rnn_cell_impl.assert_like_rnncell(type(cell), cell)
if not isinstance(helper, helper_py.Helper):
raise TypeError("helper must be a Helper, received: %s" % type(helper))
if (output_layer is not None
and not isinstance(output_layer, layers_base.Layer)):
raise TypeError(
"output_layer must be a Layer, received: %s" % type(output_layer))
self._cell = cell
self._helper = helper
self._initial_state = initial_state
self._output_layer = output_layer
@property
def batch_size(self):
return self._helper.batch_size
def _rnn_output_size(self):
size = self._cell.output_size
if self._output_layer is None:
return size
else:
# To use layer"s compute_output_shape, we need to convert the
# RNNCell"s output_size entries into shapes with an unknown
# batch size. We then pass this through the layer"s
# compute_output_shape and read off all but the first (batch)
# dimensions to get the output size of the rnn with the layer
# applied to the top.
output_shape_with_unknown_batch = nest.map_structure(
lambda s: tensor_shape.TensorShape([None]).concatenate(s),
size)
layer_output_shape = self._output_layer._compute_output_shape( # pylint: disable=protected-access
output_shape_with_unknown_batch)
return nest.map_structure(lambda s: s[1:], layer_output_shape)
@property
def output_size(self):
# Return the cell output and the id
#return CustomDecoderOutput(
# rnn_output=self._rnn_output_size(),
# token_output=self._helper.token_output_size,
# sample_id=self._helper.sample_ids_shape)
return CustomDecoderOutput(
rnn_output=self._rnn_output_size(),
sample_id=self._helper.sample_ids_shape)
@property
def output_dtype(self):
# Assume the dtype of the cell is the output_size structure
# containing the input_state"s first component's dtype.
# Return that structure and the sample_ids_dtype from the helper.
dtype = nest.flatten(self._initial_state)[0].dtype
#return CustomDecoderOutput(
# nest.map_structure(lambda _: dtype, self._rnn_output_size()),
# tf.float32,
# self._helper.sample_ids_dtype)
return CustomDecoderOutput(
nest.map_structure(lambda _: dtype, self._rnn_output_size()),
self._helper.sample_ids_dtype)
def initialize(self, name=None):
"""Initialize the decoder.
Args:
name: Name scope for any created operations.
Returns:
`(finished, first_inputs, initial_state)`.
"""
return self._helper.initialize() + (self._initial_state,)
def step(self, time, inputs, state, name=None):
"""Perform a custom decoding step.
Enables for dyanmic <stop_token> prediction
Args:
time: scalar `int32` tensor.
inputs: A (structure of) input tensors.
state: A (structure of) state tensors and TensorArrays.
name: Name scope for any created operations.
Returns:
`(outputs, next_state, next_inputs, finished)`.
"""
with ops.name_scope(name, "CustomDecoderStep", (time, inputs, state)):
#Call outputprojection wrapper cell
#(cell_outputs, stop_token), cell_state = self._cell(inputs, state)
(cell_outputs), cell_state = self._cell(inputs, state)
#apply output_layer (if existant)
if self._output_layer is not None:
cell_outputs = self._output_layer(cell_outputs)
sample_ids = self._helper.sample(
time=time, outputs=cell_outputs, state=cell_state)
#(finished, next_inputs, next_state) = self._helper.next_inputs(
# time=time,
# outputs=cell_outputs,
# state=cell_state,
# sample_ids=sample_ids,
# stop_token_prediction=stop_token)
(finished, next_inputs, next_state) = self._helper.next_inputs(
time=time,
outputs=cell_outputs,
state=cell_state,
sample_ids=sample_ids)
#outputs = CustomDecoderOutput(cell_outputs, stop_token, sample_ids)
outputs = CustomDecoderOutput(cell_outputs, sample_ids)
return (outputs, next_state, next_inputs, finished)
| 36.810811 | 101 | 0.752019 | from __future__ import absolute_import, division, print_function
import collections
import tensorflow as tf
from synthesizer.models.helpers import TacoTestHelper, TacoTrainingHelper
from tensorflow.contrib.seq2seq.python.ops import decoder
from tensorflow.contrib.seq2seq.python.ops import helper as helper_py
from tensorflow.python.framework import ops, tensor_shape
from tensorflow.python.layers import base as layers_base
from tensorflow.python.ops import rnn_cell_impl
from tensorflow.python.util import nest
class CustomDecoderOutput(
collections.namedtuple("CustomDecoderOutput", ("rnn_output", "sample_id"))):
pass
class CustomDecoder(decoder.Decoder):
def __init__(self, cell, helper, initial_state, output_layer=None):
rnn_cell_impl.assert_like_rnncell(type(cell), cell)
if not isinstance(helper, helper_py.Helper):
raise TypeError("helper must be a Helper, received: %s" % type(helper))
if (output_layer is not None
and not isinstance(output_layer, layers_base.Layer)):
raise TypeError(
"output_layer must be a Layer, received: %s" % type(output_layer))
self._cell = cell
self._helper = helper
self._initial_state = initial_state
self._output_layer = output_layer
@property
def batch_size(self):
return self._helper.batch_size
def _rnn_output_size(self):
size = self._cell.output_size
if self._output_layer is None:
return size
else:
# RNNCell"s output_size entries into shapes with an unknown
# compute_output_shape and read off all but the first (batch)
# dimensions to get the output size of the rnn with the layer
# applied to the top.
output_shape_with_unknown_batch = nest.map_structure(
lambda s: tensor_shape.TensorShape([None]).concatenate(s),
size)
layer_output_shape = self._output_layer._compute_output_shape( # pylint: disable=protected-access
output_shape_with_unknown_batch)
return nest.map_structure(lambda s: s[1:], layer_output_shape)
@property
def output_size(self):
# Return the cell output and the id
#return CustomDecoderOutput(
# rnn_output=self._rnn_output_size(),
# token_output=self._helper.token_output_size,
# sample_id=self._helper.sample_ids_shape)
return CustomDecoderOutput(
rnn_output=self._rnn_output_size(),
sample_id=self._helper.sample_ids_shape)
@property
def output_dtype(self):
# Assume the dtype of the cell is the output_size structure
# containing the input_state"s first component's dtype.
# Return that structure and the sample_ids_dtype from the helper.
dtype = nest.flatten(self._initial_state)[0].dtype
#return CustomDecoderOutput(
# nest.map_structure(lambda _: dtype, self._rnn_output_size()),
# tf.float32,
# self._helper.sample_ids_dtype)
return CustomDecoderOutput(
nest.map_structure(lambda _: dtype, self._rnn_output_size()),
self._helper.sample_ids_dtype)
def initialize(self, name=None):
return self._helper.initialize() + (self._initial_state,)
def step(self, time, inputs, state, name=None):
with ops.name_scope(name, "CustomDecoderStep", (time, inputs, state)):
#Call outputprojection wrapper cell
#(cell_outputs, stop_token), cell_state = self._cell(inputs, state)
(cell_outputs), cell_state = self._cell(inputs, state)
#apply output_layer (if existant)
if self._output_layer is not None:
cell_outputs = self._output_layer(cell_outputs)
sample_ids = self._helper.sample(
time=time, outputs=cell_outputs, state=cell_state)
#(finished, next_inputs, next_state) = self._helper.next_inputs(
# time=time,
# outputs=cell_outputs,
# state=cell_state,
# sample_ids=sample_ids,
# stop_token_prediction=stop_token)
(finished, next_inputs, next_state) = self._helper.next_inputs(
time=time,
outputs=cell_outputs,
state=cell_state,
sample_ids=sample_ids)
#outputs = CustomDecoderOutput(cell_outputs, stop_token, sample_ids)
outputs = CustomDecoderOutput(cell_outputs, sample_ids)
return (outputs, next_state, next_inputs, finished)
| true | true |
f7258fa1fd9ffacbfd6a1a0cc0f2cd988adbdb32 | 3,381 | py | Python | Server_Code/database.py | PUT-PTM/2019_SmartAttendance | cab58f3f355c07d3dfd4c73c8adb4c7bbf6d676c | [
"MIT"
] | 1 | 2019-03-13T16:00:32.000Z | 2019-03-13T16:00:32.000Z | Server_Code/database.py | PUT-PTM/2019_SmartAttendance | cab58f3f355c07d3dfd4c73c8adb4c7bbf6d676c | [
"MIT"
] | null | null | null | Server_Code/database.py | PUT-PTM/2019_SmartAttendance | cab58f3f355c07d3dfd4c73c8adb4c7bbf6d676c | [
"MIT"
] | 1 | 2021-07-10T08:27:21.000Z | 2021-07-10T08:27:21.000Z | from datetime import datetime
import json
from pathlib import Path
import pymssql
config_json: dict = json.loads(Path('config.json').read_text())
# Connecting to database
def connect():
global config_json
# Connect to Microsoft SQL server
conn = pymssql.connect(
server=config_json['server'],
user=config_json['user'],
password=config_json['password'],
database=config_json['database']
)
return conn
def student_exists(sid: int) -> bool:
conn = connect()
cursor = conn.cursor()
# Correct request
cursor.execute('select COUNT(1) from StudentInfo where SID=' + str(sid) + ';')
result = cursor.fetchone()
conn.close()
return str(result[0]) == '1'
def student_info_get() -> dict:
conn = connect()
cursor = conn.cursor()
# Get all students from database
cursor.execute('select * from StudentInfo order by SID;')
# Convert table into json
data: dict = json.loads('{"elements":[]}')
row = cursor.fetchone()
while row:
# Creating json table with data
data['elements'].append(
# Creating json table with data
json.loads(
'{"SID":' + str(row[0]) + ',' +
'"fName": "' + row[1] + '",' +
'"lName": "' + row[2] + '"}'
)
)
row = cursor.fetchone()
# While end
conn.close()
return data
def student_info_insert(info: dict) -> None:
# Get values from JSON
sid = str(info['SID'])
f_name = '\'' + info['fName'] + '\''
l_name = '\'' + info['lName'] + '\''
# Add entry to database
sql_req = 'insert into StudentInfo values (' + sid + ',' + f_name + ',' + l_name + ');'
conn = connect()
cursor = conn.cursor()
cursor.execute(sql_req)
conn.commit()
conn.close()
print('Finished sql req!')
def student_info_delete(sid: int) -> None:
conn = connect()
cursor = conn.cursor()
cursor.execute('delete from StudentInfo where SID=' + str(sid) + ';')
conn.commit()
conn.close()
def presence_get() -> dict:
conn = connect()
cursor = conn.cursor()
cursor.execute('select * from Presence order by Date;')
# Convert table into json
data = json.loads('{"elements":[]}')
row = cursor.fetchone()
while row:
# Creating json table with data
data['elements'].append(
# Creating json table with data
json.loads(
'{"SID":' + str(row[0]) + ',' +
'"Date": "' + row[1].strftime('%Y-%m-%d %H:%M:%S') + '",' +
'"CID": "' + str(row[2]) + '",' +
'"Room": "' + row[3] + '"}'
)
)
row = cursor.fetchone()
# While end
conn.close()
return data
def presence_insert(info: dict):
# Get values from JSONs
sid = str(info.get('SID'))
date = '\'' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '\''
cid = info.get('CID')
room = '\'' + info.get('Room') + '\''
# Request building
sql_req = 'insert into Presence values (' + sid + ',' + date
if cid is not None:
sql_req += ',' + str(cid)
if len(room) > 2:
sql_req += ',' + room
sql_req += ');'
conn = connect()
cursor = conn.cursor()
cursor.execute(sql_req)
conn.commit()
conn.close()
print('Finished sql req!')
| 25.613636 | 91 | 0.540964 | from datetime import datetime
import json
from pathlib import Path
import pymssql
config_json: dict = json.loads(Path('config.json').read_text())
def connect():
global config_json
conn = pymssql.connect(
server=config_json['server'],
user=config_json['user'],
password=config_json['password'],
database=config_json['database']
)
return conn
def student_exists(sid: int) -> bool:
conn = connect()
cursor = conn.cursor()
cursor.execute('select COUNT(1) from StudentInfo where SID=' + str(sid) + ';')
result = cursor.fetchone()
conn.close()
return str(result[0]) == '1'
def student_info_get() -> dict:
conn = connect()
cursor = conn.cursor()
cursor.execute('select * from StudentInfo order by SID;')
data: dict = json.loads('{"elements":[]}')
row = cursor.fetchone()
while row:
data['elements'].append(
json.loads(
'{"SID":' + str(row[0]) + ',' +
'"fName": "' + row[1] + '",' +
'"lName": "' + row[2] + '"}'
)
)
row = cursor.fetchone()
conn.close()
return data
def student_info_insert(info: dict) -> None:
sid = str(info['SID'])
f_name = '\'' + info['fName'] + '\''
l_name = '\'' + info['lName'] + '\''
sql_req = 'insert into StudentInfo values (' + sid + ',' + f_name + ',' + l_name + ');'
conn = connect()
cursor = conn.cursor()
cursor.execute(sql_req)
conn.commit()
conn.close()
print('Finished sql req!')
def student_info_delete(sid: int) -> None:
conn = connect()
cursor = conn.cursor()
cursor.execute('delete from StudentInfo where SID=' + str(sid) + ';')
conn.commit()
conn.close()
def presence_get() -> dict:
conn = connect()
cursor = conn.cursor()
cursor.execute('select * from Presence order by Date;')
data = json.loads('{"elements":[]}')
row = cursor.fetchone()
while row:
data['elements'].append(
json.loads(
'{"SID":' + str(row[0]) + ',' +
'"Date": "' + row[1].strftime('%Y-%m-%d %H:%M:%S') + '",' +
'"CID": "' + str(row[2]) + '",' +
'"Room": "' + row[3] + '"}'
)
)
row = cursor.fetchone()
conn.close()
return data
def presence_insert(info: dict):
sid = str(info.get('SID'))
date = '\'' + datetime.now().strftime('%Y-%m-%d %H:%M:%S') + '\''
cid = info.get('CID')
room = '\'' + info.get('Room') + '\''
sql_req = 'insert into Presence values (' + sid + ',' + date
if cid is not None:
sql_req += ',' + str(cid)
if len(room) > 2:
sql_req += ',' + room
sql_req += ');'
conn = connect()
cursor = conn.cursor()
cursor.execute(sql_req)
conn.commit()
conn.close()
print('Finished sql req!')
| true | true |
f725901ae4990c796dfc0c6fd9dcacb4bed91c78 | 3,385 | py | Python | examples/basic_operations/update_ad_group.py | JakobSteixner/google-ads-python | df2b802cc7e78295a4ece21cc7ef3787cd35dab0 | [
"Apache-2.0"
] | null | null | null | examples/basic_operations/update_ad_group.py | JakobSteixner/google-ads-python | df2b802cc7e78295a4ece21cc7ef3787cd35dab0 | [
"Apache-2.0"
] | null | null | null | examples/basic_operations/update_ad_group.py | JakobSteixner/google-ads-python | df2b802cc7e78295a4ece21cc7ef3787cd35dab0 | [
"Apache-2.0"
] | null | null | null | #!/usr/bin/env python
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This example updates an ad group.
To get ad groups, run get_ad_groups.py.
"""
import argparse
import sys
from google.ads.googleads.client import GoogleAdsClient
from google.ads.googleads.errors import GoogleAdsException
from google.api_core import protobuf_helpers
# [START update_ad_group]
def main(client, customer_id, ad_group_id, cpc_bid_micro_amount):
ad_group_service = client.get_service("AdGroupService")
# Create ad group operation.
ad_group_operation = client.get_type("AdGroupOperation")
ad_group = ad_group_operation.update
ad_group.resource_name = ad_group_service.ad_group_path(
customer_id, ad_group_id
)
ad_group.status = client.enums.AdGroupStatusEnum.PAUSED
ad_group.cpc_bid_micros = cpc_bid_micro_amount
client.copy_from(
ad_group_operation.update_mask,
protobuf_helpers.field_mask(None, ad_group._pb),
)
# Update the ad group.
ad_group_response = ad_group_service.mutate_ad_groups(
customer_id=customer_id, operations=[ad_group_operation]
)
print(f"Updated ad group {ad_group_response.results[0].resource_name}.")
# [END update_ad_group]
if __name__ == "__main__":
# GoogleAdsClient will read the google-ads.yaml configuration file in the
# home directory if none is specified.
googleads_client = GoogleAdsClient.load_from_storage(version="v10")
parser = argparse.ArgumentParser(
description=(
"Updates an ad group for specified customer and campaign "
"id with the given bid micro amount."
)
)
# The following argument(s) should be provided to run the example.
parser.add_argument(
"-c",
"--customer_id",
type=str,
required=True,
help="The Google Ads customer ID.",
)
parser.add_argument(
"-a", "--ad_group_id", type=str, required=True, help="The ad group ID."
)
parser.add_argument(
"-b",
"--cpc_bid_micro_amount",
type=int,
required=True,
help="The cpc bid micro amount.",
)
args = parser.parse_args()
try:
main(
googleads_client,
args.customer_id,
args.ad_group_id,
args.cpc_bid_micro_amount,
)
except GoogleAdsException as ex:
print(
f'Request with ID "{ex.request_id}" failed with status '
f'"{ex.error.code().name}" and includes the following errors:'
)
for error in ex.failure.errors:
print(f'\tError with message "{error.message}".')
if error.location:
for field_path_element in error.location.field_path_elements:
print(f"\t\tOn field: {field_path_element.field_name}")
sys.exit(1)
| 32.548077 | 79 | 0.676809 |
import argparse
import sys
from google.ads.googleads.client import GoogleAdsClient
from google.ads.googleads.errors import GoogleAdsException
from google.api_core import protobuf_helpers
def main(client, customer_id, ad_group_id, cpc_bid_micro_amount):
ad_group_service = client.get_service("AdGroupService")
ad_group_operation = client.get_type("AdGroupOperation")
ad_group = ad_group_operation.update
ad_group.resource_name = ad_group_service.ad_group_path(
customer_id, ad_group_id
)
ad_group.status = client.enums.AdGroupStatusEnum.PAUSED
ad_group.cpc_bid_micros = cpc_bid_micro_amount
client.copy_from(
ad_group_operation.update_mask,
protobuf_helpers.field_mask(None, ad_group._pb),
)
ad_group_response = ad_group_service.mutate_ad_groups(
customer_id=customer_id, operations=[ad_group_operation]
)
print(f"Updated ad group {ad_group_response.results[0].resource_name}.")
if __name__ == "__main__":
googleads_client = GoogleAdsClient.load_from_storage(version="v10")
parser = argparse.ArgumentParser(
description=(
"Updates an ad group for specified customer and campaign "
"id with the given bid micro amount."
)
)
parser.add_argument(
"-c",
"--customer_id",
type=str,
required=True,
help="The Google Ads customer ID.",
)
parser.add_argument(
"-a", "--ad_group_id", type=str, required=True, help="The ad group ID."
)
parser.add_argument(
"-b",
"--cpc_bid_micro_amount",
type=int,
required=True,
help="The cpc bid micro amount.",
)
args = parser.parse_args()
try:
main(
googleads_client,
args.customer_id,
args.ad_group_id,
args.cpc_bid_micro_amount,
)
except GoogleAdsException as ex:
print(
f'Request with ID "{ex.request_id}" failed with status '
f'"{ex.error.code().name}" and includes the following errors:'
)
for error in ex.failure.errors:
print(f'\tError with message "{error.message}".')
if error.location:
for field_path_element in error.location.field_path_elements:
print(f"\t\tOn field: {field_path_element.field_name}")
sys.exit(1)
| true | true |
f72590d6e6483328b9562e5788fc07feb3ad4594 | 11,628 | py | Python | tests/suite.py | dannielarriola/uai-coursebuilder | fbd440a8bfe1a928ac52985aea2949d5e91ad203 | [
"Apache-2.0"
] | null | null | null | tests/suite.py | dannielarriola/uai-coursebuilder | fbd440a8bfe1a928ac52985aea2949d5e91ad203 | [
"Apache-2.0"
] | 27 | 2016-08-31T19:04:46.000Z | 2016-09-29T00:22:32.000Z | tests/suite.py | dannielarriola/uai-coursebuilder | fbd440a8bfe1a928ac52985aea2949d5e91ad203 | [
"Apache-2.0"
] | null | null | null | # Copyright 2013 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS-IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Course Builder test suite.
This script runs all functional and units test in the Course Builder project.
Here is how to use the script:
- if you don't have pip, install it using 'sudo apt-get install python-pip'
- install WebTest using: 'sudo pip install WebTest'
- make sure your PYTHONPATH contains: google_appengine,
google_appengine/lib/jinja2-2.6, google_appengine/lib/webapp2-2.5.1 and
the 'coursebuilder' directory itself
- invoke this test suite from the command line:
# Run test method baz in unittest.TestCase Bar found in tests/foo.py.
python tests/suite.py --test_class_name tests.foo.Bar.baz
- review the output to make sure there are no errors or warnings
Good luck!
"""
__author__ = 'Sean Lip'
import argparse
import cStringIO
import logging
import os
import shutil
import sys
import unittest
import task_queue
import webtest
import appengine_config
from tools.etl import etl
from google.appengine.api.search import simple_search_stub
from google.appengine.datastore import datastore_stub_util
from google.appengine.ext import testbed
_PARSER = argparse.ArgumentParser()
_PARSER.add_argument(
'--test_class_name',
help='optional dotted module name of the test(s) to run', type=str)
# Direct key access so we'll throw if os.environ is misconfigured.
TEST_DATA_BASE = os.path.join(
os.environ['COURSEBUILDER_RESOURCES'], 'test-data')
def empty_environ():
os.environ['AUTH_DOMAIN'] = 'example.com'
os.environ['SERVER_NAME'] = 'localhost'
os.environ['HTTP_HOST'] = 'localhost'
os.environ['SERVER_PORT'] = '8080'
os.environ['USER_EMAIL'] = ''
os.environ['USER_ID'] = ''
os.environ['DEFAULT_VERSION_HOSTNAME'] = (
os.environ['HTTP_HOST'] + ':' + os.environ['SERVER_PORT'])
def iterate_tests(test_suite_or_case):
"""Iterate through all of the test cases in 'test_suite_or_case'."""
try:
suite = iter(test_suite_or_case)
except TypeError:
yield test_suite_or_case
else:
for test in suite:
for subtest in iterate_tests(test):
yield subtest
class TestBase(unittest.TestCase):
"""Base class for all Course Builder tests."""
INTEGRATION_SERVER_BASE_URL = 'http://localhost:8081'
ADMIN_SERVER_BASE_URL = 'http://localhost:8000'
STOP_AFTER_FIRST_FAILURE = False
HAS_PENDING_FAILURE = False
# Log level for all tests in this test case. Override if you need to test
# against different logging levels. Be very careful when setting this to
# logging.DEBUG: downstream code is sometimes very chatty at logging.DEBUG,
# and can generate enough logging data that tests run out of memory.
LOG_LEVEL = logging.ERROR
def setUp(self):
if TestBase.STOP_AFTER_FIRST_FAILURE:
assert not TestBase.HAS_PENDING_FAILURE
super(TestBase, self).setUp()
self._set_up_logging()
# e.g. TEST_DATA_BASE/tests/functional/tests/MyTestCase.
self.test_tempdir = os.path.join(
TEST_DATA_BASE, self.__class__.__module__.replace('.', os.sep),
self.__class__.__name__)
self.reset_filesystem()
self._originals = {} # Map of object -> {symbol_string: original_value}
def _set_up_logging(self):
self._log = cStringIO.StringIO()
self._stream_handler = logging.StreamHandler(self._log)
self._stream_handler.setFormatter(
logging.Formatter(fmt='%(levelname)s: %(message)s'))
self._logger = logging.getLogger()
self._logger.addHandler(self._stream_handler)
self._logger.setLevel(self.LOG_LEVEL)
def tearDown(self):
self._unswap_all()
self.reset_filesystem(remove_only=True)
self._tear_down_logging()
super(TestBase, self).tearDown()
def _tear_down_logging(self):
self._logger.removeHandler(self._stream_handler)
self._log.close()
def get_log(self):
self._log.flush()
return self._log.getvalue()
def assertLogContains(self, message):
self.assertIn(message, self.get_log())
def reset_filesystem(self, remove_only=False):
if os.path.exists(self.test_tempdir):
shutil.rmtree(self.test_tempdir)
if not remove_only:
os.makedirs(self.test_tempdir)
def run(self, result=None):
if not result:
result = self.defaultTestResult()
super(TestBase, self).run(result)
if not result.wasSuccessful():
TestBase.HAS_PENDING_FAILURE = True
def shortDescription(self):
"""Additional information logged during unittest invocation."""
# Suppress default logging of docstrings. Instead log name/status only.
return None
def swap(self, source, symbol, new): # pylint: disable=invalid-name
"""Swaps out source.symbol for a new value.
Allows swapping of members and methods:
myobject.foo = 'original_foo'
self.swap(myobject, 'foo', 'bar')
self.assertEqual('bar', myobject.foo)
myobject.baz() # -> 'original_baz'
self.swap(myobject, 'baz', lambda: 'quux')
self.assertEqual('quux', myobject.bar())
Swaps are automatically undone in tearDown().
Args:
source: object. The source object to swap from.
symbol: string. The name of the symbol to swap.
new: object. The new value to swap in.
"""
if source not in self._originals:
self._originals[source] = {}
if not self._originals[source].get(symbol, None):
self._originals[source][symbol] = getattr(source, symbol)
setattr(source, symbol, new)
def _unswap_all(self):
for source, symbol_to_value in self._originals.iteritems():
for symbol, value in symbol_to_value.iteritems():
setattr(source, symbol, value)
class FunctionalTestBase(TestBase):
"""Base class for functional tests."""
class AppEngineTestBase(FunctionalTestBase):
"""Base class for tests that require App Engine services."""
def getApp(self):
"""Returns the main application to be tested."""
raise Exception('Not implemented.')
def setUp(self):
super(AppEngineTestBase, self).setUp()
empty_environ()
# setup an app to be tested
self.testapp = webtest.TestApp(self.getApp())
self.testbed = testbed.Testbed()
self.testbed.activate()
# configure datastore policy to emulate instantaneously and globally
# consistent HRD; we also patch dev_appserver in main.py to run under
# the same policy
policy = datastore_stub_util.PseudoRandomHRConsistencyPolicy(
probability=1)
# declare any relevant App Engine service stubs here
self.testbed.init_user_stub()
self.testbed.init_memcache_stub()
self.testbed.init_datastore_v3_stub(consistency_policy=policy)
self.testbed.init_taskqueue_stub(root_path=os.environ['SOURCE_DIR'])
self.taskq = self.testbed.get_stub(testbed.TASKQUEUE_SERVICE_NAME)
self.testbed.init_urlfetch_stub()
self.testbed.init_files_stub()
self.testbed.init_blobstore_stub()
self.testbed.init_mail_stub()
self.testbed.init_app_identity_stub()
# TODO(emichael): Fix this when an official stub is created
self.testbed._register_stub(
'search', simple_search_stub.SearchServiceStub())
self.task_dispatcher = task_queue.TaskQueueHandlerDispatcher(
self.testapp, self.taskq)
# Handle for testing sent mail.
self.mail_stub = self.testbed.get_stub(testbed.MAIL_SERVICE_NAME)
def tearDown(self):
self.testbed.deactivate()
super(AppEngineTestBase, self).tearDown()
def get_mail_stub(self):
return self.testbed.get_stub(testbed.MAIL_SERVICE_NAME)
def create_test_suite(parsed_args):
"""Loads all requested test suites.
By default, loads all unittest.TestCases found under the project root's
tests/ directory.
Args:
parsed_args: argparse.Namespace. Processed command-line arguments.
Returns:
unittest.TestSuite. The test suite populated with all tests to run.
"""
loader = unittest.TestLoader()
if not parsed_args.test_class_name:
raise Exception('Expected --test_class_name to be specified.')
return loader.loadTestsFromName(parsed_args.test_class_name)
def fix_sys_path():
"""Fix the sys.path to include GAE extra paths."""
import dev_appserver # pylint: disable=C6204
# dev_appserver.fix_sys_path() prepends GAE paths to sys.path and hides
# our classes like 'tests' behind other modules that have 'tests'.
# Here, unlike dev_appserver, we append the path instead of prepending it,
# so that our classes come first.
sys.path += dev_appserver.EXTRA_PATHS[:]
# This is to work around an issue with the __import__ builtin. The
# problem seems to be that if the sys.path list contains an item that
# partially matches a package name to import, __import__ will get
# confused, and report an error message (which removes the first path
# element from the module it's complaining about, which does not help
# efforts to diagnose the problem at all).
#
# The specific case where this causes an issue is between
# $COURSEBUILDER_HOME/tests/internal and $COURSEBUILDER_HOME/internal
# Since the former exists, __import__ will ignore the second, and so
# things like .../internal/experimental/autoregister/autoregister
# cannot be loaded.
#
# To address this issue, we ensure that COURSEBUILDER_HOME is on sys.path
# before anything else, and do it before appengine_config's module
# importation starts running. (And we have to do it here, because if we
# try to do this within appengine_config, AppEngine will throw an error)
if appengine_config.BUNDLE_ROOT in sys.path:
sys.path.remove(appengine_config.BUNDLE_ROOT)
sys.path.insert(0, appengine_config.BUNDLE_ROOT)
def main():
"""Starts in-process server and runs all test cases in this module."""
fix_sys_path()
etl._set_env_vars_from_app_yaml()
parsed_args = _PARSER.parse_args()
test_suite = create_test_suite(parsed_args)
result = unittest.TextTestRunner(verbosity=2).run(test_suite)
if result.errors or result.failures:
raise Exception(
'Test suite failed: %s errors, %s failures of '
' %s tests run.' % (
len(result.errors), len(result.failures), result.testsRun))
import tests.functional.actions as actions
count = len(actions.UNIQUE_URLS_FOUND.keys())
result.stream.writeln('INFO: Unique URLs found: %s' % count)
result.stream.writeln('INFO: All %s tests PASSED!' % result.testsRun)
if __name__ == '__main__':
appengine_config.gcb_force_default_encoding('ascii')
main()
| 36.566038 | 80 | 0.689456 |
__author__ = 'Sean Lip'
import argparse
import cStringIO
import logging
import os
import shutil
import sys
import unittest
import task_queue
import webtest
import appengine_config
from tools.etl import etl
from google.appengine.api.search import simple_search_stub
from google.appengine.datastore import datastore_stub_util
from google.appengine.ext import testbed
_PARSER = argparse.ArgumentParser()
_PARSER.add_argument(
'--test_class_name',
help='optional dotted module name of the test(s) to run', type=str)
TEST_DATA_BASE = os.path.join(
os.environ['COURSEBUILDER_RESOURCES'], 'test-data')
def empty_environ():
os.environ['AUTH_DOMAIN'] = 'example.com'
os.environ['SERVER_NAME'] = 'localhost'
os.environ['HTTP_HOST'] = 'localhost'
os.environ['SERVER_PORT'] = '8080'
os.environ['USER_EMAIL'] = ''
os.environ['USER_ID'] = ''
os.environ['DEFAULT_VERSION_HOSTNAME'] = (
os.environ['HTTP_HOST'] + ':' + os.environ['SERVER_PORT'])
def iterate_tests(test_suite_or_case):
try:
suite = iter(test_suite_or_case)
except TypeError:
yield test_suite_or_case
else:
for test in suite:
for subtest in iterate_tests(test):
yield subtest
class TestBase(unittest.TestCase):
INTEGRATION_SERVER_BASE_URL = 'http://localhost:8081'
ADMIN_SERVER_BASE_URL = 'http://localhost:8000'
STOP_AFTER_FIRST_FAILURE = False
HAS_PENDING_FAILURE = False
# Log level for all tests in this test case. Override if you need to test
# against different logging levels. Be very careful when setting this to
# logging.DEBUG: downstream code is sometimes very chatty at logging.DEBUG,
# and can generate enough logging data that tests run out of memory.
LOG_LEVEL = logging.ERROR
def setUp(self):
if TestBase.STOP_AFTER_FIRST_FAILURE:
assert not TestBase.HAS_PENDING_FAILURE
super(TestBase, self).setUp()
self._set_up_logging()
# e.g. TEST_DATA_BASE/tests/functional/tests/MyTestCase.
self.test_tempdir = os.path.join(
TEST_DATA_BASE, self.__class__.__module__.replace('.', os.sep),
self.__class__.__name__)
self.reset_filesystem()
self._originals = {} # Map of object -> {symbol_string: original_value}
def _set_up_logging(self):
self._log = cStringIO.StringIO()
self._stream_handler = logging.StreamHandler(self._log)
self._stream_handler.setFormatter(
logging.Formatter(fmt='%(levelname)s: %(message)s'))
self._logger = logging.getLogger()
self._logger.addHandler(self._stream_handler)
self._logger.setLevel(self.LOG_LEVEL)
def tearDown(self):
self._unswap_all()
self.reset_filesystem(remove_only=True)
self._tear_down_logging()
super(TestBase, self).tearDown()
def _tear_down_logging(self):
self._logger.removeHandler(self._stream_handler)
self._log.close()
def get_log(self):
self._log.flush()
return self._log.getvalue()
def assertLogContains(self, message):
self.assertIn(message, self.get_log())
def reset_filesystem(self, remove_only=False):
if os.path.exists(self.test_tempdir):
shutil.rmtree(self.test_tempdir)
if not remove_only:
os.makedirs(self.test_tempdir)
def run(self, result=None):
if not result:
result = self.defaultTestResult()
super(TestBase, self).run(result)
if not result.wasSuccessful():
TestBase.HAS_PENDING_FAILURE = True
def shortDescription(self):
# Suppress default logging of docstrings. Instead log name/status only.
return None
def swap(self, source, symbol, new): # pylint: disable=invalid-name
if source not in self._originals:
self._originals[source] = {}
if not self._originals[source].get(symbol, None):
self._originals[source][symbol] = getattr(source, symbol)
setattr(source, symbol, new)
def _unswap_all(self):
for source, symbol_to_value in self._originals.iteritems():
for symbol, value in symbol_to_value.iteritems():
setattr(source, symbol, value)
class FunctionalTestBase(TestBase):
class AppEngineTestBase(FunctionalTestBase):
def getApp(self):
raise Exception('Not implemented.')
def setUp(self):
super(AppEngineTestBase, self).setUp()
empty_environ()
# setup an app to be tested
self.testapp = webtest.TestApp(self.getApp())
self.testbed = testbed.Testbed()
self.testbed.activate()
# configure datastore policy to emulate instantaneously and globally
# consistent HRD; we also patch dev_appserver in main.py to run under
# the same policy
policy = datastore_stub_util.PseudoRandomHRConsistencyPolicy(
probability=1)
# declare any relevant App Engine service stubs here
self.testbed.init_user_stub()
self.testbed.init_memcache_stub()
self.testbed.init_datastore_v3_stub(consistency_policy=policy)
self.testbed.init_taskqueue_stub(root_path=os.environ['SOURCE_DIR'])
self.taskq = self.testbed.get_stub(testbed.TASKQUEUE_SERVICE_NAME)
self.testbed.init_urlfetch_stub()
self.testbed.init_files_stub()
self.testbed.init_blobstore_stub()
self.testbed.init_mail_stub()
self.testbed.init_app_identity_stub()
# TODO(emichael): Fix this when an official stub is created
self.testbed._register_stub(
'search', simple_search_stub.SearchServiceStub())
self.task_dispatcher = task_queue.TaskQueueHandlerDispatcher(
self.testapp, self.taskq)
# Handle for testing sent mail.
self.mail_stub = self.testbed.get_stub(testbed.MAIL_SERVICE_NAME)
def tearDown(self):
self.testbed.deactivate()
super(AppEngineTestBase, self).tearDown()
def get_mail_stub(self):
return self.testbed.get_stub(testbed.MAIL_SERVICE_NAME)
def create_test_suite(parsed_args):
loader = unittest.TestLoader()
if not parsed_args.test_class_name:
raise Exception('Expected --test_class_name to be specified.')
return loader.loadTestsFromName(parsed_args.test_class_name)
def fix_sys_path():
import dev_appserver # pylint: disable=C6204
# dev_appserver.fix_sys_path() prepends GAE paths to sys.path and hides
# our classes like 'tests' behind other modules that have 'tests'.
# Here, unlike dev_appserver, we append the path instead of prepending it,
# so that our classes come first.
sys.path += dev_appserver.EXTRA_PATHS[:]
# This is to work around an issue with the __import__ builtin. The
# problem seems to be that if the sys.path list contains an item that
# partially matches a package name to import, __import__ will get
# confused, and report an error message (which removes the first path
# element from the module it's complaining about, which does not help
# importation starts running. (And we have to do it here, because if we
# try to do this within appengine_config, AppEngine will throw an error)
if appengine_config.BUNDLE_ROOT in sys.path:
sys.path.remove(appengine_config.BUNDLE_ROOT)
sys.path.insert(0, appengine_config.BUNDLE_ROOT)
def main():
fix_sys_path()
etl._set_env_vars_from_app_yaml()
parsed_args = _PARSER.parse_args()
test_suite = create_test_suite(parsed_args)
result = unittest.TextTestRunner(verbosity=2).run(test_suite)
if result.errors or result.failures:
raise Exception(
'Test suite failed: %s errors, %s failures of '
' %s tests run.' % (
len(result.errors), len(result.failures), result.testsRun))
import tests.functional.actions as actions
count = len(actions.UNIQUE_URLS_FOUND.keys())
result.stream.writeln('INFO: Unique URLs found: %s' % count)
result.stream.writeln('INFO: All %s tests PASSED!' % result.testsRun)
if __name__ == '__main__':
appengine_config.gcb_force_default_encoding('ascii')
main()
| true | true |
f7259138e078865f2f6c44a93538b382ee2f400a | 786 | py | Python | pyleecan/Methods/Output/OutElec/get_Nr.py | tobsen2code/pyleecan | 5b1ded9e389e0c79ed7b7c878b6e939f2d9962e9 | [
"Apache-2.0"
] | 5 | 2020-03-05T15:22:39.000Z | 2022-03-02T15:26:08.000Z | pyleecan/Methods/Output/OutElec/get_Nr.py | Eomys/Pyleecan | 4d7f0cbabf0311006963e7a2f435db2ecd901118 | [
"Apache-2.0"
] | 8 | 2020-07-09T07:43:01.000Z | 2022-03-08T12:52:06.000Z | pyleecan/Methods/Output/OutElec/get_Nr.py | Eomys/Pyleecan | 4d7f0cbabf0311006963e7a2f435db2ecd901118 | [
"Apache-2.0"
] | 4 | 2019-12-23T12:38:01.000Z | 2022-01-07T10:47:48.000Z | from numpy import ones
def get_Nr(self, Time=None):
"""Create speed in function of time vector Nr
Parameters
----------
self : OutElec
An OutElec object
Time : Data
a time axis (SciDataTool Data object)
Returns
-------
Nr: ndarray
speed in function of time
"""
if Time is None:
if self.axes_dict is not None and "time" in self.axes_dict:
Time = self.axes_dict["time"]
else:
raise Exception('You must define "time" property before calling get_Nr')
if self.OP.get_N0() is None:
raise Exception('You must define "N0" before calling get_Nr')
# Same speed for every timestep
Nr = self.OP.get_N0() * ones(Time.get_length(is_smallestperiod=True))
return Nr
| 23.818182 | 84 | 0.611959 | from numpy import ones
def get_Nr(self, Time=None):
if Time is None:
if self.axes_dict is not None and "time" in self.axes_dict:
Time = self.axes_dict["time"]
else:
raise Exception('You must define "time" property before calling get_Nr')
if self.OP.get_N0() is None:
raise Exception('You must define "N0" before calling get_Nr')
Nr = self.OP.get_N0() * ones(Time.get_length(is_smallestperiod=True))
return Nr
| true | true |
f72595be695d8aadf33d417466be973d11af82cd | 21,863 | py | Python | qkeras/qoctave.py | thaarres/qkeras | a04bfe65a144ef6dd0ca0880866ef6109c3638fc | [
"Apache-2.0"
] | 1 | 2019-12-08T16:22:21.000Z | 2019-12-08T16:22:21.000Z | qkeras/qoctave.py | Athena-INT/qkeras | 4cd7bb948bca11507d124cdddc3afda4956a72db | [
"Apache-2.0"
] | null | null | null | qkeras/qoctave.py | Athena-INT/qkeras | 4cd7bb948bca11507d124cdddc3afda4956a72db | [
"Apache-2.0"
] | null | null | null | # Copyright 2019 Google LLC
#
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Octave Convolution."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
from keras.layers import Activation
from keras.layers import Add
from keras.layers import AveragePooling2D
from keras.layers import Conv2D
from keras.layers import SeparableConv2D
from keras.layers import UpSampling2D
from .qlayers import QActivation
from .qlayers import QAveragePooling2D
from .qlayers import QConv2D
from .qlayers import QSeparableConv2D
def GetActivationSuffix(activation):
"""Returns suffix for layer name to facilitate debugging."""
if not activation:
return "linear"
if "po2" in activation:
return "q2"
elif "quantized_relu" in activation:
suffix = "qr"
elif "quantized_tanh" in activation:
suffix = "qt"
else:
suffix = "qb"
numbers = re.findall(r"[0-9]+", activation)
numbers = [n + "_" if len(n) > 1 else n for n in numbers]
return suffix + "".join(numbers)
def QOctaveConv2D(
filters,
kernel_size,
alpha,
strides=(1, 1),
padding="valid",
kernel_initializer="he_normal",
bias_initializer="zeros",
# NOTE: kernel_regularizer not used with separable convolution
kernel_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
use_separable=True,
name="",
**kwargs):
"""Implements quantized QOctaveConv2D."""
def _QOctaveConv2DInternal(x):
"""Computes QOctaveConv2D on a tensor."""
x_h, x_l = x
bias_quantizer = kwargs.get("bias_quantizer", None)
kernel_quantizer = kwargs.get("kernel_quantizer", None)
depthwise_quantizer = kwargs.get("depthwise_quantizer", None)
pointwise_quantizer = kwargs.get("pointwise_quantizer", None)
acc_quantizer = kwargs.get("acc_quantizer", None)
pooling_quantizer = kwargs.get("pooling_quantizer", None)
depthwise_activation = kwargs.get("depthwise_activation", None)
activation = kwargs.get("activation", None)
bias_range = kwargs.get("bias_range", 1.0)
kernel_range = kwargs.get("kernel_range", 1.0)
depthwise_range = kwargs.get("depthwise_range", 1.0)
pointwise_range = kwargs.get("pointwise_range", 1.0)
if activation:
act_suffix = "_" + GetActivationSuffix(activation)
acc_suffix = "_" + GetActivationSuffix(acc_quantizer)
if alpha == -1.0:
if use_separable:
x_h = QSeparableConv2D(
filters, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
depthwise_quantizer=depthwise_quantizer,
pointwise_quantizer=pointwise_quantizer,
bias_quantizer=bias_quantizer,
depthwise_activation=depthwise_activation,
pointwise_range=pointwise_range,
depthwise_range=depthwise_range,
bias_range=bias_range,
name=name + "_c_h_to_h")(x_h)
else:
x_h = QConv2D(
filters, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
kernel_quantizer=kernel_quantizer,
bias_quantizer=bias_quantizer,
kernel_range=kernel_range,
bias_range=bias_range,
name=name + "_c_h_to_h")(x_h)
if activation:
x_h = QActivation(
activation, name=name + "_c_h_to_h_act" + act_suffix)(
x_h)
return [x_h, None]
co_h = int(filters * (1 - alpha))
co_l = filters - co_h
x_h_to_h = None
x_h_to_l = None
x_l_to_l = None
x_l_to_h = None
if co_h > 0:
if x_h is not None:
if use_separable:
x_h_to_h = QSeparableConv2D(
co_h, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
depthwise_quantizer=depthwise_quantizer,
pointwise_quantizer=pointwise_quantizer,
bias_quantizer=bias_quantizer,
depthwise_activation=depthwise_activation,
pointwise_range=pointwise_range,
depthwise_range=depthwise_range,
bias_range=bias_range,
name=name + "_c_h_to_h")(x_h)
else:
x_h_to_h = QConv2D(
co_h, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
kernel_quantizer=kernel_quantizer,
bias_quantizer=bias_quantizer,
kernel_range=kernel_range,
bias_range=bias_range,
name=name + "_c_h_to_h")(x_h)
if acc_quantizer:
x_h_to_h = QActivation(
acc_quantizer,
name=name + "_c_h_to_h_act" + acc_suffix)(x_h_to_h)
if co_l > 0:
if x_h is not None:
x_h_to_l = QAveragePooling2D(
pool_size=2, strides=2,
quantizer=pooling_quantizer,
name=name + "_avg_h_to_l")(x_h)
if use_separable:
x_h_to_l = QSeparableConv2D(
co_l, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
depthwise_quantizer=depthwise_quantizer,
pointwise_quantizer=pointwise_quantizer,
bias_quantizer=bias_quantizer,
depthwise_activation=depthwise_activation,
pointwise_range=pointwise_range,
depthwise_range=depthwise_range,
bias_range=bias_range,
name=name + "_c_h_to_l")(x_h_to_l)
else:
x_h_to_l = QConv2D(
co_l, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
kernel_quantizer=kernel_quantizer,
bias_quantizer=bias_quantizer,
kernel_range=kernel_range,
bias_range=bias_range,
name=name + "_c_h_to_l")(x_h_to_l)
if acc_quantizer:
x_h_to_l = QActivation(
acc_quantizer,
name=name + "_c_h_to_l_act" + acc_suffix)(x_h_to_l)
if co_h > 0:
if x_l is not None:
_, height, width, _ = x_l.shape.as_list()
if height == 1 and width == 1:
local_kernel = 1
local_strides = 1
local_padding = "same"
upsampling = False
else:
local_kernel = kernel_size
local_strides = strides
local_padding = padding
upsampling = True
if use_separable and upsampling:
x_l_to_h = QSeparableConv2D(
co_h, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
depthwise_quantizer=depthwise_quantizer,
pointwise_quantizer=pointwise_quantizer,
bias_quantizer=bias_quantizer,
depthwise_activation=depthwise_activation,
pointwise_range=pointwise_range,
depthwise_range=depthwise_range,
bias_range=bias_range,
name=name + "_c_l_to_h")(x_l)
else:
x_l_to_h = QConv2D(
co_h, local_kernel, strides=local_strides, padding=local_padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
kernel_quantizer=kernel_quantizer,
bias_quantizer=bias_quantizer,
kernel_range=kernel_range,
bias_range=bias_range,
name=name + "_c_l_to_h")(x_l)
if acc_quantizer:
x_l_to_h = QActivation(
acc_quantizer,
name=name + "_c_l_to_h_act" + acc_suffix)(x_l_to_h)
if upsampling:
x_l_to_h = UpSampling2D(
size=(2, 2), name=name + "_u_l_to_h")(x_l_to_h)
if co_l > 0:
if x_l is not None:
if use_separable:
x_l_to_l = QSeparableConv2D(
co_l, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
depthwise_quantizer=depthwise_quantizer,
pointwise_quantizer=depthwise_quantizer,
bias_quantizer=bias_quantizer,
depthwise_activation=depthwise_activation,
pointwise_range=pointwise_range,
depthwise_range=depthwise_range,
bias_range=bias_range,
name=name + "_c_l_to_l")(x_l)
else:
x_l_to_l = QConv2D(
co_l, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
kernel_quantizer=kernel_quantizer,
bias_quantizer=bias_quantizer,
kernel_range=kernel_range,
bias_range=bias_range,
name=name + "_c_l_to_l")(x_l)
if acc_quantizer:
x_l_to_l = QActivation(
acc_quantizer, name=name + "_c_l_to_l_act" + acc_suffix)(
x_l_to_l)
if x_h_to_h is not None and x_l_to_h is not None:
x_h = Add(name=name + "_a_h")([x_h_to_h, x_l_to_h])
elif x_h_to_h is not None:
x_h = x_h_to_h
elif x_l_to_h is not None:
x_h = x_l_to_h
else:
x_h = None
if x_l_to_l is not None and x_h_to_l is not None:
x_l = Add(name=name + "_a_l")([x_l_to_l, x_h_to_l])
elif x_l_to_l is not None:
x_l = x_l_to_l
elif x_h_to_l is not None:
x_l = x_h_to_l
else:
x_l = None
if x_h is not None and activation is not None:
x_h = QActivation(activation,
name=name + "_h_act" + act_suffix)(x_h)
if x_l is not None and activation is not None:
x_l = QActivation(activation,
name=name + "_l_act" + act_suffix)(x_l)
return [x_h, x_l]
return _QOctaveConv2DInternal
def OctaveConv2D(
filters, kernel_size, alpha,
strides=(1, 1), padding="valid",
kernel_initializer="he_normal",
bias_initializer="zeros",
kernel_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
activation=None,
use_separable=True,
name="",
**kwargs):
"""Implements OctaveConv2D."""
def _OctaveConv2DInternal(x):
"""Computes octave on tensor."""
acc_quantizer = kwargs.get("acc_quantizer", None)
x_h, x_l = x
if alpha == -1.0:
if use_separable:
x_h = SeparableConv2D(
filters, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_h_to_h")(x_h)
else:
x_h = Conv2D(
filters, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name+"_c_h_to_h")(x_h)
if activation:
x_h = Activation(activation, name=name + "_c_h_to_h_act")(x_h)
return [x_h, None]
co_h = int(filters * (1 - alpha))
co_l = filters - co_h
x_h_to_h = None
x_h_to_l = None
x_l_to_l = None
x_l_to_h = None
if co_h > 0:
if x_h is not None:
if use_separable:
x_h_to_h = SeparableConv2D(
co_h, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_h_to_h")(x_h)
else:
x_h_to_h = Conv2D(
co_h, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_h_to_h")(x_h)
if activation:
x_h_to_h = Activation(
acc_quantizer, name=name + "_c_h_to_h_act")(x_h_to_h)
if co_l > 0:
if x_h is not None:
x_h_to_l = AveragePooling2D(pool_size=2, strides=2,
name=name + "_p_h_to_l")(x_h)
if use_separable:
x_h_to_l = SeparableConv2D(
co_l, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_h_to_l")(x_h_to_l)
else:
x_h_to_l = Conv2D(
co_l, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_h_to_l")(x_h_to_l)
if activation:
x_h_to_l = Activation(
acc_quantizer, name=name + "_c_h_to_l_act")(x_h_to_l)
if co_h > 0:
if x_l is not None:
_, height, width, _ = x_l.shape.as_list()
if height == 1 and width == 1:
local_kernel = 1
local_strides = 1
local_padding = "same"
upsampling = False
else:
local_kernel = kernel_size
local_strides = strides
local_padding = padding
upsampling = True
if use_separable and upsampling:
x_l_to_h = SeparableConv2D(
co_h, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_l_to_h")(x_l)
else:
x_l_to_h = Conv2D(
co_h, local_kernel, strides=local_strides, padding=local_padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_l_to_h")(x_l)
if activation:
x_l_to_h = Activation(
acc_quantizer, name=name + "_c_l_to_h_act")(x_l_to_h)
if upsampling:
x_l_to_h = UpSampling2D(
size=(2, 2), name=name + "_u_l_to_h")(x_l_to_h)
if co_l > 0:
if x_l is not None:
if use_separable:
x_l_to_l = SeparableConv2D(
co_l, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_l_to_l")(x_l)
else:
x_l_to_l = Conv2D(
co_l, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_l_to_l")(x_l)
if activation:
x_l_to_l = Activation(
acc_quantizer, name=name + "_c_l_to_l_act")(x_l_to_l)
if x_h_to_h is not None and x_l_to_h is not None:
x_h = Add(name=name + "_a_h")([x_h_to_h, x_l_to_h])
elif x_h_to_h is not None:
x_h = x_h_to_h
elif x_l_to_h is not None:
x_h = x_l_to_h
else:
x_h = None
if x_l_to_l is not None and x_h_to_l is not None:
x_l = Add(name=name + "_a_l")([x_l_to_l, x_h_to_l])
elif x_l_to_l is not None:
x_l = x_l_to_l
elif x_h_to_l is not None:
x_l = x_h_to_l
else:
x_l = None
if x_h is not None:
x_h = Activation(activation, name=name + "_h_act")(x_h)
if x_l is not None:
x_l = Activation(activation, name=name + "_l_act")(x_l)
return (x_h, x_l)
return _OctaveConv2DInternal
| 36.806397 | 80 | 0.634725 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
from keras.layers import Activation
from keras.layers import Add
from keras.layers import AveragePooling2D
from keras.layers import Conv2D
from keras.layers import SeparableConv2D
from keras.layers import UpSampling2D
from .qlayers import QActivation
from .qlayers import QAveragePooling2D
from .qlayers import QConv2D
from .qlayers import QSeparableConv2D
def GetActivationSuffix(activation):
if not activation:
return "linear"
if "po2" in activation:
return "q2"
elif "quantized_relu" in activation:
suffix = "qr"
elif "quantized_tanh" in activation:
suffix = "qt"
else:
suffix = "qb"
numbers = re.findall(r"[0-9]+", activation)
numbers = [n + "_" if len(n) > 1 else n for n in numbers]
return suffix + "".join(numbers)
def QOctaveConv2D(
filters,
kernel_size,
alpha,
strides=(1, 1),
padding="valid",
kernel_initializer="he_normal",
bias_initializer="zeros",
kernel_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
use_separable=True,
name="",
**kwargs):
def _QOctaveConv2DInternal(x):
x_h, x_l = x
bias_quantizer = kwargs.get("bias_quantizer", None)
kernel_quantizer = kwargs.get("kernel_quantizer", None)
depthwise_quantizer = kwargs.get("depthwise_quantizer", None)
pointwise_quantizer = kwargs.get("pointwise_quantizer", None)
acc_quantizer = kwargs.get("acc_quantizer", None)
pooling_quantizer = kwargs.get("pooling_quantizer", None)
depthwise_activation = kwargs.get("depthwise_activation", None)
activation = kwargs.get("activation", None)
bias_range = kwargs.get("bias_range", 1.0)
kernel_range = kwargs.get("kernel_range", 1.0)
depthwise_range = kwargs.get("depthwise_range", 1.0)
pointwise_range = kwargs.get("pointwise_range", 1.0)
if activation:
act_suffix = "_" + GetActivationSuffix(activation)
acc_suffix = "_" + GetActivationSuffix(acc_quantizer)
if alpha == -1.0:
if use_separable:
x_h = QSeparableConv2D(
filters, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
depthwise_quantizer=depthwise_quantizer,
pointwise_quantizer=pointwise_quantizer,
bias_quantizer=bias_quantizer,
depthwise_activation=depthwise_activation,
pointwise_range=pointwise_range,
depthwise_range=depthwise_range,
bias_range=bias_range,
name=name + "_c_h_to_h")(x_h)
else:
x_h = QConv2D(
filters, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
kernel_quantizer=kernel_quantizer,
bias_quantizer=bias_quantizer,
kernel_range=kernel_range,
bias_range=bias_range,
name=name + "_c_h_to_h")(x_h)
if activation:
x_h = QActivation(
activation, name=name + "_c_h_to_h_act" + act_suffix)(
x_h)
return [x_h, None]
co_h = int(filters * (1 - alpha))
co_l = filters - co_h
x_h_to_h = None
x_h_to_l = None
x_l_to_l = None
x_l_to_h = None
if co_h > 0:
if x_h is not None:
if use_separable:
x_h_to_h = QSeparableConv2D(
co_h, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
depthwise_quantizer=depthwise_quantizer,
pointwise_quantizer=pointwise_quantizer,
bias_quantizer=bias_quantizer,
depthwise_activation=depthwise_activation,
pointwise_range=pointwise_range,
depthwise_range=depthwise_range,
bias_range=bias_range,
name=name + "_c_h_to_h")(x_h)
else:
x_h_to_h = QConv2D(
co_h, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
kernel_quantizer=kernel_quantizer,
bias_quantizer=bias_quantizer,
kernel_range=kernel_range,
bias_range=bias_range,
name=name + "_c_h_to_h")(x_h)
if acc_quantizer:
x_h_to_h = QActivation(
acc_quantizer,
name=name + "_c_h_to_h_act" + acc_suffix)(x_h_to_h)
if co_l > 0:
if x_h is not None:
x_h_to_l = QAveragePooling2D(
pool_size=2, strides=2,
quantizer=pooling_quantizer,
name=name + "_avg_h_to_l")(x_h)
if use_separable:
x_h_to_l = QSeparableConv2D(
co_l, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
depthwise_quantizer=depthwise_quantizer,
pointwise_quantizer=pointwise_quantizer,
bias_quantizer=bias_quantizer,
depthwise_activation=depthwise_activation,
pointwise_range=pointwise_range,
depthwise_range=depthwise_range,
bias_range=bias_range,
name=name + "_c_h_to_l")(x_h_to_l)
else:
x_h_to_l = QConv2D(
co_l, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
kernel_quantizer=kernel_quantizer,
bias_quantizer=bias_quantizer,
kernel_range=kernel_range,
bias_range=bias_range,
name=name + "_c_h_to_l")(x_h_to_l)
if acc_quantizer:
x_h_to_l = QActivation(
acc_quantizer,
name=name + "_c_h_to_l_act" + acc_suffix)(x_h_to_l)
if co_h > 0:
if x_l is not None:
_, height, width, _ = x_l.shape.as_list()
if height == 1 and width == 1:
local_kernel = 1
local_strides = 1
local_padding = "same"
upsampling = False
else:
local_kernel = kernel_size
local_strides = strides
local_padding = padding
upsampling = True
if use_separable and upsampling:
x_l_to_h = QSeparableConv2D(
co_h, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
depthwise_quantizer=depthwise_quantizer,
pointwise_quantizer=pointwise_quantizer,
bias_quantizer=bias_quantizer,
depthwise_activation=depthwise_activation,
pointwise_range=pointwise_range,
depthwise_range=depthwise_range,
bias_range=bias_range,
name=name + "_c_l_to_h")(x_l)
else:
x_l_to_h = QConv2D(
co_h, local_kernel, strides=local_strides, padding=local_padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
kernel_quantizer=kernel_quantizer,
bias_quantizer=bias_quantizer,
kernel_range=kernel_range,
bias_range=bias_range,
name=name + "_c_l_to_h")(x_l)
if acc_quantizer:
x_l_to_h = QActivation(
acc_quantizer,
name=name + "_c_l_to_h_act" + acc_suffix)(x_l_to_h)
if upsampling:
x_l_to_h = UpSampling2D(
size=(2, 2), name=name + "_u_l_to_h")(x_l_to_h)
if co_l > 0:
if x_l is not None:
if use_separable:
x_l_to_l = QSeparableConv2D(
co_l, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
depthwise_quantizer=depthwise_quantizer,
pointwise_quantizer=depthwise_quantizer,
bias_quantizer=bias_quantizer,
depthwise_activation=depthwise_activation,
pointwise_range=pointwise_range,
depthwise_range=depthwise_range,
bias_range=bias_range,
name=name + "_c_l_to_l")(x_l)
else:
x_l_to_l = QConv2D(
co_l, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
kernel_quantizer=kernel_quantizer,
bias_quantizer=bias_quantizer,
kernel_range=kernel_range,
bias_range=bias_range,
name=name + "_c_l_to_l")(x_l)
if acc_quantizer:
x_l_to_l = QActivation(
acc_quantizer, name=name + "_c_l_to_l_act" + acc_suffix)(
x_l_to_l)
if x_h_to_h is not None and x_l_to_h is not None:
x_h = Add(name=name + "_a_h")([x_h_to_h, x_l_to_h])
elif x_h_to_h is not None:
x_h = x_h_to_h
elif x_l_to_h is not None:
x_h = x_l_to_h
else:
x_h = None
if x_l_to_l is not None and x_h_to_l is not None:
x_l = Add(name=name + "_a_l")([x_l_to_l, x_h_to_l])
elif x_l_to_l is not None:
x_l = x_l_to_l
elif x_h_to_l is not None:
x_l = x_h_to_l
else:
x_l = None
if x_h is not None and activation is not None:
x_h = QActivation(activation,
name=name + "_h_act" + act_suffix)(x_h)
if x_l is not None and activation is not None:
x_l = QActivation(activation,
name=name + "_l_act" + act_suffix)(x_l)
return [x_h, x_l]
return _QOctaveConv2DInternal
def OctaveConv2D(
filters, kernel_size, alpha,
strides=(1, 1), padding="valid",
kernel_initializer="he_normal",
bias_initializer="zeros",
kernel_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
activation=None,
use_separable=True,
name="",
**kwargs):
def _OctaveConv2DInternal(x):
acc_quantizer = kwargs.get("acc_quantizer", None)
x_h, x_l = x
if alpha == -1.0:
if use_separable:
x_h = SeparableConv2D(
filters, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_h_to_h")(x_h)
else:
x_h = Conv2D(
filters, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name+"_c_h_to_h")(x_h)
if activation:
x_h = Activation(activation, name=name + "_c_h_to_h_act")(x_h)
return [x_h, None]
co_h = int(filters * (1 - alpha))
co_l = filters - co_h
x_h_to_h = None
x_h_to_l = None
x_l_to_l = None
x_l_to_h = None
if co_h > 0:
if x_h is not None:
if use_separable:
x_h_to_h = SeparableConv2D(
co_h, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_h_to_h")(x_h)
else:
x_h_to_h = Conv2D(
co_h, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_h_to_h")(x_h)
if activation:
x_h_to_h = Activation(
acc_quantizer, name=name + "_c_h_to_h_act")(x_h_to_h)
if co_l > 0:
if x_h is not None:
x_h_to_l = AveragePooling2D(pool_size=2, strides=2,
name=name + "_p_h_to_l")(x_h)
if use_separable:
x_h_to_l = SeparableConv2D(
co_l, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_h_to_l")(x_h_to_l)
else:
x_h_to_l = Conv2D(
co_l, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_h_to_l")(x_h_to_l)
if activation:
x_h_to_l = Activation(
acc_quantizer, name=name + "_c_h_to_l_act")(x_h_to_l)
if co_h > 0:
if x_l is not None:
_, height, width, _ = x_l.shape.as_list()
if height == 1 and width == 1:
local_kernel = 1
local_strides = 1
local_padding = "same"
upsampling = False
else:
local_kernel = kernel_size
local_strides = strides
local_padding = padding
upsampling = True
if use_separable and upsampling:
x_l_to_h = SeparableConv2D(
co_h, kernel_size, strides=strides, padding=padding,
depthwise_regularizer=kernel_regularizer,
depthwise_constraint=kernel_constraint,
depthwise_initializer=kernel_initializer,
pointwise_regularizer=kernel_regularizer,
pointwise_constraint=kernel_constraint,
pointwise_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_l_to_h")(x_l)
else:
x_l_to_h = Conv2D(
co_h, local_kernel, strides=local_strides, padding=local_padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_l_to_h")(x_l)
if activation:
x_l_to_h = Activation(
acc_quantizer, name=name + "_c_l_to_h_act")(x_l_to_h)
if upsampling:
x_l_to_h = UpSampling2D(
size=(2, 2), name=name + "_u_l_to_h")(x_l_to_h)
if co_l > 0:
if x_l is not None:
if use_separable:
x_l_to_l = SeparableConv2D(
co_l, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_l_to_l")(x_l)
else:
x_l_to_l = Conv2D(
co_l, kernel_size, strides=strides, padding=padding,
kernel_regularizer=kernel_regularizer,
kernel_constraint=kernel_constraint,
kernel_initializer=kernel_initializer,
bias_regularizer=bias_regularizer,
bias_constraint=bias_constraint,
bias_initializer=bias_initializer,
name=name + "_c_l_to_l")(x_l)
if activation:
x_l_to_l = Activation(
acc_quantizer, name=name + "_c_l_to_l_act")(x_l_to_l)
if x_h_to_h is not None and x_l_to_h is not None:
x_h = Add(name=name + "_a_h")([x_h_to_h, x_l_to_h])
elif x_h_to_h is not None:
x_h = x_h_to_h
elif x_l_to_h is not None:
x_h = x_l_to_h
else:
x_h = None
if x_l_to_l is not None and x_h_to_l is not None:
x_l = Add(name=name + "_a_l")([x_l_to_l, x_h_to_l])
elif x_l_to_l is not None:
x_l = x_l_to_l
elif x_h_to_l is not None:
x_l = x_h_to_l
else:
x_l = None
if x_h is not None:
x_h = Activation(activation, name=name + "_h_act")(x_h)
if x_l is not None:
x_l = Activation(activation, name=name + "_l_act")(x_l)
return (x_h, x_l)
return _OctaveConv2DInternal
| true | true |
f7259600ce77f1327a75cfc7ad33fc54ddf8e263 | 261 | py | Python | daily_work/daily_work/doctype/daily_default_setting/daily_default_setting.py | muirawachanga/mwai | 1a95cba5bc6368361fc48984ba05307ae4093ead | [
"MIT"
] | null | null | null | daily_work/daily_work/doctype/daily_default_setting/daily_default_setting.py | muirawachanga/mwai | 1a95cba5bc6368361fc48984ba05307ae4093ead | [
"MIT"
] | null | null | null | daily_work/daily_work/doctype/daily_default_setting/daily_default_setting.py | muirawachanga/mwai | 1a95cba5bc6368361fc48984ba05307ae4093ead | [
"MIT"
] | null | null | null | # -*- coding: utf-8 -*-
# Copyright (c) 2019, steve and contributors
# For license information, please see license.txt
from __future__ import unicode_literals
import frappe
from frappe.model.document import Document
class DailyDefaultSetting(Document):
pass
| 23.727273 | 49 | 0.785441 |
from __future__ import unicode_literals
import frappe
from frappe.model.document import Document
class DailyDefaultSetting(Document):
pass
| true | true |
f7259615a162f1f9b8997248b81998d51618e2f1 | 1,384 | py | Python | cli.py | stesla/arxtools | 7f1a3b973e3d78faed4085d547b7d27ebcd9838d | [
"MIT"
] | null | null | null | cli.py | stesla/arxtools | 7f1a3b973e3d78faed4085d547b7d27ebcd9838d | [
"MIT"
] | null | null | null | cli.py | stesla/arxtools | 7f1a3b973e3d78faed4085d547b7d27ebcd9838d | [
"MIT"
] | null | null | null | import click
import configparser
import json
import sys
from arxtools import export_clues, fetch_clues
from arxtools.clue import Clue
@click.group()
def cli():
pass
def get_character_info(name):
config = configparser.ConfigParser()
config.read('arxtools.ini')
try:
return config[name.lower()]
except KeyError:
click.echo(f'No character named "{name}" found.', err=True)
sys.exit(1)
@cli.command("import")
@click.argument('name')
def _import(name):
info = get_character_info(name)
username = info['username']
password = info['password']
clues = fetch_clues(username, password)
click.echo(json.dumps([c.to_dict() for c in clues]))
@cli.command("export")
@click.argument('name')
def _export(name):
info = get_character_info(name)
directory = info['directory']
clues = [Clue.from_dict(c) for c in json.load(sys.stdin)]
export_clues(clues, directory)
@cli.command('update')
@click.argument('name')
def _update(name):
info = get_character_info(name)
username = info['username']
password = info['password']
directory = info['directory']
click.echo(f'Fetching clues for {username}...', err=True)
clues = fetch_clues(username, password)
click.echo(f'Exporting to markdown in {directory}...', err=True)
export_clues(clues, directory)
if __name__ == '__main__':
cli()
| 24.714286 | 68 | 0.682803 | import click
import configparser
import json
import sys
from arxtools import export_clues, fetch_clues
from arxtools.clue import Clue
@click.group()
def cli():
pass
def get_character_info(name):
config = configparser.ConfigParser()
config.read('arxtools.ini')
try:
return config[name.lower()]
except KeyError:
click.echo(f'No character named "{name}" found.', err=True)
sys.exit(1)
@cli.command("import")
@click.argument('name')
def _import(name):
info = get_character_info(name)
username = info['username']
password = info['password']
clues = fetch_clues(username, password)
click.echo(json.dumps([c.to_dict() for c in clues]))
@cli.command("export")
@click.argument('name')
def _export(name):
info = get_character_info(name)
directory = info['directory']
clues = [Clue.from_dict(c) for c in json.load(sys.stdin)]
export_clues(clues, directory)
@cli.command('update')
@click.argument('name')
def _update(name):
info = get_character_info(name)
username = info['username']
password = info['password']
directory = info['directory']
click.echo(f'Fetching clues for {username}...', err=True)
clues = fetch_clues(username, password)
click.echo(f'Exporting to markdown in {directory}...', err=True)
export_clues(clues, directory)
if __name__ == '__main__':
cli()
| true | true |
f7259776fb98e992d885bf99cf4adf99f37e8745 | 8,549 | py | Python | gui/qt/utxo_list.py | ComputerCraftr/openswap | 7de04aa80dab79bebe4b64483011dad70a48694c | [
"MIT"
] | 16 | 2018-11-05T13:19:02.000Z | 2021-04-06T12:11:49.000Z | gui/qt/utxo_list.py | ComputerCraftr/openswap | 7de04aa80dab79bebe4b64483011dad70a48694c | [
"MIT"
] | 11 | 2018-11-11T08:56:03.000Z | 2018-12-08T02:31:57.000Z | gui/qt/utxo_list.py | ComputerCraftr/openswap | 7de04aa80dab79bebe4b64483011dad70a48694c | [
"MIT"
] | 5 | 2019-01-07T13:45:05.000Z | 2020-06-12T14:13:21.000Z | #!/usr/bin/env python
#
# Electrum - lightweight Bitcoin client
# Copyright (C) 2015 Thomas Voegtlin
#
# 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.
from .util import *
from electroncash.i18n import _
class UTXOList(MyTreeWidget):
filter_columns = [0, 2] # Address, Label
def __init__(self, parent=None):
MyTreeWidget.__init__(self, parent, self.create_menu, [ _('Address'), _('Label'), _('Amount'), _('Height'), _('Output point')], 1)
self.setSelectionMode(QAbstractItemView.ExtendedSelection)
self.setSortingEnabled(True)
# force attributes to always be defined, even if None, at construction.
self.wallet = self.parent.wallet if hasattr(self.parent, 'wallet') else None
self.utxos = list()
def get_name(self, x):
return x.get('prevout_hash') + ":%d"%x.get('prevout_n')
@rate_limited(1.0) # performance tweak -- limit updates to no more than oncer per second
def update(self):
if self.wallet and self.wallet.thread and not self.wallet.thread.isRunning():
# short-cut return if window was closed and wallet is stopped
return
super().update()
def on_update(self):
prev_selection = self.get_selected() # cache previous selection, if any
self.clear()
self.wallet = self.parent.wallet
if not self.wallet: return
self.utxos = self.wallet.get_utxos()
for x in self.utxos:
address = x['address']
address_text = address.to_ui_string()
height = x['height']
name = self.get_name(x)
label = self.wallet.get_label(x['prevout_hash'])
amount = self.parent.format_amount(x['value'])
utxo_item = SortableTreeWidgetItem([address_text, label, amount,
str(height),
name[0:10] + '...' + name[-2:]])
utxo_item.DataRole = Qt.UserRole+100 # set this here to avoid sorting based on Qt.UserRole+1
utxo_item.setFont(0, QFont(MONOSPACE_FONT))
utxo_item.setFont(4, QFont(MONOSPACE_FONT))
utxo_item.setData(0, Qt.UserRole, name)
a_frozen = self.wallet.is_frozen(address)
c_frozen = x['is_frozen_coin']
if a_frozen and not c_frozen:
# address is frozen, coin is not frozen
# emulate the "Look" off the address_list .py's frozen entry
utxo_item.setBackground(0, QColor('lightblue'))
elif c_frozen and not a_frozen:
# coin is frozen, address is not frozen
utxo_item.setBackground(0, ColorScheme.BLUE.as_color(True))
elif c_frozen and a_frozen:
# both coin and address are frozen so color-code it to indicate that.
utxo_item.setBackground(0, QColor('lightblue'))
utxo_item.setForeground(0, QColor('#3399ff'))
# save the address-level-frozen and coin-level-frozen flags to the data item for retrieval later in create_menu() below.
utxo_item.setData(0, Qt.UserRole+1, "{}{}".format(("a" if a_frozen else ""), ("c" if c_frozen else "")))
self.addChild(utxo_item)
if name in prev_selection:
# NB: This needs to be here after the item is added to the widget. See #979.
utxo_item.setSelected(True) # restore previous selection
def get_selected(self):
return { x.data(0, Qt.UserRole) : x.data(0, Qt.UserRole+1) # dict of "name" -> frozen flags string (eg: "ac")
for x in self.selectedItems() }
def create_menu(self, position):
selected = self.get_selected()
if not selected:
return
menu = QMenu()
coins = filter(lambda x: self.get_name(x) in selected, self.utxos)
spendable_coins = list(filter(lambda x: not selected.get(self.get_name(x), ''), coins))
# Unconditionally add the "Spend" option but leave it disabled if there are no spendable_coins
menu.addAction(_("Spend"), lambda: self.parent.spend_coins(spendable_coins)).setEnabled(bool(spendable_coins))
if len(selected) == 1:
# single selection, offer them the "Details" option and also coin/address "freeze" status, if any
txid = list(selected.keys())[0].split(':')[0]
frozen_flags = list(selected.values())[0]
tx = self.wallet.transactions.get(txid)
if tx:
label = self.wallet.get_label(txid) or None
menu.addAction(_("Details"), lambda: self.parent.show_transaction(tx, label))
act = None
needsep = True
if 'c' in frozen_flags:
menu.addSeparator()
menu.addAction(_("Coin is frozen"), lambda: None).setEnabled(False)
menu.addAction(_("Unfreeze Coin"), lambda: self.set_frozen_coins(list(selected.keys()), False))
menu.addSeparator()
needsep = False
else:
menu.addAction(_("Freeze Coin"), lambda: self.set_frozen_coins(list(selected.keys()), True))
if 'a' in frozen_flags:
if needsep: menu.addSeparator()
menu.addAction(_("Address is frozen"), lambda: None).setEnabled(False)
menu.addAction(_("Unfreeze Address"), lambda: self.set_frozen_addresses_for_coins(list(selected.keys()), False))
else:
menu.addAction(_("Freeze Address"), lambda: self.set_frozen_addresses_for_coins(list(selected.keys()), True))
else:
# multi-selection
menu.addSeparator()
if any(['c' not in flags for flags in selected.values()]):
# they have some coin-level non-frozen in the selection, so add the menu action "Freeze coins"
menu.addAction(_("Freeze Coins"), lambda: self.set_frozen_coins(list(selected.keys()), True))
if any(['c' in flags for flags in selected.values()]):
# they have some coin-level frozen in the selection, so add the menu action "Unfreeze coins"
menu.addAction(_("Unfreeze Coins"), lambda: self.set_frozen_coins(list(selected.keys()), False))
if any(['a' not in flags for flags in selected.values()]):
# they have some address-level non-frozen in the selection, so add the menu action "Freeze addresses"
menu.addAction(_("Freeze Addresses"), lambda: self.set_frozen_addresses_for_coins(list(selected.keys()), True))
if any(['a' in flags for flags in selected.values()]):
# they have some address-level frozen in the selection, so add the menu action "Unfreeze addresses"
menu.addAction(_("Unfreeze Addresses"), lambda: self.set_frozen_addresses_for_coins(list(selected.keys()), False))
menu.exec_(self.viewport().mapToGlobal(position))
def on_permit_edit(self, item, column):
# disable editing fields in this tab (labels)
return False
def set_frozen_coins(self, coins, b):
if self.parent:
self.parent.set_frozen_coin_state(coins, b)
def set_frozen_addresses_for_coins(self, coins, b):
if not self.parent: return
addrs = set()
for utxo in self.utxos:
name = self.get_name(utxo)
if name in coins:
addrs.add(utxo['address'])
if addrs:
self.parent.set_frozen_state(list(addrs), b)
| 52.771605 | 138 | 0.63177 |
from .util import *
from electroncash.i18n import _
class UTXOList(MyTreeWidget):
filter_columns = [0, 2]
def __init__(self, parent=None):
MyTreeWidget.__init__(self, parent, self.create_menu, [ _('Address'), _('Label'), _('Amount'), _('Height'), _('Output point')], 1)
self.setSelectionMode(QAbstractItemView.ExtendedSelection)
self.setSortingEnabled(True)
self.wallet = self.parent.wallet if hasattr(self.parent, 'wallet') else None
self.utxos = list()
def get_name(self, x):
return x.get('prevout_hash') + ":%d"%x.get('prevout_n')
@rate_limited(1.0)
def update(self):
if self.wallet and self.wallet.thread and not self.wallet.thread.isRunning():
return
super().update()
def on_update(self):
prev_selection = self.get_selected()
self.clear()
self.wallet = self.parent.wallet
if not self.wallet: return
self.utxos = self.wallet.get_utxos()
for x in self.utxos:
address = x['address']
address_text = address.to_ui_string()
height = x['height']
name = self.get_name(x)
label = self.wallet.get_label(x['prevout_hash'])
amount = self.parent.format_amount(x['value'])
utxo_item = SortableTreeWidgetItem([address_text, label, amount,
str(height),
name[0:10] + '...' + name[-2:]])
utxo_item.DataRole = Qt.UserRole+100
utxo_item.setFont(0, QFont(MONOSPACE_FONT))
utxo_item.setFont(4, QFont(MONOSPACE_FONT))
utxo_item.setData(0, Qt.UserRole, name)
a_frozen = self.wallet.is_frozen(address)
c_frozen = x['is_frozen_coin']
if a_frozen and not c_frozen:
utxo_item.setBackground(0, QColor('lightblue'))
elif c_frozen and not a_frozen:
# coin is frozen, address is not frozen
utxo_item.setBackground(0, ColorScheme.BLUE.as_color(True))
elif c_frozen and a_frozen:
# both coin and address are frozen so color-code it to indicate that.
utxo_item.setBackground(0, QColor('lightblue'))
utxo_item.setForeground(0, QColor('
# save the address-level-frozen and coin-level-frozen flags to the data item for retrieval later in create_menu() below.
utxo_item.setData(0, Qt.UserRole+1, "{}{}".format(("a" if a_frozen else ""), ("c" if c_frozen else "")))
self.addChild(utxo_item)
if name in prev_selection:
# NB: This needs to be here after the item is added to the widget. See #979.
utxo_item.setSelected(True) # restore previous selection
def get_selected(self):
return { x.data(0, Qt.UserRole) : x.data(0, Qt.UserRole+1) # dict of "name" -> frozen flags string (eg: "ac")
for x in self.selectedItems() }
def create_menu(self, position):
selected = self.get_selected()
if not selected:
return
menu = QMenu()
coins = filter(lambda x: self.get_name(x) in selected, self.utxos)
spendable_coins = list(filter(lambda x: not selected.get(self.get_name(x), ''), coins))
# Unconditionally add the "Spend" option but leave it disabled if there are no spendable_coins
menu.addAction(_("Spend"), lambda: self.parent.spend_coins(spendable_coins)).setEnabled(bool(spendable_coins))
if len(selected) == 1:
# single selection, offer them the "Details" option and also coin/address "freeze" status, if any
txid = list(selected.keys())[0].split(':')[0]
frozen_flags = list(selected.values())[0]
tx = self.wallet.transactions.get(txid)
if tx:
label = self.wallet.get_label(txid) or None
menu.addAction(_("Details"), lambda: self.parent.show_transaction(tx, label))
act = None
needsep = True
if 'c' in frozen_flags:
menu.addSeparator()
menu.addAction(_("Coin is frozen"), lambda: None).setEnabled(False)
menu.addAction(_("Unfreeze Coin"), lambda: self.set_frozen_coins(list(selected.keys()), False))
menu.addSeparator()
needsep = False
else:
menu.addAction(_("Freeze Coin"), lambda: self.set_frozen_coins(list(selected.keys()), True))
if 'a' in frozen_flags:
if needsep: menu.addSeparator()
menu.addAction(_("Address is frozen"), lambda: None).setEnabled(False)
menu.addAction(_("Unfreeze Address"), lambda: self.set_frozen_addresses_for_coins(list(selected.keys()), False))
else:
menu.addAction(_("Freeze Address"), lambda: self.set_frozen_addresses_for_coins(list(selected.keys()), True))
else:
# multi-selection
menu.addSeparator()
if any(['c' not in flags for flags in selected.values()]):
# they have some coin-level non-frozen in the selection, so add the menu action "Freeze coins"
menu.addAction(_("Freeze Coins"), lambda: self.set_frozen_coins(list(selected.keys()), True))
if any(['c' in flags for flags in selected.values()]):
# they have some coin-level frozen in the selection, so add the menu action "Unfreeze coins"
menu.addAction(_("Unfreeze Coins"), lambda: self.set_frozen_coins(list(selected.keys()), False))
if any(['a' not in flags for flags in selected.values()]):
# they have some address-level non-frozen in the selection, so add the menu action "Freeze addresses"
menu.addAction(_("Freeze Addresses"), lambda: self.set_frozen_addresses_for_coins(list(selected.keys()), True))
if any(['a' in flags for flags in selected.values()]):
# they have some address-level frozen in the selection, so add the menu action "Unfreeze addresses"
menu.addAction(_("Unfreeze Addresses"), lambda: self.set_frozen_addresses_for_coins(list(selected.keys()), False))
menu.exec_(self.viewport().mapToGlobal(position))
def on_permit_edit(self, item, column):
# disable editing fields in this tab (labels)
return False
def set_frozen_coins(self, coins, b):
if self.parent:
self.parent.set_frozen_coin_state(coins, b)
def set_frozen_addresses_for_coins(self, coins, b):
if not self.parent: return
addrs = set()
for utxo in self.utxos:
name = self.get_name(utxo)
if name in coins:
addrs.add(utxo['address'])
if addrs:
self.parent.set_frozen_state(list(addrs), b)
| true | true |
f72597acf84a319c1cde2a9c2a80f3c7b7cfc2a4 | 984 | py | Python | src/platform/avr/pmfeatures.py | ArtemovSA/PyMite | a22fbae773b285ccf4993905a46dd396cb762f69 | [
"OLDAP-2.6",
"Python-2.0"
] | 51 | 2015-03-24T07:53:03.000Z | 2021-08-06T12:55:53.000Z | src/platform/avr/pmfeatures.py | ArtemovSA/PyMite | a22fbae773b285ccf4993905a46dd396cb762f69 | [
"OLDAP-2.6",
"Python-2.0"
] | null | null | null | src/platform/avr/pmfeatures.py | ArtemovSA/PyMite | a22fbae773b285ccf4993905a46dd396cb762f69 | [
"OLDAP-2.6",
"Python-2.0"
] | 15 | 2015-04-09T14:17:27.000Z | 2022-01-26T02:42:47.000Z | # This file is Copyright 2010 Dean Hall.
#
# This file is part of the Python-on-a-Chip program.
# Python-on-a-Chip is free software: you can redistribute it and/or modify
# it under the terms of the GNU LESSER GENERAL PUBLIC LICENSE Version 2.1.
#
# Python-on-a-Chip is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# A copy of the GNU LESSER GENERAL PUBLIC LICENSE Version 2.1
# is seen in the file COPYING up one directory from this.
PM_FEATURES = {
"HAVE_PRINT": True,
"HAVE_GC": True,
"HAVE_FLOAT": False,
"HAVE_DEL": True,
"HAVE_IMPORTS": True,
"HAVE_DEFAULTARGS": True,
"HAVE_REPLICATION": True,
"HAVE_CLASSES": False,
"HAVE_ASSERT": False,
"HAVE_GENERATORS": False,
"HAVE_BACKTICK": True,
"HAVE_STRING_FORMAT": True,
"HAVE_CLOSURES": False,
"HAVE_BYTEARRAY": False,
"HAVE_DEBUG_INFO": False,
}
| 32.8 | 74 | 0.707317 |
PM_FEATURES = {
"HAVE_PRINT": True,
"HAVE_GC": True,
"HAVE_FLOAT": False,
"HAVE_DEL": True,
"HAVE_IMPORTS": True,
"HAVE_DEFAULTARGS": True,
"HAVE_REPLICATION": True,
"HAVE_CLASSES": False,
"HAVE_ASSERT": False,
"HAVE_GENERATORS": False,
"HAVE_BACKTICK": True,
"HAVE_STRING_FORMAT": True,
"HAVE_CLOSURES": False,
"HAVE_BYTEARRAY": False,
"HAVE_DEBUG_INFO": False,
}
| true | true |
f7259813b4e802d256d946467bf98359af8d5554 | 2,970 | py | Python | ally/Order/tests/PricingConstruction.py | jpwatt/PyAlly | 463ac0ad22df7dd79456c58ab8da4d378427983c | [
"MIT"
] | 2 | 2021-02-28T22:02:38.000Z | 2021-12-20T19:00:25.000Z | ally/Order/tests/PricingConstruction.py | jpwatt/PyAlly | 463ac0ad22df7dd79456c58ab8da4d378427983c | [
"MIT"
] | null | null | null | ally/Order/tests/PricingConstruction.py | jpwatt/PyAlly | 463ac0ad22df7dd79456c58ab8da4d378427983c | [
"MIT"
] | null | null | null | # MIT License
#
# Copyright (c) 2020 Brett Graves
#
# 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 xml.etree.ElementTree as ET
import unittest
from ..classes import *
from .classes import *
from ..order import Order
class TestPricingConstruction(unittest.TestCase):
def test_market_construction(self):
p = Market()
self.assertEqual(
p.attributes,
{'Typ':'1'},
"Should have type 1"
)
def test_limit_construction(self):
p = Limit(limpx=10.51)
self.assertEqual(
p.attributes,
{'Typ':'2', 'Px':'10.51'},
"Should have type 2, with Px included"
)
p = Limit(limpx=10.51141)
self.assertEqual(
p.attributes,
{'Typ':'2', 'Px':'10.51'},
"Should have type 2, with Px included"
)
def test_stop_construction(self):
p = Stop(stoppx=10.51)
self.assertEqual(
p.attributes,
{ 'Typ':"3", 'StopPx':"10.51"},
"Should have typ 3, with StopPx included"
)
p = Stop(stoppx=10.5112312)
self.assertEqual(
p.attributes,
{ 'Typ':"3", 'StopPx':"10.51"},
"Should have typ 3, with StopPx included"
)
def test_stoplimit_construction(self):
p = StopLimit(limpx = 10.1, stoppx = 10.51)
self.assertEqual(
p.attributes,
{ 'Typ':"4", 'Px': '10.1', 'StopPx':"10.51" },
"Should have typ 3, with StopPx included"
)
def test_trailingstop_construction(self):
p = TrailingStop(
use_pct = True,
offset = 1.12
)
self.assertEqual(
p.attributes,
{ 'Typ':"P"},
"Should have typ P"
)
self.assertEqual(
p.fixml,
{
'PegInstr': {
'OfstTyp': 1,
'PegPxTyp': 1,
'OfstVal': 1.12
}
},
"Should include tag with peginstr"
)
p = TrailingStop(
use_pct = False,
offset = 5.69
)
self.assertEqual(
p.attributes,
{ 'Typ':"P"},
"Should have typ P"
)
self.assertEqual(
p.fixml,
{
'PegInstr': {
'OfstTyp': 0,
'PegPxTyp': 1,
'OfstVal': 5.69
}
},
"Should include tag with peginstr"
)
| 23.385827 | 80 | 0.66835 |
import xml.etree.ElementTree as ET
import unittest
from ..classes import *
from .classes import *
from ..order import Order
class TestPricingConstruction(unittest.TestCase):
def test_market_construction(self):
p = Market()
self.assertEqual(
p.attributes,
{'Typ':'1'},
"Should have type 1"
)
def test_limit_construction(self):
p = Limit(limpx=10.51)
self.assertEqual(
p.attributes,
{'Typ':'2', 'Px':'10.51'},
"Should have type 2, with Px included"
)
p = Limit(limpx=10.51141)
self.assertEqual(
p.attributes,
{'Typ':'2', 'Px':'10.51'},
"Should have type 2, with Px included"
)
def test_stop_construction(self):
p = Stop(stoppx=10.51)
self.assertEqual(
p.attributes,
{ 'Typ':"3", 'StopPx':"10.51"},
"Should have typ 3, with StopPx included"
)
p = Stop(stoppx=10.5112312)
self.assertEqual(
p.attributes,
{ 'Typ':"3", 'StopPx':"10.51"},
"Should have typ 3, with StopPx included"
)
def test_stoplimit_construction(self):
p = StopLimit(limpx = 10.1, stoppx = 10.51)
self.assertEqual(
p.attributes,
{ 'Typ':"4", 'Px': '10.1', 'StopPx':"10.51" },
"Should have typ 3, with StopPx included"
)
def test_trailingstop_construction(self):
p = TrailingStop(
use_pct = True,
offset = 1.12
)
self.assertEqual(
p.attributes,
{ 'Typ':"P"},
"Should have typ P"
)
self.assertEqual(
p.fixml,
{
'PegInstr': {
'OfstTyp': 1,
'PegPxTyp': 1,
'OfstVal': 1.12
}
},
"Should include tag with peginstr"
)
p = TrailingStop(
use_pct = False,
offset = 5.69
)
self.assertEqual(
p.attributes,
{ 'Typ':"P"},
"Should have typ P"
)
self.assertEqual(
p.fixml,
{
'PegInstr': {
'OfstTyp': 0,
'PegPxTyp': 1,
'OfstVal': 5.69
}
},
"Should include tag with peginstr"
)
| true | true |
f72598b7fb1cec8183f6a3b3b326c48b968194b9 | 2,843 | py | Python | starvine/bvcopula/tests/test_freeze_params.py | wgurecky/StarVine | b952a88eeaff476484ba6a26420cfe4ef575d162 | [
"BSD-3-Clause"
] | 12 | 2018-10-04T06:15:13.000Z | 2020-01-08T03:32:30.000Z | starvine/bvcopula/tests/test_freeze_params.py | wgurecky/StarVine | b952a88eeaff476484ba6a26420cfe4ef575d162 | [
"BSD-3-Clause"
] | 25 | 2017-08-29T06:28:37.000Z | 2020-10-16T23:56:57.000Z | starvine/bvcopula/tests/test_freeze_params.py | wgurecky/StarVine | b952a88eeaff476484ba6a26420cfe4ef575d162 | [
"BSD-3-Clause"
] | 3 | 2017-04-08T20:19:09.000Z | 2020-01-09T20:01:02.000Z | ##
# \brief Test ability to determine best fit copula via AIC
from __future__ import print_function, division
from starvine.bvcopula.pc_base import PairCopula
import unittest
import numpy as np
import os
pwd_ = os.getcwd()
dataDir = pwd_ + "/tests/data/"
np.random.seed(123)
class TestGaussFrozen(unittest.TestCase):
@classmethod
def setUpClass(self):
np.random.seed(123)
def testGaussFrozen(self):
# Load matlab data set
stocks = np.loadtxt(dataDir + 'stocks.csv', delimiter=',')
x = stocks[:, 0]
y = stocks[:, 1]
stockModel = PairCopula(x, y)
# Try to fit all copula
stockModel.copulaTournament(verbosity=0)
# Ensure that the gaussian copula was chosen as the best fit
self.assertTrue(stockModel.copulaModel.name == "gauss")
self.assertTrue(stockModel.copulaParams[0] == "gauss")
# Check gaussian copula parameters for correctness
self.assertAlmostEqual(stockModel.copulaParams[1][0], 0.73874003, 4)
# Eval the frozen model
frzU, frzV = stockModel.copulaModel.sample(40000)
# Eval a model with specified params
setU, setV = stockModel.copulaModel.sample(40000, (0.73874003,))
# Ensure both frozen model and specified param model produce same result
frzModel = PairCopula(frzU, frzV)
setModel = PairCopula(setU, setV)
frzKtau, fp = frzModel.empKTau()
setKtau, sp = setModel.empKTau()
self.assertAlmostEqual(frzKtau, setKtau, delta=0.02)
self.assertAlmostEqual(fp, sp, delta=0.02)
# Eval a model with different specified params
setU2, setV2 = stockModel.copulaModel.sample(20000, (0.3,))
setModel2 = PairCopula(setU2, setV2)
setKtau2, sp2 = setModel2.empKTau()
self.assertTrue(setKtau2 != setKtau)
self.assertTrue(abs(setKtau2 - setKtau) > 0.2)
def testFrankFrozen(self):
np.random.seed(123)
# Load matlab data set
stocks = np.loadtxt(dataDir + 'stocks.csv', delimiter=',')
x = stocks[:, 0]
y = stocks[:, 1]
stockModel = PairCopula(x, y, family={'frank': 0, })
# Try to fit all copula
stockModel.copulaTournament(verbosity=0)
# Eval the frozen model
frzU, frzV = stockModel.copulaModel.sample(40000)
# Eval a model with specified params
setU, setV = stockModel.copulaModel.sample(40000, *stockModel.copulaParams[1])
# Ensure both frozen model and specified param model produce same result
frzModel = PairCopula(frzU, frzV)
setModel = PairCopula(setU, setV)
frzKtau, fp = frzModel.empKTau()
setKtau, sp = setModel.empKTau()
self.assertAlmostEqual(frzKtau, setKtau, delta=0.02)
self.assertAlmostEqual(fp, sp, delta=0.02)
| 35.098765 | 86 | 0.651425 |
from __future__ import print_function, division
from starvine.bvcopula.pc_base import PairCopula
import unittest
import numpy as np
import os
pwd_ = os.getcwd()
dataDir = pwd_ + "/tests/data/"
np.random.seed(123)
class TestGaussFrozen(unittest.TestCase):
@classmethod
def setUpClass(self):
np.random.seed(123)
def testGaussFrozen(self):
stocks = np.loadtxt(dataDir + 'stocks.csv', delimiter=',')
x = stocks[:, 0]
y = stocks[:, 1]
stockModel = PairCopula(x, y)
stockModel.copulaTournament(verbosity=0)
self.assertTrue(stockModel.copulaModel.name == "gauss")
self.assertTrue(stockModel.copulaParams[0] == "gauss")
self.assertAlmostEqual(stockModel.copulaParams[1][0], 0.73874003, 4)
frzU, frzV = stockModel.copulaModel.sample(40000)
setU, setV = stockModel.copulaModel.sample(40000, (0.73874003,))
frzModel = PairCopula(frzU, frzV)
setModel = PairCopula(setU, setV)
frzKtau, fp = frzModel.empKTau()
setKtau, sp = setModel.empKTau()
self.assertAlmostEqual(frzKtau, setKtau, delta=0.02)
self.assertAlmostEqual(fp, sp, delta=0.02)
setU2, setV2 = stockModel.copulaModel.sample(20000, (0.3,))
setModel2 = PairCopula(setU2, setV2)
setKtau2, sp2 = setModel2.empKTau()
self.assertTrue(setKtau2 != setKtau)
self.assertTrue(abs(setKtau2 - setKtau) > 0.2)
def testFrankFrozen(self):
np.random.seed(123)
stocks = np.loadtxt(dataDir + 'stocks.csv', delimiter=',')
x = stocks[:, 0]
y = stocks[:, 1]
stockModel = PairCopula(x, y, family={'frank': 0, })
stockModel.copulaTournament(verbosity=0)
frzU, frzV = stockModel.copulaModel.sample(40000)
setU, setV = stockModel.copulaModel.sample(40000, *stockModel.copulaParams[1])
frzModel = PairCopula(frzU, frzV)
setModel = PairCopula(setU, setV)
frzKtau, fp = frzModel.empKTau()
setKtau, sp = setModel.empKTau()
self.assertAlmostEqual(frzKtau, setKtau, delta=0.02)
self.assertAlmostEqual(fp, sp, delta=0.02)
| true | true |
f7259984a8976fd2deb189a6e90d3aaeabfca3d9 | 1,402 | py | Python | source/appModules/totalcmd.py | oleguldberg/nvda | 05f55ff146ef8ba481a2de4f1bcf187200474cea | [
"bzip2-1.0.6"
] | 1,592 | 2015-11-10T12:05:44.000Z | 2022-03-31T11:50:40.000Z | source/appModules/totalcmd.py | oleguldberg/nvda | 05f55ff146ef8ba481a2de4f1bcf187200474cea | [
"bzip2-1.0.6"
] | 9,479 | 2015-11-10T20:56:48.000Z | 2022-03-31T23:51:30.000Z | source/appModules/totalcmd.py | oleguldberg/nvda | 05f55ff146ef8ba481a2de4f1bcf187200474cea | [
"bzip2-1.0.6"
] | 682 | 2015-11-10T11:19:23.000Z | 2022-03-31T07:51:29.000Z | #appModules/totalcmd.py
#A part of NonVisual Desktop Access (NVDA)
#Copyright (C) 2006-2012 NVDA Contributors
#This file is covered by the GNU General Public License.
#See the file COPYING for more details.
import appModuleHandler
from NVDAObjects.IAccessible import IAccessible
import speech
import controlTypes
import ui
oldActivePannel=0
class AppModule(appModuleHandler.AppModule):
def chooseNVDAObjectOverlayClasses(self, obj, clsList):
if obj.windowClassName in ("TMyListBox", "TMyListBox.UnicodeClass"):
clsList.insert(0, TCList)
class TCList(IAccessible):
def event_gainFocus(self):
global oldActivePannel
if oldActivePannel !=self.windowControlID:
oldActivePannel=self.windowControlID
obj=self
while obj and obj.parent and obj.parent.windowClassName!="TTOTAL_CMD":
obj=obj.parent
counter=0
while obj and obj.previous and obj.windowClassName!="TPanel":
obj=obj.previous
if obj.windowClassName!="TDrivePanel":
counter+=1
if counter==2:
ui.message(_("left"))
else:
ui.message(_("right"))
super(TCList,self).event_gainFocus()
def reportFocus(self):
if self.name:
speakList=[]
if controlTypes.State.SELECTED in self.states:
speakList.append(controlTypes.State.SELECTED.displayString)
speakList.append(self.name.split("\\")[-1])
speech.speakMessage(" ".join(speakList))
else:
super(TCList,self).reportFocus()
| 28.04 | 73 | 0.752496 |
import appModuleHandler
from NVDAObjects.IAccessible import IAccessible
import speech
import controlTypes
import ui
oldActivePannel=0
class AppModule(appModuleHandler.AppModule):
def chooseNVDAObjectOverlayClasses(self, obj, clsList):
if obj.windowClassName in ("TMyListBox", "TMyListBox.UnicodeClass"):
clsList.insert(0, TCList)
class TCList(IAccessible):
def event_gainFocus(self):
global oldActivePannel
if oldActivePannel !=self.windowControlID:
oldActivePannel=self.windowControlID
obj=self
while obj and obj.parent and obj.parent.windowClassName!="TTOTAL_CMD":
obj=obj.parent
counter=0
while obj and obj.previous and obj.windowClassName!="TPanel":
obj=obj.previous
if obj.windowClassName!="TDrivePanel":
counter+=1
if counter==2:
ui.message(_("left"))
else:
ui.message(_("right"))
super(TCList,self).event_gainFocus()
def reportFocus(self):
if self.name:
speakList=[]
if controlTypes.State.SELECTED in self.states:
speakList.append(controlTypes.State.SELECTED.displayString)
speakList.append(self.name.split("\\")[-1])
speech.speakMessage(" ".join(speakList))
else:
super(TCList,self).reportFocus()
| true | true |
f7259a6ab0d551d886b30961db6d5aff508bdecb | 772 | py | Python | scripts/plotting/reprocessed_kccg_samples_pca/main.py | populationgenomics/ancestry | faf6fd4bc3a1f8b2a2adb7e59cf584d4bfdf79e6 | [
"MIT"
] | null | null | null | scripts/plotting/reprocessed_kccg_samples_pca/main.py | populationgenomics/ancestry | faf6fd4bc3a1f8b2a2adb7e59cf584d4bfdf79e6 | [
"MIT"
] | 21 | 2021-03-09T06:35:59.000Z | 2022-02-21T22:56:15.000Z | scripts/plotting/reprocessed_kccg_samples_pca/main.py | populationgenomics/ancestry | faf6fd4bc3a1f8b2a2adb7e59cf584d4bfdf79e6 | [
"MIT"
] | null | null | null | """Entry point for the analysis runner."""
import os
import sys
import hail as hl
import hailtop.batch as hb
from analysis_runner import dataproc
OUTPUT = os.getenv('OUTPUT')
assert OUTPUT
hl.init(default_reference='GRCh38')
POP = sys.argv[1] if len(sys.argv) > 1 else 'nfe'
service_backend = hb.ServiceBackend(
billing_project=os.getenv('HAIL_BILLING_PROJECT'), bucket=os.getenv('HAIL_BUCKET')
)
batch = hb.Batch(name=f'{POP} kccg-reprocessed', backend=service_backend)
dataproc.hail_dataproc_job(
batch,
f'project_reprocessed_kccg_samples.py --output={OUTPUT} --pop {POP}',
max_age='5h',
packages=['click', 'selenium'],
init=['gs://cpg-reference/hail_dataproc/install_phantomjs.sh'],
job_name=f'{POP}-kccg-reprocessed',
)
batch.run()
| 24.125 | 86 | 0.727979 |
import os
import sys
import hail as hl
import hailtop.batch as hb
from analysis_runner import dataproc
OUTPUT = os.getenv('OUTPUT')
assert OUTPUT
hl.init(default_reference='GRCh38')
POP = sys.argv[1] if len(sys.argv) > 1 else 'nfe'
service_backend = hb.ServiceBackend(
billing_project=os.getenv('HAIL_BILLING_PROJECT'), bucket=os.getenv('HAIL_BUCKET')
)
batch = hb.Batch(name=f'{POP} kccg-reprocessed', backend=service_backend)
dataproc.hail_dataproc_job(
batch,
f'project_reprocessed_kccg_samples.py --output={OUTPUT} --pop {POP}',
max_age='5h',
packages=['click', 'selenium'],
init=['gs://cpg-reference/hail_dataproc/install_phantomjs.sh'],
job_name=f'{POP}-kccg-reprocessed',
)
batch.run()
| true | true |
f7259b305c1ad5cae4a9beae16737854d8e966c3 | 7,804 | py | Python | botstory/ast/story_context/__init__.py | botstory/bot-story | 9c5b2fc7f7a14dbd467d70f60d5ba855ef89dac3 | [
"MIT"
] | 5 | 2017-01-14T13:42:13.000Z | 2021-07-27T21:52:04.000Z | botstory/ast/story_context/__init__.py | botstory/bot-story | 9c5b2fc7f7a14dbd467d70f60d5ba855ef89dac3 | [
"MIT"
] | 235 | 2016-11-07T23:33:28.000Z | 2018-03-13T11:27:33.000Z | botstory/ast/story_context/__init__.py | hyzhak/bot-story | 9c5b2fc7f7a14dbd467d70f60d5ba855ef89dac3 | [
"MIT"
] | 5 | 2017-01-14T13:42:14.000Z | 2020-11-06T08:33:20.000Z | from botstory import matchers, utils
from botstory.ast import callable, forking, loop
from botstory.ast.story_context import reducers
from botstory.utils import advanced_json_encoder
import numbers
import logging
import uuid
logger = logging.getLogger(__name__)
class MissedStoryPart(Exception):
pass
class StoryContext:
def __init__(self, message, library, matched=False, waiting_for=None, parent_uid=None):
self.uid = str(uuid.uuid4())
self.parent_uid = parent_uid
self.library = library
# whether message was passed validation and was matched one story
self.matched = matched
self.message = message
self.waiting_for = waiting_for
def clone(self):
return StoryContext(library=self.library,
matched=self.matched,
message=self.message,
parent_uid=self.uid,
waiting_for=self.waiting_for,
)
def compiled_story(self):
if self.is_empty_stack():
return self.library.get_global_story(self.message)
else:
return self.library.get_story_by_topic(self.stack_tail()['topic'], stack=self.stack()[:-1])
def could_scope_out(self):
"""
could bubble up from current scope
:return:
"""
return not self.waiting_for or \
isinstance(self.waiting_for, callable.EndOfStory) or \
self.is_breaking_a_loop()
def current_step(self):
return self.stack_tail()['step']
def does_it_match_any_story(self):
return self.compiled_story() is not None and not self.matched
def get_child_story(self):
logger.debug('# get_child_story')
"""
try child story that match message and get scope of it
:return:
"""
story_loop = self.compiled_story()
if hasattr(story_loop, 'children_matcher') and not self.matched:
return self.get_story_scope_child(story_loop)
story_part = self.get_current_story_part()
if not hasattr(story_part, 'get_child_by_validation_result'):
logger.debug('# does not have get_child_by_validation_result')
return None
if isinstance(self.waiting_for, forking.SwitchOnValue):
logger.debug('# switch on value')
return story_part.get_child_by_validation_result(self.waiting_for.value)
# for some base classes we could try validate result direct
child_story = story_part.get_child_by_validation_result(self.waiting_for)
if child_story:
logger.debug('# child_story')
logger.debug(child_story)
return child_story
stack_tail = self.stack_tail()
if stack_tail['data'] is not None and not self.matched:
validator = matchers.deserialize(stack_tail['data'])
logger.debug('# validator')
logger.debug(validator)
logger.debug('# self.message')
logger.debug(self.message)
validation_result = validator.validate(self.message)
logger.debug('# validation_result')
logger.debug(validation_result)
res = story_part.get_child_by_validation_result(validation_result)
logger.debug('# res')
logger.debug(res)
# or we validate message
# but can't find right child story
# maybe we should use independent validators for each story here
if res is None:
return self.get_story_scope_child(story_part)
else:
return res
return None
def get_story_scope_child(self, story_part):
logger.debug('# get_story_scope_child')
validator = story_part.children_matcher()
logger.debug('# validator')
logger.debug(validator)
logger.debug('# self.message')
logger.debug(self.message)
topic = validator.validate(self.message)
# if topic == None:
# we inside story loop scope
# but got message that doesn't match
# any local stories
logger.debug('# topic {}'.format(topic))
return story_part.by_topic(topic)
def get_current_story_part(self):
compiled_story = self.compiled_story()
if not compiled_story:
return None
try:
return compiled_story.story_line[self.current_step()]
except IndexError:
return None
def get_user_data(self):
return get_user_data(self.message)
def has_child_story(self):
return self.get_child_story() is not None
def is_breaking_a_loop(self):
return isinstance(self.waiting_for, loop.BreakLoop)
def is_empty_stack(self):
return len(self.stack()) == 0
def is_end_of_story(self):
compiled_story = self.compiled_story()
if not compiled_story:
return True
return self.current_step() >= len(compiled_story.story_line)
def is_scope_level(self):
return isinstance(self.compiled_story(), loop.StoriesLoopNode)
def is_scope_level_part(self):
return isinstance(self.get_current_story_part(), loop.StoriesLoopNode)
def is_tail_of_story(self):
compiled_story = self.compiled_story()
if not compiled_story:
return True
return self.current_step() >= len(compiled_story.story_line) - 1
def is_waiting_for_input(self):
"""
could make one step further
:return:
"""
return self.waiting_for and \
not isinstance(self.waiting_for, forking.SwitchOnValue) and \
not is_base_type(self.waiting_for)
def stack(self):
return self.message['session']['stack']
def stack_tail(self):
stack = self.stack()
if len(stack) == 0:
raise MissedStoryPart()
return stack[-1]
def to_json(self):
return {
'uid': self.uid,
'parent_uid': self.parent_uid,
'matched': self.matched,
'message': self.message,
'waiting_for': str(self.waiting_for),
}
def user(self):
return self.message['user']
def __repr__(self):
try:
return advanced_json_encoder.AdvancedJSONEncoder().encode(self.to_json())
except Exception as err:
logger.warn(err)
logger.warn('fail to dump json of message {} '
'waiting for {}'.format(str(self.message), str(self.waiting_for)))
def is_base_type(value):
return isinstance(value, str) or isinstance(value, numbers.Number)
def clean_message_data(ctx):
return utils.safe_set(ctx, 'session', 'data', 'message', {})
def get_user_data(ctx):
return ctx['session']['data']
def get_message_data(ctx, *args, **kwargs):
return utils.safe_get(get_user_data(ctx)['message'], *args, **kwargs)
def get_message_attachment(ctx, attachment_type):
logger.debug('# get_message_data(ctx)')
logger.debug(get_message_data(ctx))
attachment_list = get_message_data(ctx, 'attachments')
if attachment_list is None or len(attachment_list) == 0:
return None
attachment_list_of_type = [a for a in attachment_list if a['type'] == attachment_type]
if attachment_list_of_type is None or len(attachment_list_of_type) == 0:
return None
return attachment_list_of_type[0]
def set_user_data(ctx, data):
ctx['session']['data'] = {
**get_user_data(ctx),
**data,
}
return ctx
def set_message_data(ctx, *args):
return utils.safe_set(ctx, 'session', 'data', 'message', *args)
__all__ = [get_user_data, reducers, set_user_data, StoryContext]
| 32.381743 | 103 | 0.631471 | from botstory import matchers, utils
from botstory.ast import callable, forking, loop
from botstory.ast.story_context import reducers
from botstory.utils import advanced_json_encoder
import numbers
import logging
import uuid
logger = logging.getLogger(__name__)
class MissedStoryPart(Exception):
pass
class StoryContext:
def __init__(self, message, library, matched=False, waiting_for=None, parent_uid=None):
self.uid = str(uuid.uuid4())
self.parent_uid = parent_uid
self.library = library
self.matched = matched
self.message = message
self.waiting_for = waiting_for
def clone(self):
return StoryContext(library=self.library,
matched=self.matched,
message=self.message,
parent_uid=self.uid,
waiting_for=self.waiting_for,
)
def compiled_story(self):
if self.is_empty_stack():
return self.library.get_global_story(self.message)
else:
return self.library.get_story_by_topic(self.stack_tail()['topic'], stack=self.stack()[:-1])
def could_scope_out(self):
return not self.waiting_for or \
isinstance(self.waiting_for, callable.EndOfStory) or \
self.is_breaking_a_loop()
def current_step(self):
return self.stack_tail()['step']
def does_it_match_any_story(self):
return self.compiled_story() is not None and not self.matched
def get_child_story(self):
logger.debug('# get_child_story')
story_loop = self.compiled_story()
if hasattr(story_loop, 'children_matcher') and not self.matched:
return self.get_story_scope_child(story_loop)
story_part = self.get_current_story_part()
if not hasattr(story_part, 'get_child_by_validation_result'):
logger.debug('# does not have get_child_by_validation_result')
return None
if isinstance(self.waiting_for, forking.SwitchOnValue):
logger.debug('# switch on value')
return story_part.get_child_by_validation_result(self.waiting_for.value)
child_story = story_part.get_child_by_validation_result(self.waiting_for)
if child_story:
logger.debug('# child_story')
logger.debug(child_story)
return child_story
stack_tail = self.stack_tail()
if stack_tail['data'] is not None and not self.matched:
validator = matchers.deserialize(stack_tail['data'])
logger.debug('# validator')
logger.debug(validator)
logger.debug('# self.message')
logger.debug(self.message)
validation_result = validator.validate(self.message)
logger.debug('# validation_result')
logger.debug(validation_result)
res = story_part.get_child_by_validation_result(validation_result)
logger.debug('# res')
logger.debug(res)
# maybe we should use independent validators for each story here
if res is None:
return self.get_story_scope_child(story_part)
else:
return res
return None
def get_story_scope_child(self, story_part):
logger.debug('
validator = story_part.children_matcher()
logger.debug('
logger.debug(validator)
logger.debug('
logger.debug(self.message)
topic = validator.validate(self.message)
# if topic == None:
# we inside story loop scope
# but got message that doesn't match
logger.debug('# topic {}'.format(topic))
return story_part.by_topic(topic)
def get_current_story_part(self):
compiled_story = self.compiled_story()
if not compiled_story:
return None
try:
return compiled_story.story_line[self.current_step()]
except IndexError:
return None
def get_user_data(self):
return get_user_data(self.message)
def has_child_story(self):
return self.get_child_story() is not None
def is_breaking_a_loop(self):
return isinstance(self.waiting_for, loop.BreakLoop)
def is_empty_stack(self):
return len(self.stack()) == 0
def is_end_of_story(self):
compiled_story = self.compiled_story()
if not compiled_story:
return True
return self.current_step() >= len(compiled_story.story_line)
def is_scope_level(self):
return isinstance(self.compiled_story(), loop.StoriesLoopNode)
def is_scope_level_part(self):
return isinstance(self.get_current_story_part(), loop.StoriesLoopNode)
def is_tail_of_story(self):
compiled_story = self.compiled_story()
if not compiled_story:
return True
return self.current_step() >= len(compiled_story.story_line) - 1
def is_waiting_for_input(self):
return self.waiting_for and \
not isinstance(self.waiting_for, forking.SwitchOnValue) and \
not is_base_type(self.waiting_for)
def stack(self):
return self.message['session']['stack']
def stack_tail(self):
stack = self.stack()
if len(stack) == 0:
raise MissedStoryPart()
return stack[-1]
def to_json(self):
return {
'uid': self.uid,
'parent_uid': self.parent_uid,
'matched': self.matched,
'message': self.message,
'waiting_for': str(self.waiting_for),
}
def user(self):
return self.message['user']
def __repr__(self):
try:
return advanced_json_encoder.AdvancedJSONEncoder().encode(self.to_json())
except Exception as err:
logger.warn(err)
logger.warn('fail to dump json of message {} '
'waiting for {}'.format(str(self.message), str(self.waiting_for)))
def is_base_type(value):
return isinstance(value, str) or isinstance(value, numbers.Number)
def clean_message_data(ctx):
return utils.safe_set(ctx, 'session', 'data', 'message', {})
def get_user_data(ctx):
return ctx['session']['data']
def get_message_data(ctx, *args, **kwargs):
return utils.safe_get(get_user_data(ctx)['message'], *args, **kwargs)
def get_message_attachment(ctx, attachment_type):
logger.debug('# get_message_data(ctx)')
logger.debug(get_message_data(ctx))
attachment_list = get_message_data(ctx, 'attachments')
if attachment_list is None or len(attachment_list) == 0:
return None
attachment_list_of_type = [a for a in attachment_list if a['type'] == attachment_type]
if attachment_list_of_type is None or len(attachment_list_of_type) == 0:
return None
return attachment_list_of_type[0]
def set_user_data(ctx, data):
ctx['session']['data'] = {
**get_user_data(ctx),
**data,
}
return ctx
def set_message_data(ctx, *args):
return utils.safe_set(ctx, 'session', 'data', 'message', *args)
__all__ = [get_user_data, reducers, set_user_data, StoryContext]
| true | true |
f7259b4e1f74cc2b78fa2fe6a806234b94c86cc1 | 6,467 | py | Python | scd_manager.py | gavinlive/scd-data-manager | 570b67bacb4bf17f0b49c5875933f233ddd76e6c | [
"MIT"
] | null | null | null | scd_manager.py | gavinlive/scd-data-manager | 570b67bacb4bf17f0b49c5875933f233ddd76e6c | [
"MIT"
] | 1 | 2020-06-09T08:48:01.000Z | 2020-06-09T09:23:07.000Z | scd_manager.py | gavinlive/scd-data-manager | 570b67bacb4bf17f0b49c5875933f233ddd76e6c | [
"MIT"
] | null | null | null | import os, sys
import pydicom as pyd
import matplotlib.pyplot as plt
import collections
import pandas as pd
PatientRecord = collections.namedtuple('PatientRecord', ['patient_id', 'image_folder', 'original_id', 'gender', 'age', 'pathology', 'all_scans', 'scans', 'scans_list', 'scans_total'])
PatientScans = collections.namedtuple('PatientScans', ['files', 'prefixes', 'suffixes', 'total_number'])
DataRecord = collections.namedtuple('DataRecord', ['dicom_path', 'contour_path'])
class DataManager(object):
def __config__(self):
self.path = '.'
self.patient_info_path = self.path + '/scd_patientdata.csv'
self.meta_data_path = self.path + '/scd_patientdata.csv'
self.images_path = self.path + '/SCD_DeidentifiedImages/'
self.segmentations_path = self.path + '/SCD_ManualContours/'
def __init__(self):
self.__config__()
self.__load_patient_info()
self._patients = next(os.walk(self.images_path))[1]
print(self._patients)
self.__get_patient_images()
if(self.__verify_scan_sets()==True): print("verified all scan sets")
if(self.__verify_scan_numbers()==True): print("verified all scan numbers")
def __import_contours(self, patient, suffixes, files):
path = self.segmentations_path + patient + '/contours-manual/IRCCI-expert/'
records_list=[]
numbers_list = []
for file in os.listdir(path):
if file.endswith(".txt"):
f=file.split("-")
scan_number = int(f[2])
f=f[3]
if((f=="icontour") and (scan_number in suffixes)):
contour_filepath = path +'/' + file
indx = suffixes.index(scan_number)
dicom_filepath = files[indx]
records_list.append(DataRecord(dicom_filepath, contour_filepath))
numbers_list.append(scan_number)
return records_list,numbers_list,len(records_list)
def __load_patient_info(self):
self.patient_info = pd.read_csv(self.patient_info_path)
def __get_patient_info(self, patient):
this = self.patient_info[self.patient_info['OriginalID']==patient].values.tolist()[0]
print(this)
cols = self.patient_info.columns.values.tolist()
toget = ['Gender', 'Age', 'PathologyID', 'Pathology']
toreturn=[]
for t in toget:
indx = cols.index(t)
toreturn.append(this[indx])
return toreturn
def depre__import_contours(self):
for patient in self._patients:
path = self.segmentations_path + patient + '/contours-manual/IRCCI-expert/'
records_list=[]
numbers_list = []
for file in os.listdir(path):
if file.endswith(".txt"):
f=file.split("-")
scan_number = f[2]
f=f[3]
if((f=="icontour") and (scan_number in self.patient[patient].all_scans.suffixes)):
contour_filepath = path +'/' + file
indx = self.patient[patient].all_scans.suffixes.indx(scan_number)
dicom_filepath = self.patient[patient].all_scans.files[indx]
records_list.append(DataRecord(dicom_filepath, contour_filepath))
numbers_list.append(scan_number)
self.patient[patient].scans=records_list
self.patient[patient].scans_list=numbers_list
self.patient[patient].scans_total = len(records_list)
def __verify_scan_sets(self):
for patient in self._patients:
prefix_list = self.patient[patient].all_scans.prefixes
b = [(x==1) for x in prefix_list]
if False in b:
print('Fault for patient: %s' % patient)
return False
return True
def __verify_scan_numbers(self):
for patient in self._patients:
prefix_list = self.patient[patient].all_scans.suffixes
b = [(prefix_list.count(x)==1) for x in prefix_list]
if False in b:
print('Fault for patient: %s' % patient)
return False
return True
def __get_patient_images(self):
self.patient = {}
for patient in self._patients:
#list_of_image_folders_for_patient = next(os.walk(self.images_path + patient))[1]
#list_of_image_folders_for_patient_full_path = [self.images_path + patient + '/' + x for x in list_of_image_folders_for_patient]
#self.patient[patient] = {"patient_id": patient, "images": list_of_image_folders_for_patient, "original_id": "",
#"gender": "", "age": 0, "pathology": "" }
def get_files(list_of_folders):
file_list = []
for folder in list_of_folders:
for file in os.listdir(folder):
if file.endswith(".dcm"):
file_list.append(folder +'/' + file)
return file_list
def get_prefix_suffix(files):
prefixes = []
suffixes = []
for file in files:
f=file.split("-")
f=f[-2::]
f[1] = f[1].split(".")[0]
prefixes.append(int(f[0]))
suffixes.append(int(f[1]))
return prefixes, suffixes
files = get_files([self.images_path+patient+'/DICOM'])
prefixes, suffixes = get_prefix_suffix(files)
this_patient_scan_set = PatientScans(files, prefixes, suffixes, len(files))
scans, scans_list, scans_total = self.__import_contours(patient, suffixes, files)
gender, age, pathologyID, _ = self.__get_patient_info(patient)
this_patient_record = PatientRecord(patient_id=patient, image_folder=self.images_path + patient, original_id="",
gender=gender, age=age, pathology=pathologyID, all_scans=this_patient_scan_set, scans=scans, scans_list=scans_list, scans_total=scans_total)
self.patient[patient] = this_patient_record
def total_examples(self):
count=0
for patient in self._patients:
count += self.patient[patient].scans_total
return count
def __call__(self, patient,scan_number):
return self.patient[patient].all_scans.files[scan_number]
| 44.6 | 183 | 0.598113 | import os, sys
import pydicom as pyd
import matplotlib.pyplot as plt
import collections
import pandas as pd
PatientRecord = collections.namedtuple('PatientRecord', ['patient_id', 'image_folder', 'original_id', 'gender', 'age', 'pathology', 'all_scans', 'scans', 'scans_list', 'scans_total'])
PatientScans = collections.namedtuple('PatientScans', ['files', 'prefixes', 'suffixes', 'total_number'])
DataRecord = collections.namedtuple('DataRecord', ['dicom_path', 'contour_path'])
class DataManager(object):
def __config__(self):
self.path = '.'
self.patient_info_path = self.path + '/scd_patientdata.csv'
self.meta_data_path = self.path + '/scd_patientdata.csv'
self.images_path = self.path + '/SCD_DeidentifiedImages/'
self.segmentations_path = self.path + '/SCD_ManualContours/'
def __init__(self):
self.__config__()
self.__load_patient_info()
self._patients = next(os.walk(self.images_path))[1]
print(self._patients)
self.__get_patient_images()
if(self.__verify_scan_sets()==True): print("verified all scan sets")
if(self.__verify_scan_numbers()==True): print("verified all scan numbers")
def __import_contours(self, patient, suffixes, files):
path = self.segmentations_path + patient + '/contours-manual/IRCCI-expert/'
records_list=[]
numbers_list = []
for file in os.listdir(path):
if file.endswith(".txt"):
f=file.split("-")
scan_number = int(f[2])
f=f[3]
if((f=="icontour") and (scan_number in suffixes)):
contour_filepath = path +'/' + file
indx = suffixes.index(scan_number)
dicom_filepath = files[indx]
records_list.append(DataRecord(dicom_filepath, contour_filepath))
numbers_list.append(scan_number)
return records_list,numbers_list,len(records_list)
def __load_patient_info(self):
self.patient_info = pd.read_csv(self.patient_info_path)
def __get_patient_info(self, patient):
this = self.patient_info[self.patient_info['OriginalID']==patient].values.tolist()[0]
print(this)
cols = self.patient_info.columns.values.tolist()
toget = ['Gender', 'Age', 'PathologyID', 'Pathology']
toreturn=[]
for t in toget:
indx = cols.index(t)
toreturn.append(this[indx])
return toreturn
def depre__import_contours(self):
for patient in self._patients:
path = self.segmentations_path + patient + '/contours-manual/IRCCI-expert/'
records_list=[]
numbers_list = []
for file in os.listdir(path):
if file.endswith(".txt"):
f=file.split("-")
scan_number = f[2]
f=f[3]
if((f=="icontour") and (scan_number in self.patient[patient].all_scans.suffixes)):
contour_filepath = path +'/' + file
indx = self.patient[patient].all_scans.suffixes.indx(scan_number)
dicom_filepath = self.patient[patient].all_scans.files[indx]
records_list.append(DataRecord(dicom_filepath, contour_filepath))
numbers_list.append(scan_number)
self.patient[patient].scans=records_list
self.patient[patient].scans_list=numbers_list
self.patient[patient].scans_total = len(records_list)
def __verify_scan_sets(self):
for patient in self._patients:
prefix_list = self.patient[patient].all_scans.prefixes
b = [(x==1) for x in prefix_list]
if False in b:
print('Fault for patient: %s' % patient)
return False
return True
def __verify_scan_numbers(self):
for patient in self._patients:
prefix_list = self.patient[patient].all_scans.suffixes
b = [(prefix_list.count(x)==1) for x in prefix_list]
if False in b:
print('Fault for patient: %s' % patient)
return False
return True
def __get_patient_images(self):
self.patient = {}
for patient in self._patients:
def get_files(list_of_folders):
file_list = []
for folder in list_of_folders:
for file in os.listdir(folder):
if file.endswith(".dcm"):
file_list.append(folder +'/' + file)
return file_list
def get_prefix_suffix(files):
prefixes = []
suffixes = []
for file in files:
f=file.split("-")
f=f[-2::]
f[1] = f[1].split(".")[0]
prefixes.append(int(f[0]))
suffixes.append(int(f[1]))
return prefixes, suffixes
files = get_files([self.images_path+patient+'/DICOM'])
prefixes, suffixes = get_prefix_suffix(files)
this_patient_scan_set = PatientScans(files, prefixes, suffixes, len(files))
scans, scans_list, scans_total = self.__import_contours(patient, suffixes, files)
gender, age, pathologyID, _ = self.__get_patient_info(patient)
this_patient_record = PatientRecord(patient_id=patient, image_folder=self.images_path + patient, original_id="",
gender=gender, age=age, pathology=pathologyID, all_scans=this_patient_scan_set, scans=scans, scans_list=scans_list, scans_total=scans_total)
self.patient[patient] = this_patient_record
def total_examples(self):
count=0
for patient in self._patients:
count += self.patient[patient].scans_total
return count
def __call__(self, patient,scan_number):
return self.patient[patient].all_scans.files[scan_number]
| true | true |
f7259c70b3269cebc23b86955129c52126b5c426 | 587 | py | Python | Python1/python1Homework/input_counter1.py | ceeblet/OST_PythonCertificationTrack | 042e0ce964bc88b3f4132dcbd7e06c5f504eae34 | [
"MIT"
] | null | null | null | Python1/python1Homework/input_counter1.py | ceeblet/OST_PythonCertificationTrack | 042e0ce964bc88b3f4132dcbd7e06c5f504eae34 | [
"MIT"
] | null | null | null | Python1/python1Homework/input_counter1.py | ceeblet/OST_PythonCertificationTrack | 042e0ce964bc88b3f4132dcbd7e06c5f504eae34 | [
"MIT"
] | null | null | null | #!/usr/local/bin/python3
"""input_counter.py"""
myset = set()
mydict = {}
mysetlength = len(myset)
while True:
text = input("Enter a line (or Enter to quit): ")
if not text:
break
for punc in ",?;.":
text = text.replace(punc, "")
textwords = (text.lower().split())
for word in textwords:
myset.add(word)
newsetlength = len(myset)
if newsetlength > mysetlength:
mydict[word] = newsetlength
mysetlength = newsetlength
for word in (mydict.keys()):
print(word, mydict[word])
print("Finished") | 26.681818 | 53 | 0.584327 |
myset = set()
mydict = {}
mysetlength = len(myset)
while True:
text = input("Enter a line (or Enter to quit): ")
if not text:
break
for punc in ",?;.":
text = text.replace(punc, "")
textwords = (text.lower().split())
for word in textwords:
myset.add(word)
newsetlength = len(myset)
if newsetlength > mysetlength:
mydict[word] = newsetlength
mysetlength = newsetlength
for word in (mydict.keys()):
print(word, mydict[word])
print("Finished") | true | true |
f7259ef31d09ee215158684c34454fabb4e5926d | 614 | py | Python | aws-auth0-auth/helloWorld.py | skarlekar/ms-auth-tutorials | 0de172817e54533be93700de19028cfa8757861f | [
"MIT"
] | null | null | null | aws-auth0-auth/helloWorld.py | skarlekar/ms-auth-tutorials | 0de172817e54533be93700de19028cfa8757861f | [
"MIT"
] | 1 | 2021-06-01T21:41:36.000Z | 2021-06-01T21:41:36.000Z | aws-auth0-auth/helloWorld.py | skarlekar/ms-auth-tutorials | 0de172817e54533be93700de19028cfa8757861f | [
"MIT"
] | 1 | 2017-10-26T15:08:40.000Z | 2017-10-26T15:08:40.000Z | """Simple helloWorld service."""
import json
def sayHello(event, context):
"""Return a message in the response body."""
print('Event is: {}'.format(json.dumps(event)))
body = {
"message": "Hello! Your Auth0 authorized function executed successfully!"
}
response = {
"statusCode": 200,
"body": json.dumps(body)
}
return response
# Use this code if you don't use the http event with the LAMBDA-PROXY
# integration
"""
return {
"message": "Go Serverless v1.0! Your function executed successfully!",
"event": event
}
"""
| 22.740741 | 81 | 0.599349 | import json
def sayHello(event, context):
print('Event is: {}'.format(json.dumps(event)))
body = {
"message": "Hello! Your Auth0 authorized function executed successfully!"
}
response = {
"statusCode": 200,
"body": json.dumps(body)
}
return response
# integration
| true | true |
f7259f1cbf5a3448b4489597dc832795cc8d1b52 | 9,120 | py | Python | fuji_server/models/data_provenance.py | vemonet/fuji | 92aabcb58d76a58981c677bcf0da8e57309c6096 | [
"MIT"
] | null | null | null | fuji_server/models/data_provenance.py | vemonet/fuji | 92aabcb58d76a58981c677bcf0da8e57309c6096 | [
"MIT"
] | null | null | null | fuji_server/models/data_provenance.py | vemonet/fuji | 92aabcb58d76a58981c677bcf0da8e57309c6096 | [
"MIT"
] | null | null | null | # coding: utf-8
from __future__ import absolute_import
from datetime import date, datetime # noqa: F401
from typing import List, Dict # noqa: F401
from fuji_server.models.base_model_ import Model
from fuji_server.models.data_provenance_output import DataProvenanceOutput # noqa: F401,E501
from fuji_server.models.debug import Debug # noqa: F401,E501
from fuji_server.models.fair_result_common import FAIRResultCommon # noqa: F401,E501
from fuji_server.models.fair_result_common_score import FAIRResultCommonScore # noqa: F401,E501
from fuji_server.models.fair_result_evaluation_criterium import FAIRResultEvaluationCriterium # noqa: F401,E501
from fuji_server import util
class DataProvenance(Model):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
def __init__(self, id: int=None, metric_identifier: str=None, metric_name: str=None, metric_tests: Dict[str, FAIRResultEvaluationCriterium]=None, test_status: str='fail', score: FAIRResultCommonScore=None, maturity: str='incomplete', output: DataProvenanceOutput=None, test_debug: Debug=None): # noqa: E501
"""DataProvenance - a model defined in Swagger
:param id: The id of this DataProvenance. # noqa: E501
:type id: int
:param metric_identifier: The metric_identifier of this DataProvenance. # noqa: E501
:type metric_identifier: str
:param metric_name: The metric_name of this DataProvenance. # noqa: E501
:type metric_name: str
:param metric_tests: The metric_tests of this DataProvenance. # noqa: E501
:type metric_tests: Dict[str, FAIRResultEvaluationCriterium]
:param test_status: The test_status of this DataProvenance. # noqa: E501
:type test_status: str
:param score: The score of this DataProvenance. # noqa: E501
:type score: FAIRResultCommonScore
:param maturity: The maturity of this DataProvenance. # noqa: E501
:type maturity: str
:param output: The output of this DataProvenance. # noqa: E501
:type output: DataProvenanceOutput
:param test_debug: The test_debug of this DataProvenance. # noqa: E501
:type test_debug: Debug
"""
self.swagger_types = {
'id': int,
'metric_identifier': str,
'metric_name': str,
'metric_tests': Dict[str, FAIRResultEvaluationCriterium],
'test_status': str,
'score': FAIRResultCommonScore,
'maturity': str,
'output': DataProvenanceOutput,
'test_debug': Debug
}
self.attribute_map = {
'id': 'id',
'metric_identifier': 'metric_identifier',
'metric_name': 'metric_name',
'metric_tests': 'metric_tests',
'test_status': 'test_status',
'score': 'score',
'maturity': 'maturity',
'output': 'output',
'test_debug': 'test_debug'
}
self._id = id
self._metric_identifier = metric_identifier
self._metric_name = metric_name
self._metric_tests = metric_tests
self._test_status = test_status
self._score = score
self._maturity = maturity
self._output = output
self._test_debug = test_debug
@classmethod
def from_dict(cls, dikt) -> 'DataProvenance':
"""Returns the dict as a model
:param dikt: A dict.
:type: dict
:return: The DataProvenance of this DataProvenance. # noqa: E501
:rtype: DataProvenance
"""
return util.deserialize_model(dikt, cls)
@property
def id(self) -> int:
"""Gets the id of this DataProvenance.
:return: The id of this DataProvenance.
:rtype: int
"""
return self._id
@id.setter
def id(self, id: int):
"""Sets the id of this DataProvenance.
:param id: The id of this DataProvenance.
:type id: int
"""
if id is None:
raise ValueError("Invalid value for `id`, must not be `None`") # noqa: E501
self._id = id
@property
def metric_identifier(self) -> str:
"""Gets the metric_identifier of this DataProvenance.
:return: The metric_identifier of this DataProvenance.
:rtype: str
"""
return self._metric_identifier
@metric_identifier.setter
def metric_identifier(self, metric_identifier: str):
"""Sets the metric_identifier of this DataProvenance.
:param metric_identifier: The metric_identifier of this DataProvenance.
:type metric_identifier: str
"""
if metric_identifier is None:
raise ValueError("Invalid value for `metric_identifier`, must not be `None`") # noqa: E501
self._metric_identifier = metric_identifier
@property
def metric_name(self) -> str:
"""Gets the metric_name of this DataProvenance.
:return: The metric_name of this DataProvenance.
:rtype: str
"""
return self._metric_name
@metric_name.setter
def metric_name(self, metric_name: str):
"""Sets the metric_name of this DataProvenance.
:param metric_name: The metric_name of this DataProvenance.
:type metric_name: str
"""
if metric_name is None:
raise ValueError("Invalid value for `metric_name`, must not be `None`") # noqa: E501
self._metric_name = metric_name
@property
def metric_tests(self) -> Dict[str, FAIRResultEvaluationCriterium]:
"""Gets the metric_tests of this DataProvenance.
:return: The metric_tests of this DataProvenance.
:rtype: Dict[str, FAIRResultEvaluationCriterium]
"""
return self._metric_tests
@metric_tests.setter
def metric_tests(self, metric_tests: Dict[str, FAIRResultEvaluationCriterium]):
"""Sets the metric_tests of this DataProvenance.
:param metric_tests: The metric_tests of this DataProvenance.
:type metric_tests: Dict[str, FAIRResultEvaluationCriterium]
"""
self._metric_tests = metric_tests
@property
def test_status(self) -> str:
"""Gets the test_status of this DataProvenance.
:return: The test_status of this DataProvenance.
:rtype: str
"""
return self._test_status
@test_status.setter
def test_status(self, test_status: str):
"""Sets the test_status of this DataProvenance.
:param test_status: The test_status of this DataProvenance.
:type test_status: str
"""
allowed_values = ["pass", "fail", "indeterminate"] # noqa: E501
if test_status not in allowed_values:
raise ValueError(
"Invalid value for `test_status` ({0}), must be one of {1}"
.format(test_status, allowed_values)
)
self._test_status = test_status
@property
def score(self) -> FAIRResultCommonScore:
"""Gets the score of this DataProvenance.
:return: The score of this DataProvenance.
:rtype: FAIRResultCommonScore
"""
return self._score
@score.setter
def score(self, score: FAIRResultCommonScore):
"""Sets the score of this DataProvenance.
:param score: The score of this DataProvenance.
:type score: FAIRResultCommonScore
"""
if score is None:
raise ValueError("Invalid value for `score`, must not be `None`") # noqa: E501
self._score = score
@property
def maturity(self) -> str:
"""Gets the maturity of this DataProvenance.
:return: The maturity of this DataProvenance.
:rtype: str
"""
return self._maturity
@maturity.setter
def maturity(self, maturity: int):
"""Sets the maturity of this Uniqueness.
:param maturity: The maturity of this Uniqueness.
:type maturity: int
"""
self._maturity = maturity
@property
def output(self) -> DataProvenanceOutput:
"""Gets the output of this DataProvenance.
:return: The output of this DataProvenance.
:rtype: DataProvenanceOutput
"""
return self._output
@output.setter
def output(self, output: DataProvenanceOutput):
"""Sets the output of this DataProvenance.
:param output: The output of this DataProvenance.
:type output: DataProvenanceOutput
"""
self._output = output
@property
def test_debug(self) -> Debug:
"""Gets the test_debug of this DataProvenance.
:return: The test_debug of this DataProvenance.
:rtype: Debug
"""
return self._test_debug
@test_debug.setter
def test_debug(self, test_debug: Debug):
"""Sets the test_debug of this DataProvenance.
:param test_debug: The test_debug of this DataProvenance.
:type test_debug: Debug
"""
self._test_debug = test_debug
| 31.448276 | 311 | 0.639035 |
from __future__ import absolute_import
from datetime import date, datetime
from typing import List, Dict
from fuji_server.models.base_model_ import Model
from fuji_server.models.data_provenance_output import DataProvenanceOutput
from fuji_server.models.debug import Debug
from fuji_server.models.fair_result_common import FAIRResultCommon
from fuji_server.models.fair_result_common_score import FAIRResultCommonScore
from fuji_server.models.fair_result_evaluation_criterium import FAIRResultEvaluationCriterium
from fuji_server import util
class DataProvenance(Model):
def __init__(self, id: int=None, metric_identifier: str=None, metric_name: str=None, metric_tests: Dict[str, FAIRResultEvaluationCriterium]=None, test_status: str='fail', score: FAIRResultCommonScore=None, maturity: str='incomplete', output: DataProvenanceOutput=None, test_debug: Debug=None):
self.swagger_types = {
'id': int,
'metric_identifier': str,
'metric_name': str,
'metric_tests': Dict[str, FAIRResultEvaluationCriterium],
'test_status': str,
'score': FAIRResultCommonScore,
'maturity': str,
'output': DataProvenanceOutput,
'test_debug': Debug
}
self.attribute_map = {
'id': 'id',
'metric_identifier': 'metric_identifier',
'metric_name': 'metric_name',
'metric_tests': 'metric_tests',
'test_status': 'test_status',
'score': 'score',
'maturity': 'maturity',
'output': 'output',
'test_debug': 'test_debug'
}
self._id = id
self._metric_identifier = metric_identifier
self._metric_name = metric_name
self._metric_tests = metric_tests
self._test_status = test_status
self._score = score
self._maturity = maturity
self._output = output
self._test_debug = test_debug
@classmethod
def from_dict(cls, dikt) -> 'DataProvenance':
return util.deserialize_model(dikt, cls)
@property
def id(self) -> int:
return self._id
@id.setter
def id(self, id: int):
if id is None:
raise ValueError("Invalid value for `id`, must not be `None`")
self._id = id
@property
def metric_identifier(self) -> str:
return self._metric_identifier
@metric_identifier.setter
def metric_identifier(self, metric_identifier: str):
if metric_identifier is None:
raise ValueError("Invalid value for `metric_identifier`, must not be `None`")
self._metric_identifier = metric_identifier
@property
def metric_name(self) -> str:
return self._metric_name
@metric_name.setter
def metric_name(self, metric_name: str):
if metric_name is None:
raise ValueError("Invalid value for `metric_name`, must not be `None`")
self._metric_name = metric_name
@property
def metric_tests(self) -> Dict[str, FAIRResultEvaluationCriterium]:
return self._metric_tests
@metric_tests.setter
def metric_tests(self, metric_tests: Dict[str, FAIRResultEvaluationCriterium]):
self._metric_tests = metric_tests
@property
def test_status(self) -> str:
return self._test_status
@test_status.setter
def test_status(self, test_status: str):
allowed_values = ["pass", "fail", "indeterminate"]
if test_status not in allowed_values:
raise ValueError(
"Invalid value for `test_status` ({0}), must be one of {1}"
.format(test_status, allowed_values)
)
self._test_status = test_status
@property
def score(self) -> FAIRResultCommonScore:
return self._score
@score.setter
def score(self, score: FAIRResultCommonScore):
if score is None:
raise ValueError("Invalid value for `score`, must not be `None`")
self._score = score
@property
def maturity(self) -> str:
return self._maturity
@maturity.setter
def maturity(self, maturity: int):
self._maturity = maturity
@property
def output(self) -> DataProvenanceOutput:
return self._output
@output.setter
def output(self, output: DataProvenanceOutput):
self._output = output
@property
def test_debug(self) -> Debug:
return self._test_debug
@test_debug.setter
def test_debug(self, test_debug: Debug):
self._test_debug = test_debug
| true | true |
f725a0d77d7b829ac919d0eaca76f246b6e73be7 | 43,643 | py | Python | dbaas/maintenance/migrations/0030_auto__add_field_maintenance_disable_alarms.py | didindinn/database-as-a-service | 747de31ff8546f7874ddd654af860e130afd17a0 | [
"BSD-3-Clause"
] | 303 | 2015-01-08T10:35:54.000Z | 2022-02-28T08:54:06.000Z | dbaas/maintenance/migrations/0030_auto__add_field_maintenance_disable_alarms.py | nouraellm/database-as-a-service | 5e655c9347bea991b7218a01549f5e44f161d7be | [
"BSD-3-Clause"
] | 124 | 2015-01-14T12:56:15.000Z | 2022-03-22T20:45:11.000Z | dbaas/maintenance/migrations/0030_auto__add_field_maintenance_disable_alarms.py | nouraellm/database-as-a-service | 5e655c9347bea991b7218a01549f5e44f161d7be | [
"BSD-3-Clause"
] | 110 | 2015-01-02T11:59:48.000Z | 2022-02-28T08:54:06.000Z | # -*- coding: utf-8 -*-
from south.utils import datetime_utils as datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding field 'Maintenance.disable_alarms'
db.add_column(u'maintenance_maintenance', 'disable_alarms',
self.gf('django.db.models.fields.BooleanField')(default=False),
keep_default=False)
def backwards(self, orm):
# Deleting field 'Maintenance.disable_alarms'
db.delete_column(u'maintenance_maintenance', 'disable_alarms')
models = {
u'account.team': {
'Meta': {'ordering': "[u'name']", 'object_name': 'Team'},
'contacts': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'database_alocation_limit': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '2'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'role': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['auth.Group']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'users': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.User']", 'symmetrical': 'False'})
},
u'auth.group': {
'Meta': {'object_name': 'Group'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'})
},
u'auth.permission': {
'Meta': {'ordering': "(u'content_type__app_label', u'content_type__model', u'codename')", 'unique_together': "((u'content_type', u'codename'),)", 'object_name': 'Permission'},
'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
u'auth.user': {
'Meta': {'object_name': 'User'},
'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'groups': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "u'user_set'", 'blank': 'True', 'to': u"orm['auth.Group']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "u'user_set'", 'blank': 'True', 'to': u"orm['auth.Permission']"}),
'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'})
},
u'backup.backupgroup': {
'Meta': {'object_name': 'BackupGroup'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'contenttypes.contenttype': {
'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"},
'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
u'logical.database': {
'Meta': {'ordering': "(u'name',)", 'unique_together': "((u'name', u'environment'),)", 'object_name': 'Database'},
'backup_path': ('django.db.models.fields.CharField', [], {'max_length': '300', 'null': 'True', 'blank': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'databaseinfra': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databases'", 'on_delete': 'models.PROTECT', 'to': u"orm['physical.DatabaseInfra']"}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'disk_auto_resize': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'environment': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databases'", 'on_delete': 'models.PROTECT', 'to': u"orm['physical.Environment']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_in_quarantine': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_protected': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'db_index': 'True'}),
'project': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'databases'", 'null': 'True', 'on_delete': 'models.PROTECT', 'to': u"orm['logical.Project']"}),
'quarantine_dt': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}),
'quarantine_user': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'databases_quarantine'", 'null': 'True', 'to': u"orm['auth.User']"}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '2'}),
'subscribe_to_email_events': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'team': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'databases'", 'null': 'True', 'to': u"orm['account.Team']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'used_size_in_bytes': ('django.db.models.fields.FloatField', [], {'default': '0.0'})
},
u'logical.project': {
'Meta': {'ordering': "[u'name']", 'object_name': 'Project'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '50'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.databasechangeparameter': {
'Meta': {'object_name': 'DatabaseChangeParameter'},
'can_do_retry': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'current_step': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}),
'database': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'change_parameters'", 'to': u"orm['logical.Database']"}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'task': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_change_parameters'", 'to': u"orm['notification.TaskHistory']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.databasecreate': {
'Meta': {'object_name': 'DatabaseCreate'},
'can_do_retry': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'current_step': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}),
'database': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'databases_create'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['logical.Database']"}),
'description': ('django.db.models.fields.TextField', [], {}),
'environment': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databases_create'", 'to': u"orm['physical.Environment']"}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'infra': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databases_create'", 'to': u"orm['physical.DatabaseInfra']"}),
'is_protected': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'plan': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'databases_create'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Plan']"}),
'plan_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'project': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'databases_create'", 'null': 'True', 'to': u"orm['logical.Project']"}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'subscribe_to_email_events': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'task': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'create_database'", 'to': u"orm['notification.TaskHistory']"}),
'team': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databases_create'", 'to': u"orm['account.Team']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'user': ('django.db.models.fields.CharField', [], {'max_length': '200'})
},
u'maintenance.databasereinstallvm': {
'Meta': {'object_name': 'DatabaseReinstallVM'},
'can_do_retry': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'current_step': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}),
'database': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'reinstall_vm'", 'to': u"orm['logical.Database']"}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'instance': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_reinstall_vm'", 'to': u"orm['physical.Instance']"}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'task': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_reinsgtall_vm'", 'to': u"orm['notification.TaskHistory']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.databaseresize': {
'Meta': {'object_name': 'DatabaseResize'},
'can_do_retry': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'current_step': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}),
'database': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'resizes'", 'to': u"orm['logical.Database']"}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'source_offer': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'database_resizes_source'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Offering']"}),
'source_offer_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'target_offer': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'database_resizes_target'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Offering']"}),
'target_offer_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'task': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_resizes'", 'to': u"orm['notification.TaskHistory']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.databaserestore': {
'Meta': {'object_name': 'DatabaseRestore'},
'can_do_retry': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'current_step': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}),
'database': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_restore'", 'to': u"orm['logical.Database']"}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'group': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_restore'", 'to': u"orm['backup.BackupGroup']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'new_group': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'database_restore_new'", 'null': 'True', 'to': u"orm['backup.BackupGroup']"}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'task': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_restore'", 'to': u"orm['notification.TaskHistory']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.databaserestoreinstancepair': {
'Meta': {'unique_together': "((u'master', u'slave', u'restore'),)", 'object_name': 'DatabaseRestoreInstancePair'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'master': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'restore_master'", 'to': u"orm['physical.Instance']"}),
'restore': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'restore_instances'", 'to': u"orm['maintenance.DatabaseRestore']"}),
'slave': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'restore_slave'", 'to': u"orm['physical.Instance']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.databaseupgrade': {
'Meta': {'object_name': 'DatabaseUpgrade'},
'can_do_retry': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'current_step': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}),
'database': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'upgrades'", 'to': u"orm['logical.Database']"}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'source_plan': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'database_upgrades_source'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Plan']"}),
'source_plan_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'target_plan': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'database_upgrades_target'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Plan']"}),
'target_plan_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'task': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_upgrades'", 'to': u"orm['notification.TaskHistory']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.hostmaintenance': {
'Meta': {'unique_together': "((u'host', u'maintenance'),)", 'object_name': 'HostMaintenance', 'index_together': "[[u'host', u'maintenance']]"},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True'}),
'host': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'host_maintenance'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Host']"}),
'hostname': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '255'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'main_log': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'maintenance': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'maintenance'", 'to': u"orm['maintenance.Maintenance']"}),
'rollback_log': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '4'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.maintenance': {
'Meta': {'object_name': 'Maintenance'},
'affected_hosts': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'celery_task_id': ('django.db.models.fields.CharField', [], {'max_length': '50', 'null': 'True', 'blank': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'created_by': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.CharField', [], {'max_length': '500'}),
'disable_alarms': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'hostsid': ('django.db.models.fields.CommaSeparatedIntegerField', [], {'max_length': '10000'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'main_script': ('django.db.models.fields.TextField', [], {}),
'maximum_workers': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '1'}),
'revoked_by': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'rollback_script': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'scheduled_for': ('django.db.models.fields.DateTimeField', [], {'unique': 'True'}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.maintenanceparameters': {
'Meta': {'object_name': 'MaintenanceParameters'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'function_name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'maintenance': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'maintenance_params'", 'to': u"orm['maintenance.Maintenance']"}),
'parameter_name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'notification.taskhistory': {
'Meta': {'object_name': 'TaskHistory'},
'arguments': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'context': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'database_name': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '255', 'null': 'True', 'blank': 'True'}),
'db_id': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}),
'details': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'ended_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object_class': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'object_id': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}),
'task_id': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'task_name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'task_status': ('django.db.models.fields.CharField', [], {'default': "u'WAITING'", 'max_length': '100', 'db_index': 'True'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'user': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '255', 'null': 'True', 'blank': 'True'})
},
u'physical.databaseinfra': {
'Meta': {'object_name': 'DatabaseInfra'},
'capacity': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'database_key': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'disk_offering': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databaseinfras'", 'null': 'True', 'on_delete': 'models.PROTECT', 'to': u"orm['physical.DiskOffering']"}),
'endpoint': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'endpoint_dns': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'engine': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databaseinfras'", 'on_delete': 'models.PROTECT', 'to': u"orm['physical.Engine']"}),
'environment': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databaseinfras'", 'on_delete': 'models.PROTECT', 'to': u"orm['physical.Environment']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'last_vm_created': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'name_prefix': ('django.db.models.fields.CharField', [], {'max_length': '10', 'null': 'True', 'blank': 'True'}),
'name_stamp': ('django.db.models.fields.CharField', [], {'max_length': '20', 'null': 'True', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '406', 'blank': 'True'}),
'per_database_size_mbytes': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'plan': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databaseinfras'", 'on_delete': 'models.PROTECT', 'to': u"orm['physical.Plan']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'user': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'})
},
u'physical.diskoffering': {
'Meta': {'object_name': 'DiskOffering'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '255'}),
'size_kb': ('django.db.models.fields.PositiveIntegerField', [], {}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'physical.engine': {
'Meta': {'ordering': "(u'engine_type__name', u'version')", 'unique_together': "((u'version', u'engine_type'),)", 'object_name': 'Engine'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'engine_type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'engines'", 'on_delete': 'models.PROTECT', 'to': u"orm['physical.EngineType']"}),
'engine_upgrade_option': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'backwards_engine'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Engine']"}),
'has_users': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'path': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'read_node_description': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '100', 'null': 'True', 'blank': 'True'}),
'template_name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'user_data_script': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'version': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'write_node_description': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '100', 'null': 'True', 'blank': 'True'})
},
u'physical.enginetype': {
'Meta': {'ordering': "(u'name',)", 'object_name': 'EngineType'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_in_memory': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'physical.environment': {
'Meta': {'object_name': 'Environment'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'migrate_environment': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'migrate_to'", 'null': 'True', 'to': u"orm['physical.Environment']"}),
'min_of_zones': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'physical.host': {
'Meta': {'object_name': 'Host'},
'address': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'future_host': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['physical.Host']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'hostname': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '255'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'identifier': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '255'}),
'monitor_url': ('django.db.models.fields.URLField', [], {'max_length': '500', 'null': 'True', 'blank': 'True'}),
'offering': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['physical.Offering']", 'null': 'True'}),
'os_description': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '406', 'null': 'True', 'blank': 'True'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'user': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'})
},
u'physical.instance': {
'Meta': {'unique_together': "((u'address', u'port'),)", 'object_name': 'Instance'},
'address': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'databaseinfra': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'instances'", 'to': u"orm['physical.DatabaseInfra']"}),
'dns': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'future_instance': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['physical.Instance']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'hostname': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'instances'", 'to': u"orm['physical.Host']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'instance_type': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'port': ('django.db.models.fields.IntegerField', [], {}),
'read_only': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'shard': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '2'}),
'total_size_in_bytes': ('django.db.models.fields.FloatField', [], {'null': 'True', 'blank': 'True'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'used_size_in_bytes': ('django.db.models.fields.FloatField', [], {'null': 'True', 'blank': 'True'})
},
u'physical.offering': {
'Meta': {'object_name': 'Offering'},
'cpus': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'environments': ('django.db.models.fields.related.ManyToManyField', [], {'related_name': "u'offerings'", 'symmetrical': 'False', 'to': u"orm['physical.Environment']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'memory_size_mb': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'physical.parameter': {
'Meta': {'ordering': "(u'engine_type__name', u'name')", 'unique_together': "((u'name', u'engine_type'),)", 'object_name': 'Parameter'},
'allowed_values': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '200', 'null': 'True', 'blank': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'custom_method': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'dynamic': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'engine_type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'enginetype'", 'on_delete': 'models.PROTECT', 'to': u"orm['physical.EngineType']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'parameter_type': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '100'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'physical.plan': {
'Meta': {'object_name': 'Plan'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'disk_offering': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'plans'", 'null': 'True', 'on_delete': 'models.PROTECT', 'to': u"orm['physical.DiskOffering']"}),
'engine': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'plans'", 'to': u"orm['physical.Engine']"}),
'engine_equivalent_plan': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'backwards_plan'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Plan']"}),
'environments': ('django.db.models.fields.related.ManyToManyField', [], {'related_name': "u'plans'", 'symmetrical': 'False', 'to': u"orm['physical.Environment']"}),
'has_persistence': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'is_ha': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'max_db_size': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'migrate_plan': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'migrate_to'", 'null': 'True', 'to': u"orm['physical.Plan']"}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'provider': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'replication_topology': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'replication_topology'", 'null': 'True', 'to': u"orm['physical.ReplicationTopology']"}),
'stronger_offering': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'main_offerings'", 'null': 'True', 'to': u"orm['physical.Offering']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'weaker_offering': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'weaker_offerings'", 'null': 'True', 'to': u"orm['physical.Offering']"})
},
u'physical.replicationtopology': {
'Meta': {'object_name': 'ReplicationTopology'},
'can_change_parameters': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'can_clone_db': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'can_reinstall_vm': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'can_resize_vm': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'can_switch_master': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'can_upgrade_db': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'class_path': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'details': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'engine': ('django.db.models.fields.related.ManyToManyField', [], {'related_name': "u'replication_topologies'", 'symmetrical': 'False', 'to': u"orm['physical.Engine']"}),
'has_horizontal_scalability': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'parameter': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "u'replication_topologies'", 'blank': 'True', 'to': u"orm['physical.Parameter']"}),
'script': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'replication_topologies'", 'null': 'True', 'to': u"orm['physical.Script']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'physical.script': {
'Meta': {'object_name': 'Script'},
'configuration': ('django.db.models.fields.CharField', [], {'max_length': '300'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'initialization': ('django.db.models.fields.CharField', [], {'max_length': '300'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'start_database': ('django.db.models.fields.CharField', [], {'max_length': '300'}),
'start_replication': ('django.db.models.fields.CharField', [], {'max_length': '300', 'null': 'True', 'blank': 'True'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
}
}
complete_apps = ['maintenance'] | 97.200445 | 227 | 0.578512 |
from south.utils import datetime_utils as datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
db.add_column(u'maintenance_maintenance', 'disable_alarms',
self.gf('django.db.models.fields.BooleanField')(default=False),
keep_default=False)
def backwards(self, orm):
db.delete_column(u'maintenance_maintenance', 'disable_alarms')
models = {
u'account.team': {
'Meta': {'ordering': "[u'name']", 'object_name': 'Team'},
'contacts': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'database_alocation_limit': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '2'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'role': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['auth.Group']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'users': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.User']", 'symmetrical': 'False'})
},
u'auth.group': {
'Meta': {'object_name': 'Group'},
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '80'}),
'permissions': ('django.db.models.fields.related.ManyToManyField', [], {'to': u"orm['auth.Permission']", 'symmetrical': 'False', 'blank': 'True'})
},
u'auth.permission': {
'Meta': {'ordering': "(u'content_type__app_label', u'content_type__model', u'codename')", 'unique_together': "((u'content_type', u'codename'),)", 'object_name': 'Permission'},
'codename': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'content_type': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['contenttypes.ContentType']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '50'})
},
u'auth.user': {
'Meta': {'object_name': 'User'},
'date_joined': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'email': ('django.db.models.fields.EmailField', [], {'max_length': '75', 'blank': 'True'}),
'first_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'groups': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "u'user_set'", 'blank': 'True', 'to': u"orm['auth.Group']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'is_staff': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_superuser': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'last_login': ('django.db.models.fields.DateTimeField', [], {'default': 'datetime.datetime.now'}),
'last_name': ('django.db.models.fields.CharField', [], {'max_length': '30', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '128'}),
'user_permissions': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "u'user_set'", 'blank': 'True', 'to': u"orm['auth.Permission']"}),
'username': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '30'})
},
u'backup.backupgroup': {
'Meta': {'object_name': 'BackupGroup'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'contenttypes.contenttype': {
'Meta': {'ordering': "('name',)", 'unique_together': "(('app_label', 'model'),)", 'object_name': 'ContentType', 'db_table': "'django_content_type'"},
'app_label': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'model': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'})
},
u'logical.database': {
'Meta': {'ordering': "(u'name',)", 'unique_together': "((u'name', u'environment'),)", 'object_name': 'Database'},
'backup_path': ('django.db.models.fields.CharField', [], {'max_length': '300', 'null': 'True', 'blank': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'databaseinfra': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databases'", 'on_delete': 'models.PROTECT', 'to': u"orm['physical.DatabaseInfra']"}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'disk_auto_resize': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'environment': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databases'", 'on_delete': 'models.PROTECT', 'to': u"orm['physical.Environment']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_in_quarantine': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'is_protected': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'db_index': 'True'}),
'project': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'databases'", 'null': 'True', 'on_delete': 'models.PROTECT', 'to': u"orm['logical.Project']"}),
'quarantine_dt': ('django.db.models.fields.DateField', [], {'null': 'True', 'blank': 'True'}),
'quarantine_user': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'databases_quarantine'", 'null': 'True', 'to': u"orm['auth.User']"}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '2'}),
'subscribe_to_email_events': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'team': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'databases'", 'null': 'True', 'to': u"orm['account.Team']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'used_size_in_bytes': ('django.db.models.fields.FloatField', [], {'default': '0.0'})
},
u'logical.project': {
'Meta': {'ordering': "[u'name']", 'object_name': 'Project'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'slug': ('django.db.models.fields.SlugField', [], {'max_length': '50'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.databasechangeparameter': {
'Meta': {'object_name': 'DatabaseChangeParameter'},
'can_do_retry': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'current_step': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}),
'database': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'change_parameters'", 'to': u"orm['logical.Database']"}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'task': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_change_parameters'", 'to': u"orm['notification.TaskHistory']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.databasecreate': {
'Meta': {'object_name': 'DatabaseCreate'},
'can_do_retry': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'current_step': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}),
'database': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'databases_create'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['logical.Database']"}),
'description': ('django.db.models.fields.TextField', [], {}),
'environment': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databases_create'", 'to': u"orm['physical.Environment']"}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'infra': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databases_create'", 'to': u"orm['physical.DatabaseInfra']"}),
'is_protected': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'plan': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'databases_create'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Plan']"}),
'plan_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'project': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'databases_create'", 'null': 'True', 'to': u"orm['logical.Project']"}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'subscribe_to_email_events': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'task': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'create_database'", 'to': u"orm['notification.TaskHistory']"}),
'team': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databases_create'", 'to': u"orm['account.Team']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'user': ('django.db.models.fields.CharField', [], {'max_length': '200'})
},
u'maintenance.databasereinstallvm': {
'Meta': {'object_name': 'DatabaseReinstallVM'},
'can_do_retry': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'current_step': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}),
'database': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'reinstall_vm'", 'to': u"orm['logical.Database']"}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'instance': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_reinstall_vm'", 'to': u"orm['physical.Instance']"}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'task': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_reinsgtall_vm'", 'to': u"orm['notification.TaskHistory']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.databaseresize': {
'Meta': {'object_name': 'DatabaseResize'},
'can_do_retry': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'current_step': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}),
'database': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'resizes'", 'to': u"orm['logical.Database']"}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'source_offer': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'database_resizes_source'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Offering']"}),
'source_offer_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'target_offer': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'database_resizes_target'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Offering']"}),
'target_offer_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'task': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_resizes'", 'to': u"orm['notification.TaskHistory']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.databaserestore': {
'Meta': {'object_name': 'DatabaseRestore'},
'can_do_retry': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'current_step': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}),
'database': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_restore'", 'to': u"orm['logical.Database']"}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'group': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_restore'", 'to': u"orm['backup.BackupGroup']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'new_group': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'database_restore_new'", 'null': 'True', 'to': u"orm['backup.BackupGroup']"}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'task': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_restore'", 'to': u"orm['notification.TaskHistory']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.databaserestoreinstancepair': {
'Meta': {'unique_together': "((u'master', u'slave', u'restore'),)", 'object_name': 'DatabaseRestoreInstancePair'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'master': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'restore_master'", 'to': u"orm['physical.Instance']"}),
'restore': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'restore_instances'", 'to': u"orm['maintenance.DatabaseRestore']"}),
'slave': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'restore_slave'", 'to': u"orm['physical.Instance']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.databaseupgrade': {
'Meta': {'object_name': 'DatabaseUpgrade'},
'can_do_retry': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'current_step': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '0'}),
'database': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'upgrades'", 'to': u"orm['logical.Database']"}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'source_plan': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'database_upgrades_source'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Plan']"}),
'source_plan_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'target_plan': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'database_upgrades_target'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Plan']"}),
'target_plan_name': ('django.db.models.fields.CharField', [], {'max_length': '100', 'null': 'True', 'blank': 'True'}),
'task': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'database_upgrades'", 'to': u"orm['notification.TaskHistory']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.hostmaintenance': {
'Meta': {'unique_together': "((u'host', u'maintenance'),)", 'object_name': 'HostMaintenance', 'index_together': "[[u'host', u'maintenance']]"},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True'}),
'host': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'host_maintenance'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Host']"}),
'hostname': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '255'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'main_log': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'maintenance': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'maintenance'", 'to': u"orm['maintenance.Maintenance']"}),
'rollback_log': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '4'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.maintenance': {
'Meta': {'object_name': 'Maintenance'},
'affected_hosts': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'celery_task_id': ('django.db.models.fields.CharField', [], {'max_length': '50', 'null': 'True', 'blank': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'created_by': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.CharField', [], {'max_length': '500'}),
'disable_alarms': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'finished_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'hostsid': ('django.db.models.fields.CommaSeparatedIntegerField', [], {'max_length': '10000'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'main_script': ('django.db.models.fields.TextField', [], {}),
'maximum_workers': ('django.db.models.fields.PositiveSmallIntegerField', [], {'default': '1'}),
'revoked_by': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'rollback_script': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'scheduled_for': ('django.db.models.fields.DateTimeField', [], {'unique': 'True'}),
'started_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'maintenance.maintenanceparameters': {
'Meta': {'object_name': 'MaintenanceParameters'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'function_name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'maintenance': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'maintenance_params'", 'to': u"orm['maintenance.Maintenance']"}),
'parameter_name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'notification.taskhistory': {
'Meta': {'object_name': 'TaskHistory'},
'arguments': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'context': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'database_name': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '255', 'null': 'True', 'blank': 'True'}),
'db_id': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}),
'details': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'ended_at': ('django.db.models.fields.DateTimeField', [], {'null': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'object_class': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'object_id': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}),
'task_id': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'task_name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'task_status': ('django.db.models.fields.CharField', [], {'default': "u'WAITING'", 'max_length': '100', 'db_index': 'True'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'user': ('django.db.models.fields.CharField', [], {'db_index': 'True', 'max_length': '255', 'null': 'True', 'blank': 'True'})
},
u'physical.databaseinfra': {
'Meta': {'object_name': 'DatabaseInfra'},
'capacity': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'database_key': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'disk_offering': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databaseinfras'", 'null': 'True', 'on_delete': 'models.PROTECT', 'to': u"orm['physical.DiskOffering']"}),
'endpoint': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'endpoint_dns': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'engine': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databaseinfras'", 'on_delete': 'models.PROTECT', 'to': u"orm['physical.Engine']"}),
'environment': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databaseinfras'", 'on_delete': 'models.PROTECT', 'to': u"orm['physical.Environment']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'last_vm_created': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'name_prefix': ('django.db.models.fields.CharField', [], {'max_length': '10', 'null': 'True', 'blank': 'True'}),
'name_stamp': ('django.db.models.fields.CharField', [], {'max_length': '20', 'null': 'True', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '406', 'blank': 'True'}),
'per_database_size_mbytes': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'plan': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'databaseinfras'", 'on_delete': 'models.PROTECT', 'to': u"orm['physical.Plan']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'user': ('django.db.models.fields.CharField', [], {'max_length': '100', 'blank': 'True'})
},
u'physical.diskoffering': {
'Meta': {'object_name': 'DiskOffering'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '255'}),
'size_kb': ('django.db.models.fields.PositiveIntegerField', [], {}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'physical.engine': {
'Meta': {'ordering': "(u'engine_type__name', u'version')", 'unique_together': "((u'version', u'engine_type'),)", 'object_name': 'Engine'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'engine_type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'engines'", 'on_delete': 'models.PROTECT', 'to': u"orm['physical.EngineType']"}),
'engine_upgrade_option': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'backwards_engine'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Engine']"}),
'has_users': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'path': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'read_node_description': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '100', 'null': 'True', 'blank': 'True'}),
'template_name': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'user_data_script': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'version': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'write_node_description': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '100', 'null': 'True', 'blank': 'True'})
},
u'physical.enginetype': {
'Meta': {'ordering': "(u'name',)", 'object_name': 'EngineType'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_in_memory': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'physical.environment': {
'Meta': {'object_name': 'Environment'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'migrate_environment': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'migrate_to'", 'null': 'True', 'to': u"orm['physical.Environment']"}),
'min_of_zones': ('django.db.models.fields.PositiveIntegerField', [], {'default': '1'}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'physical.host': {
'Meta': {'object_name': 'Host'},
'address': ('django.db.models.fields.CharField', [], {'max_length': '255'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'future_host': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['physical.Host']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'hostname': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '255'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'identifier': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '255'}),
'monitor_url': ('django.db.models.fields.URLField', [], {'max_length': '500', 'null': 'True', 'blank': 'True'}),
'offering': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['physical.Offering']", 'null': 'True'}),
'os_description': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'}),
'password': ('django.db.models.fields.CharField', [], {'max_length': '406', 'null': 'True', 'blank': 'True'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'user': ('django.db.models.fields.CharField', [], {'max_length': '255', 'null': 'True', 'blank': 'True'})
},
u'physical.instance': {
'Meta': {'unique_together': "((u'address', u'port'),)", 'object_name': 'Instance'},
'address': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'databaseinfra': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'instances'", 'to': u"orm['physical.DatabaseInfra']"}),
'dns': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'future_instance': ('django.db.models.fields.related.ForeignKey', [], {'to': u"orm['physical.Instance']", 'null': 'True', 'on_delete': 'models.SET_NULL', 'blank': 'True'}),
'hostname': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'instances'", 'to': u"orm['physical.Host']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'instance_type': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'port': ('django.db.models.fields.IntegerField', [], {}),
'read_only': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'shard': ('django.db.models.fields.IntegerField', [], {'null': 'True', 'blank': 'True'}),
'status': ('django.db.models.fields.IntegerField', [], {'default': '2'}),
'total_size_in_bytes': ('django.db.models.fields.FloatField', [], {'null': 'True', 'blank': 'True'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'used_size_in_bytes': ('django.db.models.fields.FloatField', [], {'null': 'True', 'blank': 'True'})
},
u'physical.offering': {
'Meta': {'object_name': 'Offering'},
'cpus': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'environments': ('django.db.models.fields.related.ManyToManyField', [], {'related_name': "u'offerings'", 'symmetrical': 'False', 'to': u"orm['physical.Environment']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'memory_size_mb': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'physical.parameter': {
'Meta': {'ordering': "(u'engine_type__name', u'name')", 'unique_together': "((u'name', u'engine_type'),)", 'object_name': 'Parameter'},
'allowed_values': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '200', 'null': 'True', 'blank': 'True'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'custom_method': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'dynamic': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'engine_type': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'enginetype'", 'on_delete': 'models.PROTECT', 'to': u"orm['physical.EngineType']"}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'parameter_type': ('django.db.models.fields.CharField', [], {'default': "u''", 'max_length': '100'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'physical.plan': {
'Meta': {'object_name': 'Plan'},
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'description': ('django.db.models.fields.TextField', [], {'null': 'True', 'blank': 'True'}),
'disk_offering': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'plans'", 'null': 'True', 'on_delete': 'models.PROTECT', 'to': u"orm['physical.DiskOffering']"}),
'engine': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'plans'", 'to': u"orm['physical.Engine']"}),
'engine_equivalent_plan': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'backwards_plan'", 'null': 'True', 'on_delete': 'models.SET_NULL', 'to': u"orm['physical.Plan']"}),
'environments': ('django.db.models.fields.related.ManyToManyField', [], {'related_name': "u'plans'", 'symmetrical': 'False', 'to': u"orm['physical.Environment']"}),
'has_persistence': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'is_active': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'is_ha': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
'max_db_size': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'migrate_plan': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'migrate_to'", 'null': 'True', 'to': u"orm['physical.Plan']"}),
'name': ('django.db.models.fields.CharField', [], {'unique': 'True', 'max_length': '100'}),
'provider': ('django.db.models.fields.IntegerField', [], {'default': '0'}),
'replication_topology': ('django.db.models.fields.related.ForeignKey', [], {'related_name': "u'replication_topology'", 'null': 'True', 'to': u"orm['physical.ReplicationTopology']"}),
'stronger_offering': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'main_offerings'", 'null': 'True', 'to': u"orm['physical.Offering']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'}),
'weaker_offering': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'weaker_offerings'", 'null': 'True', 'to': u"orm['physical.Offering']"})
},
u'physical.replicationtopology': {
'Meta': {'object_name': 'ReplicationTopology'},
'can_change_parameters': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'can_clone_db': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'can_reinstall_vm': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'can_resize_vm': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'can_switch_master': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'can_upgrade_db': ('django.db.models.fields.BooleanField', [], {'default': 'True'}),
'class_path': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
'details': ('django.db.models.fields.CharField', [], {'max_length': '200', 'null': 'True', 'blank': 'True'}),
'engine': ('django.db.models.fields.related.ManyToManyField', [], {'related_name': "u'replication_topologies'", 'symmetrical': 'False', 'to': u"orm['physical.Engine']"}),
'has_horizontal_scalability': ('django.db.models.fields.BooleanField', [], {'default': 'False'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '200'}),
'parameter': ('django.db.models.fields.related.ManyToManyField', [], {'symmetrical': 'False', 'related_name': "u'replication_topologies'", 'blank': 'True', 'to': u"orm['physical.Parameter']"}),
'script': ('django.db.models.fields.related.ForeignKey', [], {'blank': 'True', 'related_name': "u'replication_topologies'", 'null': 'True', 'to': u"orm['physical.Script']"}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
},
u'physical.script': {
'Meta': {'object_name': 'Script'},
'configuration': ('django.db.models.fields.CharField', [], {'max_length': '300'}),
'created_at': ('django.db.models.fields.DateTimeField', [], {'auto_now_add': 'True', 'blank': 'True'}),
u'id': ('django.db.models.fields.AutoField', [], {'primary_key': 'True'}),
'initialization': ('django.db.models.fields.CharField', [], {'max_length': '300'}),
'name': ('django.db.models.fields.CharField', [], {'max_length': '100'}),
'start_database': ('django.db.models.fields.CharField', [], {'max_length': '300'}),
'start_replication': ('django.db.models.fields.CharField', [], {'max_length': '300', 'null': 'True', 'blank': 'True'}),
'updated_at': ('django.db.models.fields.DateTimeField', [], {'auto_now': 'True', 'blank': 'True'})
}
}
complete_apps = ['maintenance'] | true | true |
f725a15222a68089d26599b0bfbcfc3a016ae93b | 1,466 | py | Python | _action_files/nb2post.py | inc0/fastpages | 28603b7c6f38f83eea715ab12d71895078dbff9a | [
"Apache-2.0"
] | null | null | null | _action_files/nb2post.py | inc0/fastpages | 28603b7c6f38f83eea715ab12d71895078dbff9a | [
"Apache-2.0"
] | null | null | null | _action_files/nb2post.py | inc0/fastpages | 28603b7c6f38f83eea715ab12d71895078dbff9a | [
"Apache-2.0"
] | null | null | null | """Converts Jupyter Notebooks to Jekyll compliant blog posts"""
from datetime import datetime
import re, os, logging
from nbdev import export2html
from nbdev.export2html import Config, Path, _re_digits, _to_html, _re_block_notes
from fast_template import rename_for_jekyll
warnings = set()
# Modify the naming process such that destination files get named properly for Jekyll _posts
def _nb2htmlfname(nb_path, dest=None):
fname = rename_for_jekyll(nb_path, warnings=warnings)
if dest is None: dest = Config().doc_path
return Path(dest)/fname
# Add embedded links for youtube and twitter
def add_embedded_links(cell):
"Convert block quotes to embedded links in `cell`"
_styles = ['youtube', 'twitter']
def _inner(m):
title,text = m.groups()
if title.lower() not in _styles: return f"> {m.groups()[0]}: {m.groups()[1]}"
return '{% include '+title.lower()+".html content=\'`"+_to_html(text)+"`\' %}"
if cell['cell_type'] == 'markdown':
cell['source'] = _re_block_notes.sub(_inner, cell['source'])
return cell
# TODO: Open a GitHub Issue in addition to printing warnings
for original, new in warnings:
print(f'{original} has been renamed to {new} to be complaint with Jekyll naming conventions.\n')
## apply monkey patches
export2html._nb2htmlfname = _nb2htmlfname
export2html.process_cell.append(add_embedded_links)
export2html.notebook2html(fname='_notebooks/*.ipynb', dest='_posts/')
| 39.621622 | 100 | 0.723056 | from datetime import datetime
import re, os, logging
from nbdev import export2html
from nbdev.export2html import Config, Path, _re_digits, _to_html, _re_block_notes
from fast_template import rename_for_jekyll
warnings = set()
def _nb2htmlfname(nb_path, dest=None):
fname = rename_for_jekyll(nb_path, warnings=warnings)
if dest is None: dest = Config().doc_path
return Path(dest)/fname
def add_embedded_links(cell):
_styles = ['youtube', 'twitter']
def _inner(m):
title,text = m.groups()
if title.lower() not in _styles: return f"> {m.groups()[0]}: {m.groups()[1]}"
return '{% include '+title.lower()+".html content=\'`"+_to_html(text)+"`\' %}"
if cell['cell_type'] == 'markdown':
cell['source'] = _re_block_notes.sub(_inner, cell['source'])
return cell
for original, new in warnings:
print(f'{original} has been renamed to {new} to be complaint with Jekyll naming conventions.\n')
name = _nb2htmlfname
export2html.process_cell.append(add_embedded_links)
export2html.notebook2html(fname='_notebooks/*.ipynb', dest='_posts/')
| true | true |
f725a1d093a3821295e1aa148bb4213f8b3be699 | 7,204 | py | Python | Chapter05/stock_trading_visual_continuous_env.py | harinipsamy/Tensorflow-2-Reinforcement-Learning-Cookbook | b8858554e4c819c96de10c100f8213ab41561c69 | [
"MIT"
] | null | null | null | Chapter05/stock_trading_visual_continuous_env.py | harinipsamy/Tensorflow-2-Reinforcement-Learning-Cookbook | b8858554e4c819c96de10c100f8213ab41561c69 | [
"MIT"
] | null | null | null | Chapter05/stock_trading_visual_continuous_env.py | harinipsamy/Tensorflow-2-Reinforcement-Learning-Cookbook | b8858554e4c819c96de10c100f8213ab41561c69 | [
"MIT"
] | 1 | 2021-03-27T18:35:14.000Z | 2021-03-27T18:35:14.000Z | #!/usr/bin/env python
# Visual stock/share trading RL environment with continuous trade actions
# Chapter 5, TensorFlow 2 Reinforcement Learning Cookbook | Praveen Palanisamy
import os
import random
from typing import Dict
import cv2
import gym
import numpy as np
import pandas as pd
from gym import spaces
from trading_utils import TradeVisualizer
env_config = {
"ticker": "MSFT",
"opening_account_balance": 1000,
# Number of steps (days) of data provided to the agent in one observation
"observation_horizon_sequence_length": 30,
}
class StockTradingVisualContinuousEnv(gym.Env):
def __init__(self, env_config: Dict = env_config):
"""Stock trading environment for RL agents with continuous action space
Args:
ticker (str, optional): Ticker symbol for the stock. Defaults to "MSFT".
env_config (Dict): Env configuration values
"""
super(StockTradingVisualContinuousEnv, self).__init__()
self.ticker = env_config.get("ticker", "MSFT")
data_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "data")
self.ticker_file_stream = os.path.join(f"{data_dir}", f"{self.ticker}.csv")
assert os.path.isfile(
self.ticker_file_stream
), f"Historical stock data file stream not found at: data/{self.ticker}.csv"
# Stock market data stream. An offline file stream is used. Alternatively, a web
# API can be used to pull live data.
# Data-Frame: Date Open High Low Close Adj-Close Volume
self.ohlcv_df = pd.read_csv(self.ticker_file_stream)
self.opening_account_balance = env_config["opening_account_balance"]
# Action: 1-dim value indicating a fraction amount of shares to Buy (0 to 1) or
# sell (-1 to 0). The fraction is taken on the allowable number of
# shares that can be bought or sold based on the account balance (no margin).
self.action_space = spaces.Box(
low=np.array([-1]), high=np.array([1]), dtype=np.float
)
self.observation_features = [
"Open",
"High",
"Low",
"Close",
"Adj Close",
"Volume",
]
self.obs_width, self.obs_height = 128, 128
self.horizon = env_config.get("observation_horizon_sequence_length")
self.observation_space = spaces.Box(
low=0,
high=255,
shape=(128, 128, 3),
dtype=np.uint8,
)
self.viz = None # Visualizer
def step(self, action):
# Execute one step within the environment
self.execute_trade_action(action)
self.current_step += 1
reward = self.account_value - self.opening_account_balance # Profit (loss)
done = self.account_value <= 0 or self.current_step >= len(
self.ohlcv_df.loc[:, "Open"].values
)
obs = self.get_observation()
return obs, reward, done, {}
def reset(self):
# Reset the state of the environment to an initial state
self.cash_balance = self.opening_account_balance
self.account_value = self.opening_account_balance
self.num_shares_held = 0
self.cost_basis = 0
self.current_step = 0
self.trades = []
if self.viz is None:
self.viz = TradeVisualizer(
self.ticker,
self.ticker_file_stream,
"TFRL-Cookbook Ch4-StockTradingVisualContinuousEnv",
)
return self.get_observation()
def render(self, **kwargs):
# Render the environment to the screen
if self.current_step > self.horizon:
self.viz.render(
self.current_step,
self.account_value,
self.trades,
window_size=self.horizon,
)
def close(self):
if self.viz is not None:
self.viz.close()
self.viz = None
def get_observation(self):
"""Return a view of the Ticker price chart as image observation
Returns:
img_observation (np.ndarray): Image of ticker candle stick plot
with volume bars as observation
"""
img_observation = self.viz.render_image_observation(
self.current_step, self.horizon
)
img_observation = cv2.resize(
img_observation, dsize=(128, 128), interpolation=cv2.INTER_CUBIC
)
return img_observation
def execute_trade_action(self, action):
if action == 0: # Indicates "Hold" action
# Hold position; No trade to be executed
return
order_type = "buy" if action > 0 else "sell"
order_fraction_of_allowable_shares = abs(action)
# Stochastically determine the current stock price based on Market Open & Close
current_price = random.uniform(
self.ohlcv_df.loc[self.current_step, "Open"],
self.ohlcv_df.loc[self.current_step, "Close"],
)
if order_type == "buy":
allowable_shares = int(self.cash_balance / current_price)
# Simulate a BUY order and execute it at current_price
num_shares_bought = int(
allowable_shares * order_fraction_of_allowable_shares
)
current_cost = self.cost_basis * self.num_shares_held
additional_cost = num_shares_bought * current_price
self.cash_balance -= additional_cost
self.cost_basis = (current_cost + additional_cost) / (
self.num_shares_held + num_shares_bought
)
self.num_shares_held += num_shares_bought
if num_shares_bought > 0:
self.trades.append(
{
"type": "buy",
"step": self.current_step,
"shares": num_shares_bought,
"proceeds": additional_cost,
}
)
elif order_type == "sell":
# Simulate a SELL order and execute it at current_price
num_shares_sold = int(
self.num_shares_held * order_fraction_of_allowable_shares
)
self.cash_balance += num_shares_sold * current_price
self.num_shares_held -= num_shares_sold
sale_proceeds = num_shares_sold * current_price
if num_shares_sold > 0:
self.trades.append(
{
"type": "sell",
"step": self.current_step,
"shares": num_shares_sold,
"proceeds": sale_proceeds,
}
)
if self.num_shares_held == 0:
self.cost_basis = 0
# Update account value
self.account_value = self.cash_balance + self.num_shares_held * current_price
if __name__ == "__main__":
env = StockTradingVisualContinuousEnv()
obs = env.reset()
for _ in range(600):
action = env.action_space.sample()
next_obs, reward, done, _ = env.step(action)
env.render()
| 35.141463 | 88 | 0.593282 |
import os
import random
from typing import Dict
import cv2
import gym
import numpy as np
import pandas as pd
from gym import spaces
from trading_utils import TradeVisualizer
env_config = {
"ticker": "MSFT",
"opening_account_balance": 1000,
"observation_horizon_sequence_length": 30,
}
class StockTradingVisualContinuousEnv(gym.Env):
def __init__(self, env_config: Dict = env_config):
super(StockTradingVisualContinuousEnv, self).__init__()
self.ticker = env_config.get("ticker", "MSFT")
data_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "data")
self.ticker_file_stream = os.path.join(f"{data_dir}", f"{self.ticker}.csv")
assert os.path.isfile(
self.ticker_file_stream
), f"Historical stock data file stream not found at: data/{self.ticker}.csv"
self.ohlcv_df = pd.read_csv(self.ticker_file_stream)
self.opening_account_balance = env_config["opening_account_balance"]
self.action_space = spaces.Box(
low=np.array([-1]), high=np.array([1]), dtype=np.float
)
self.observation_features = [
"Open",
"High",
"Low",
"Close",
"Adj Close",
"Volume",
]
self.obs_width, self.obs_height = 128, 128
self.horizon = env_config.get("observation_horizon_sequence_length")
self.observation_space = spaces.Box(
low=0,
high=255,
shape=(128, 128, 3),
dtype=np.uint8,
)
self.viz = None
def step(self, action):
self.execute_trade_action(action)
self.current_step += 1
reward = self.account_value - self.opening_account_balance
done = self.account_value <= 0 or self.current_step >= len(
self.ohlcv_df.loc[:, "Open"].values
)
obs = self.get_observation()
return obs, reward, done, {}
def reset(self):
self.cash_balance = self.opening_account_balance
self.account_value = self.opening_account_balance
self.num_shares_held = 0
self.cost_basis = 0
self.current_step = 0
self.trades = []
if self.viz is None:
self.viz = TradeVisualizer(
self.ticker,
self.ticker_file_stream,
"TFRL-Cookbook Ch4-StockTradingVisualContinuousEnv",
)
return self.get_observation()
def render(self, **kwargs):
if self.current_step > self.horizon:
self.viz.render(
self.current_step,
self.account_value,
self.trades,
window_size=self.horizon,
)
def close(self):
if self.viz is not None:
self.viz.close()
self.viz = None
def get_observation(self):
img_observation = self.viz.render_image_observation(
self.current_step, self.horizon
)
img_observation = cv2.resize(
img_observation, dsize=(128, 128), interpolation=cv2.INTER_CUBIC
)
return img_observation
def execute_trade_action(self, action):
if action == 0:
return
order_type = "buy" if action > 0 else "sell"
order_fraction_of_allowable_shares = abs(action)
current_price = random.uniform(
self.ohlcv_df.loc[self.current_step, "Open"],
self.ohlcv_df.loc[self.current_step, "Close"],
)
if order_type == "buy":
allowable_shares = int(self.cash_balance / current_price)
num_shares_bought = int(
allowable_shares * order_fraction_of_allowable_shares
)
current_cost = self.cost_basis * self.num_shares_held
additional_cost = num_shares_bought * current_price
self.cash_balance -= additional_cost
self.cost_basis = (current_cost + additional_cost) / (
self.num_shares_held + num_shares_bought
)
self.num_shares_held += num_shares_bought
if num_shares_bought > 0:
self.trades.append(
{
"type": "buy",
"step": self.current_step,
"shares": num_shares_bought,
"proceeds": additional_cost,
}
)
elif order_type == "sell":
num_shares_sold = int(
self.num_shares_held * order_fraction_of_allowable_shares
)
self.cash_balance += num_shares_sold * current_price
self.num_shares_held -= num_shares_sold
sale_proceeds = num_shares_sold * current_price
if num_shares_sold > 0:
self.trades.append(
{
"type": "sell",
"step": self.current_step,
"shares": num_shares_sold,
"proceeds": sale_proceeds,
}
)
if self.num_shares_held == 0:
self.cost_basis = 0
self.account_value = self.cash_balance + self.num_shares_held * current_price
if __name__ == "__main__":
env = StockTradingVisualContinuousEnv()
obs = env.reset()
for _ in range(600):
action = env.action_space.sample()
next_obs, reward, done, _ = env.step(action)
env.render()
| true | true |
f725a200284daaaded9eadc2048d2708f4206809 | 109,449 | py | Python | olfactorybulb/slices/CoronalSlice/TC4_0.py | fameshpatel/olfactorybulb | 8d7a644b4560309ef177c0590ff73ed4c2432604 | [
"MIT"
] | null | null | null | olfactorybulb/slices/CoronalSlice/TC4_0.py | fameshpatel/olfactorybulb | 8d7a644b4560309ef177c0590ff73ed4c2432604 | [
"MIT"
] | null | null | null | olfactorybulb/slices/CoronalSlice/TC4_0.py | fameshpatel/olfactorybulb | 8d7a644b4560309ef177c0590ff73ed4c2432604 | [
"MIT"
] | null | null | null |
from neuron import h
class TransformTC4:
def __init__(self):
# Create a section lookup by section name
# Note: this assumes each section has a unique name
self.name2section = { sec.name(): sec for sec in h.allsec() }
# This will store the new section coordinates
self.section_coords = { }
def set_coords(self, sec_name):
# Lookup the section
nrn_section = self.name2section[sec_name]
# Lookup its new coords
new_coords = self.section_coords[sec_name]
# Use 3D points as section L and diam sources
h.pt3dconst(1, sec=nrn_section)
# Clear the existing points - and allocate room for the incoming points
h.pt3dclear(len(new_coords["diam"]), sec=nrn_section)
# Use vectorization to add the points to section
xvec = h.Vector(new_coords["x"])
yvec = h.Vector(new_coords["y"])
zvec = h.Vector(new_coords["z"])
dvec = h.Vector(new_coords["diam"])
h.pt3dadd(xvec, yvec, zvec, dvec, sec=nrn_section)
def set_all(self):
for sec_name in self.section_coords.keys():
self.set_coords(sec_name)
@staticmethod
def apply_on(prefix):
t = TransformTC4()
t.define_coords(prefix)
t.set_all()
@staticmethod
def apply():
t = TransformTC4()
t.define_coords()
t.set_all()
def define_coords(self, prefix = 'TC4[0]'):
if prefix != '':
prefix += '.'
self.section_coords = {
prefix + 'apic[68]': {
'x':[-401.322,-402.235,-402.958,-401.756],
'y':[743.117,744.267,743.755,747.357],
'z':[-23.836,-21.982,-21.153,-19.972],
'diam':[0.264,0.264,0.264,0.264]
},
prefix + 'apic[100]': {
'x':[-368.650,-369.977,-371.475,-372.161,-372.512,-373.361,-373.937,-374.711,-375.289,-375.521,-375.522,-376.265,-375.041,-375.495,-375.517,-374.957,-374.165,-373.673,-372.672,-371.700],
'y':[721.906,722.984,723.293,723.815,724.044,724.948,725.556,726.107,726.840,727.356,728.641,728.811,731.930,731.927,732.329,732.519,732.126,732.122,732.931,732.884],
'z':[-26.366,-25.829,-24.064,-23.086,-21.692,-20.685,-20.100,-19.668,-18.622,-17.585,-16.403,-13.942,-13.605,-11.861,-10.119,-8.762,-7.375,-6.275,-5.944,-4.515],
'diam':[0.613,0.442,0.442,0.442,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349]
},
prefix + 'apic[113]': {
'x':[-383.489,-383.890,-383.410,-381.081,-380.535],
'y':[724.129,724.331,723.344,724.340,723.080],
'z':[-44.607,-43.031,-41.923,-41.646,-40.779],
'diam':[0.613,0.264,0.264,0.264,0.264]
},
prefix + 'apic[37]': {
'x':[-375.585,-375.040,-374.570,-372.798,-371.266],
'y':[726.541,727.069,727.549,727.462,727.211],
'z':[-37.841,-35.655,-33.710,-32.052,-30.669],
'diam':[0.613,0.706,0.706,0.792,0.792]
},
prefix + 'dend[2]': {
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prefix + 'apic[21]': {
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prefix + 'apic[130]': {
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prefix + 'apic[94]': {
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prefix + 'apic[126]': {
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prefix + 'apic[3]': {
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prefix + 'apic[115]': {
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prefix + 'apic[108]': {
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prefix + 'apic[109]': {
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prefix + 'apic[31]': {
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prefix + 'dend[4]': {
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prefix + 'apic[77]': {
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prefix + 'apic[42]': {
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prefix + 'apic[104]': {
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},
prefix + 'apic[118]': {
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'diam':[0.877,1.056,0.792,0.792,0.792]
},
prefix + 'apic[16]': {
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prefix + 'dend[0]': {
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prefix + 'apic[26]': {
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prefix + 'apic[11]': {
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prefix + 'apic[25]': {
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prefix + 'apic[76]': {
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prefix + 'apic[85]': {
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prefix + 'apic[12]': {
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}
| 92.910866 | 3,305 | 0.576177 |
from neuron import h
class TransformTC4:
def __init__(self):
self.name2section = { sec.name(): sec for sec in h.allsec() }
self.section_coords = { }
def set_coords(self, sec_name):
nrn_section = self.name2section[sec_name]
new_coords = self.section_coords[sec_name]
h.pt3dconst(1, sec=nrn_section)
h.pt3dclear(len(new_coords["diam"]), sec=nrn_section)
xvec = h.Vector(new_coords["x"])
yvec = h.Vector(new_coords["y"])
zvec = h.Vector(new_coords["z"])
dvec = h.Vector(new_coords["diam"])
h.pt3dadd(xvec, yvec, zvec, dvec, sec=nrn_section)
def set_all(self):
for sec_name in self.section_coords.keys():
self.set_coords(sec_name)
@staticmethod
def apply_on(prefix):
t = TransformTC4()
t.define_coords(prefix)
t.set_all()
@staticmethod
def apply():
t = TransformTC4()
t.define_coords()
t.set_all()
def define_coords(self, prefix = 'TC4[0]'):
if prefix != '':
prefix += '.'
self.section_coords = {
prefix + 'apic[68]': {
'x':[-401.322,-402.235,-402.958,-401.756],
'y':[743.117,744.267,743.755,747.357],
'z':[-23.836,-21.982,-21.153,-19.972],
'diam':[0.264,0.264,0.264,0.264]
},
prefix + 'apic[100]': {
'x':[-368.650,-369.977,-371.475,-372.161,-372.512,-373.361,-373.937,-374.711,-375.289,-375.521,-375.522,-376.265,-375.041,-375.495,-375.517,-374.957,-374.165,-373.673,-372.672,-371.700],
'y':[721.906,722.984,723.293,723.815,724.044,724.948,725.556,726.107,726.840,727.356,728.641,728.811,731.930,731.927,732.329,732.519,732.126,732.122,732.931,732.884],
'z':[-26.366,-25.829,-24.064,-23.086,-21.692,-20.685,-20.100,-19.668,-18.622,-17.585,-16.403,-13.942,-13.605,-11.861,-10.119,-8.762,-7.375,-6.275,-5.944,-4.515],
'diam':[0.613,0.442,0.442,0.442,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349]
},
prefix + 'apic[113]': {
'x':[-383.489,-383.890,-383.410,-381.081,-380.535],
'y':[724.129,724.331,723.344,724.340,723.080],
'z':[-44.607,-43.031,-41.923,-41.646,-40.779],
'diam':[0.613,0.264,0.264,0.264,0.264]
},
prefix + 'apic[37]': {
'x':[-375.585,-375.040,-374.570,-372.798,-371.266],
'y':[726.541,727.069,727.549,727.462,727.211],
'z':[-37.841,-35.655,-33.710,-32.052,-30.669],
'diam':[0.613,0.706,0.706,0.792,0.792]
},
prefix + 'dend[2]': {
'x':[-305.424,-306.149,-306.972,-307.633,-307.203,-306.701,-307.338,-308.001,-307.992,-308.743,-309.466,-310.076,-309.901,-309.758,-310.736,-310.876,-311.714,-311.679,-312.585,-313.265,-314.333,-315.285,-316.729,-317.738,-318.707,-319.653,-320.304,-321.717,-323.098,-323.748,-323.829,-324.387,-325.403,-325.801,-326.171,-327.007,-326.773,-326.650,-327.482,-327.738,-328.016,-328.723,-328.947,-329.713,-330.844,-331.091,-331.111,-332.015,-332.114,-332.743,-333.113,-334.087,-334.663,-335.558,-336.136,-336.693,-337.237,-337.555,-337.335,-337.787,-338.119,-339.486,-340.852,-341.635,-342.410,-343.707,-345.556,-346.546,-347.430,-347.788,-348.533,-350.674,-351.546,-352.687,-353.566,-355.548,-356.503,-358.427,-359.864,-360.664,-361.856,-363.812,-364.870,-365.902,-367.369,-368.292,-368.764,-369.440,-370.536,-369.616,-370.027,-370.562,-369.958,-371.540,-372.116,-372.849,-373.095,-373.288,-373.202,-373.851,-374.295,-374.686,-374.293,-374.813,-375.303,-375.578,-376.148,-376.218,-377.242,-377.452,-377.267,-377.331,-377.443,-377.591,-377.441,-377.731,-378.310,-378.240,-378.158,-379.308,-379.335,-379.848,-380.406,-380.797,-380.513,-380.372,-380.032,-379.849,-379.917,-379.376,-378.853,-379.067,-378.944,-379.432,-379.750,-381.569,-381.330,-381.775,-382.134,-382.884,-383.706,-384.466,-384.231,-383.911,-385.008,-385.241,-385.364,-385.829,-386.448,-386.579,-386.330,-384.136,-387.079,-387.052,-386.854,-387.056,-387.134,-386.899,-387.208,-387.344,-387.404,-388.023,-387.858,-388.750,-389.583,-389.749,-390.293,-390.904,-391.807,-392.082,-393.498,-394.299,-395.083,-396.237,-397.409,-398.536,-398.992,-399.711,-400.673,-401.799,-401.981,-402.645,-402.713,-403.474,-404.364,-404.557,-405.328,-406.310,-406.915,-406.661,-407.622,-408.540,-408.281,-408.601,-409.468,-410.498,-411.801,-412.336,-413.417,-414.658,-415.811,-417.025,-417.416,-418.083,-418.578,-418.893,-419.861,-419.442,-420.697,-420.904,-421.188,-421.585,-422.166,-421.938,-421.805,-421.925,-422.798,-423.851,-424.795],
'y':[657.846,655.817,654.383,653.371,652.356,651.413,650.015,649.378,647.780,646.618,645.949,644.136,642.224,640.841,639.570,637.857,636.189,635.102,634.031,633.678,632.205,631.336,631.200,630.695,628.655,627.457,626.996,626.050,625.432,624.547,623.001,621.427,620.541,619.190,617.529,616.051,614.099,613.047,612.730,611.830,610.882,610.074,608.913,607.579,606.409,605.188,603.234,602.723,601.681,600.729,599.748,598.653,597.857,597.572,596.367,594.512,593.448,591.688,590.399,589.428,587.989,587.069,585.854,585.221,584.161,583.198,580.883,581.289,582.894,584.788,583.671,583.680,584.025,584.415,583.736,583.322,583.894,582.830,582.608,583.592,582.619,582.818,582.532,583.399,583.482,583.227,581.977,580.616,579.303,578.936,577.589,576.747,575.455,573.755,572.849,571.127,569.857,568.450,567.149,565.861,564.832,563.384,562.328,559.544,558.447,557.437,556.355,554.987,553.428,552.117,550.812,549.716,546.844,545.215,542.425,541.523,539.703,538.178,536.442,534.657,532.485,531.476,530.006,528.892,526.795,525.742,524.451,521.642,519.838,518.634,516.972,514.884,513.827,511.941,511.025,508.376,506.818,505.672,504.514,503.805,502.764,502.042,500.095,498.508,497.199,495.989,494.709,493.780,492.397,490.989,490.029,489.595,487.717,485.106,484.052,482.960,481.577,479.617,478.870,477.803,476.740,474.906,473.845,473.536,473.042,471.747,470.879,470.946,470.850,469.999,468.585,467.704,467.859,467.609,467.765,466.920,465.363,464.453,464.173,463.719,462.390,461.857,461.112,459.863,459.303,458.068,457.058,455.924,454.394,453.021,452.240,452.244,451.172,450.056,449.408,449.081,449.179,448.412,446.642,445.859,444.628,444.020,442.960,442.026,441.460,440.411,439.813,438.771,437.936,436.800,435.005,434.026,432.869,431.080,429.895,428.409,426.955,426.013,425.250],
'z':[-86.353,-87.989,-88.761,-88.939,-88.680,-88.000,-88.761,-89.898,-89.692,-89.536,-90.332,-90.940,-91.312,-91.513,-92.119,-92.179,-92.385,-92.720,-93.485,-94.379,-96.344,-98.222,-99.345,-100.621,-101.779,-102.844,-103.498,-105.342,-106.480,-106.656,-107.169,-107.746,-108.364,-109.480,-109.473,-110.100,-110.682,-111.082,-112.447,-113.092,-113.890,-115.204,-115.781,-117.054,-117.343,-117.169,-117.852,-119.066,-119.581,-119.313,-119.441,-119.837,-120.587,-121.746,-122.672,-123.778,-124.385,-125.363,-124.846,-125.350,-126.532,-127.184,-127.755,-128.331,-129.353,-130.216,-130.395,-130.526,-130.866,-130.201,-130.487,-130.377,-130.020,-130.195,-130.095,-129.484,-129.739,-130.273,-129.503,-128.554,-128.446,-128.139,-128.708,-129.447,-129.267,-129.626,-128.220,-129.090,-128.702,-127.449,-127.936,-128.480,-128.426,-128.490,-128.582,-129.206,-128.592,-127.909,-128.036,-127.543,-128.534,-129.986,-129.992,-130.331,-130.234,-130.883,-131.173,-131.188,-131.680,-131.871,-132.231,-132.626,-133.187,-134.125,-134.815,-135.144,-136.072,-136.551,-136.788,-137.401,-138.135,-138.287,-139.150,-139.579,-140.098,-140.192,-140.236,-141.162,-141.916,-142.092,-142.369,-142.775,-143.167,-144.192,-145.113,-145.094,-145.724,-146.425,-146.455,-146.475,-147.001,-147.326,-147.918,-148.456,-148.977,-149.126,-149.373,-149.399,-149.570,-150.616,-150.866,-152.262,-150.603,-151.171,-151.544,-151.308,-152.278,-153.162,-153.967,-154.817,-155.295,-155.630,-155.975,-156.965,-156.694,-157.271,-158.206,-159.039,-160.278,-161.103,-161.318,-162.349,-163.169,-162.746,-163.119,-164.759,-164.727,-165.663,-166.734,-167.411,-167.563,-168.462,-169.395,-169.037,-169.627,-170.025,-170.360,-171.438,-171.585,-172.070,-173.092,-174.290,-174.577,-175.730,-176.070,-176.351,-177.666,-178.886,-180.318,-180.816,-182.097,-182.728,-183.377,-184.530,-185.581,-186.584,-186.938,-186.899,-187.257,-187.614,-188.210,-188.098,-187.870,-188.570,-188.928,-189.297,-189.777,-189.732,-189.137],
'diam':[0.706,0.706,0.706,0.706,0.706,0.706,0.706,0.706,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.528,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.442,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264]
},
prefix + 'apic[123]': {
'x':[-388.217,-387.909,-388.043,-388.419,-388.477,-388.170,-388.968,-389.566,-390.968,-391.192,-392.156,-392.402,-392.782,-393.483,-393.249,-393.743],
'y':[750.404,751.127,751.354,751.752,752.328,752.722,753.615,754.306,754.648,754.734,755.681,757.866,758.125,758.138,761.297,762.410],
'z':[-11.011,-8.893,-6.999,-6.000,-4.675,-3.202,-0.731,0.335,2.959,4.058,5.472,7.517,8.479,9.546,10.264,10.996],
'diam':[0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349]
},
prefix + 'apic[137]': {
'x':[-406.717,-408.041,-408.660],
'y':[739.828,738.773,737.771],
'z':[-42.726,-43.717,-44.083],
'diam':[0.264,0.264,0.264]
},
prefix + 'apic[87]': {
'x':[-383.728,-383.712,-383.710,-384.757,-384.819,-384.736,-384.132,-383.222,-383.501,-383.637,-383.721],
'y':[729.654,730.154,730.665,731.362,732.288,733.530,733.810,734.566,736.049,736.656,737.408],
'z':[-36.522,-35.245,-33.983,-32.367,-30.814,-29.637,-28.144,-26.853,-24.876,-24.069,-23.133],
'diam':[0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264]
},
prefix + 'apic[107]': {
'x':[-402.452,-402.181,-402.512,-403.510,-404.990,-407.221,-407.770,-408.463,-409.672,-410.454,-411.101,-411.695,-411.789],
'y':[733.848,735.891,736.974,738.379,738.467,740.361,741.079,741.936,743.547,744.308,744.920,746.146,747.115],
'z':[-36.376,-35.862,-35.126,-34.206,-33.382,-31.205,-30.321,-29.313,-27.433,-26.810,-26.317,-25.184,-24.501],
'diam':[0.349,0.349,0.349,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264]
},
prefix + 'apic[45]': {
'x':[-366.165,-366.313,-366.835],
'y':[732.626,733.106,733.993],
'z':[-4.049,-3.108,-1.770],
'diam':[0.349,0.349,0.349]
},
prefix + 'apic[35]': {
'x':[-375.082,-376.135,-376.999,-377.821],
'y':[723.155,724.325,724.617,725.564],
'z':[-47.527,-44.332,-42.503,-41.334],
'diam':[1.234,0.792,0.792,0.792]
},
prefix + 'apic[38]': {
'x':[-371.266,-371.908],
'y':[727.211,728.584],
'z':[-30.669,-28.319],
'diam':[0.792,0.528]
},
prefix + 'apic[33]': {
'x':[-374.024,-375.311,-377.581,-378.871,-380.203,-381.533,-382.323,-383.122,-384.607,-385.543,-385.955,-385.374,-384.843,-384.505,-384.329,-384.884,-385.583,-386.239,-387.042],
'y':[715.198,716.497,717.045,717.621,716.615,718.152,718.867,719.520,720.476,721.465,723.395,724.035,724.041,723.880,724.005,724.978,725.181,725.388,726.011],
'z':[-38.925,-37.785,-37.421,-37.312,-36.767,-35.118,-34.480,-34.154,-33.048,-31.047,-27.090,-24.605,-23.094,-21.588,-20.360,-18.534,-17.451,-14.734,-12.598],
'diam':[0.877,0.442,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264,0.264]
},
prefix + 'apic[4]': {
'x':[-372.673,-370.268,-368.721,-367.836,-366.480,-365.724,-364.489,-364.000,-364.098],
'y':[717.133,716.035,716.542,716.517,716.592,716.446,716.739,717.539,717.234],
'z':[-46.593,-45.274,-45.463,-44.560,-43.284,-42.583,-42.087,-41.024,-39.614],
'diam':[1.320,0.706,0.706,0.706,0.706,0.706,0.706,0.706,0.706]
},
prefix + 'apic[73]': {
'x':[-382.134,-384.144,-385.221,-385.503,-386.609,-388.356,-389.217,-389.277],
'y':[726.717,727.360,728.561,729.123,729.959,730.043,731.161,731.679],
'z':[-40.431,-37.713,-36.462,-35.538,-34.327,-31.762,-30.401,-29.218],
'diam':[0.706,0.706,0.706,0.706,0.706,0.706,0.706,0.706]
},
prefix + 'apic[27]': {
'x':[-356.857,-356.708,-357.203,-357.340],
'y':[702.880,703.299,703.826,703.884],
'z':[-29.899,-28.605,-27.042,-25.850],
'diam':[0.442,0.442,0.442,0.442]
},
prefix + 'apic[23]': {
'x':[-362.344,-361.072,-360.172,-360.559,-359.839],
'y':[708.536,708.312,707.816,707.671,708.648],
'z':[-29.530,-28.825,-28.610,-27.247,-26.957],
'diam':[0.528,0.264,0.264,0.264,0.264]
},
prefix + 'apic[120]': {
'x':[-394.546,-395.049,-395.217,-395.218],
'y':[737.443,738.429,739.655,740.049],
'z':[-41.888,-40.244,-37.510,-36.531],
'diam':[0.528,0.528,0.613,0.613]
},
prefix + 'apic[80]': {
'x':[-396.249,-396.770,-398.279,-400.340,-401.841,-402.934,-404.005,-403.900,-405.902],
'y':[731.890,731.769,733.115,732.914,733.113,733.800,733.330,734.782,732.221],
'z':[-29.596,-30.740,-31.656,-31.900,-31.798,-31.216,-30.988,-32.214,-32.098],
'diam':[0.442,0.528,0.528,0.349,0.349,0.349,0.349,0.349,0.264]
},
prefix + 'apic[125]': {
'x':[-396.161,-396.692,-397.460,-397.899,-398.619],
'y':[740.635,741.256,742.070,742.393,742.819],
'z':[-35.501,-33.897,-33.109,-32.098,-31.263],
'diam':[0.528,0.528,0.528,0.528,0.528]
},
prefix + 'apic[140]': {
'x':[-392.313,-393.520,-394.433,-396.215,-397.872,-398.268,-400.003,-400.972,-401.942,-403.022,-403.964,-405.032,-405.625,-405.135,-405.528],
'y':[732.909,733.465,733.906,733.127,733.614,735.331,735.090,735.531,736.857,737.613,738.345,738.197,737.888,740.515,741.655],
'z':[-53.049,-53.616,-53.980,-53.947,-53.513,-53.518,-53.593,-54.213,-53.589,-53.466,-53.172,-52.276,-51.350,-51.238,-49.825],
'diam':[0.442,0.349,0.349,0.349,0.349,0.442,0.442,0.442,0.349,0.349,0.349,0.349,0.349,0.349,0.349]
},
prefix + 'apic[28]': {
'x':[-356.857,-356.919,-356.761],
'y':[702.880,701.549,701.058],
'z':[-29.899,-30.956,-32.674],
'diam':[0.442,0.442,0.442]
},
prefix + 'apic[79]': {
'x':[-389.277,-392.355,-393.562,-395.216,-396.249],
'y':[731.679,731.891,732.249,732.656,731.890],
'z':[-29.218,-29.900,-30.475,-29.766,-29.596],
'diam':[0.706,0.442,0.442,0.442,0.442]
},
prefix + 'apic[6]': {
'x':[-362.553,-361.157],
'y':[717.930,717.776],
'z':[-39.618,-40.571],
'diam':[0.442,0.264]
},
prefix + 'apic[58]': {
'x':[-368.533,-367.127,-367.500,-367.990,-366.772,-364.813,-363.144,-361.999,-360.753,-360.087,-359.239,-358.460,-356.936,-355.972],
'y':[725.556,724.702,725.159,725.813,725.501,724.931,724.064,723.631,723.152,723.091,721.588,721.156,720.129,718.758],
'z':[-29.616,-28.522,-27.381,-26.560,-25.220,-23.497,-21.118,-19.723,-18.596,-17.662,-17.243,-16.599,-16.381,-16.184],
'diam':[0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349,0.349]
},
prefix + 'apic[116]': {
'x':[-407.931,-408.947,-409.732,-410.750,-412.326,-413.177],
'y':[734.348,735.025,735.733,735.467,735.552,735.258],
'z':[-37.540,-37.480,-37.005,-36.943,-36.793,-37.324],
'diam':[0.179,0.179,0.179,0.179,0.179,0.179]
},
prefix + 'apic[40]': {
'x':[-372.570,-372.849,-373.328],
'y':[730.628,731.242,731.957],
'z':[-21.545,-20.472,-19.495],
'diam':[0.528,0.349,0.349]
},
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| true | true |
f725a28db4c34f0091289d14f2db993acdf9d82d | 9,405 | py | Python | core/addons/api/__init__.py | photos-network/core | dae64af83dccfa37fc0923370072a360da941c28 | [
"Apache-2.0"
] | 4 | 2020-12-30T12:48:46.000Z | 2022-03-29T17:44:00.000Z | core/addons/api/__init__.py | photos-network/core | dae64af83dccfa37fc0923370072a360da941c28 | [
"Apache-2.0"
] | null | null | null | core/addons/api/__init__.py | photos-network/core | dae64af83dccfa37fc0923370072a360da941c28 | [
"Apache-2.0"
] | 2 | 2021-01-24T19:13:53.000Z | 2021-11-02T16:43:25.000Z | """REST API implementation."""
import json
import logging
import os
import pathlib
from datetime import datetime
from aiohttp import web
from core.addons.api.dto.details import Details
from core.addons.api.dto.location import Location
from core.addons.api.dto.photo import PhotoDetailsResponse, PhotoEncoder, PhotoResponse
from core.addons.api.dto.photo_response import PhotosResponse
from core.base import Session
from core.core import ApplicationCore
from core.persistency.dto.photo import Photo
from core.webserver.request import KEY_USER_ID, RequestView
from core.webserver.status import HTTP_CREATED, HTTP_OK
_LOGGER = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG)
async def async_setup(core: ApplicationCore, config: dict) -> bool:
"""Set up addon to core application."""
if config is not None and "cors" in config:
is_cors_enabled = config["cors"]
else:
is_cors_enabled = False
_LOGGER.debug(f"enable cors: {is_cors_enabled}")
core.http.register_request(APIStatusView())
core.http.register_request(PhotosView())
core.http.register_request(PhotoDetailsView())
core.http.register_request(PhotoView())
core.http.register_request(AlbumView())
return True
class APIStatusView(RequestView):
"""View to handle Status requests."""
requires_auth = False
url = "/api"
name = "api:status"
async def get(self, core: ApplicationCore, request: web.Request):
"""Retrieve if API is running."""
return self.json_message("API running.")
async def head(self, core: ApplicationCore, request: web.Request):
"""Retrieve if API is running."""
return self.json_message("API running.")
class PhotosView(RequestView):
"""View to handle photos requests."""
url = "/api/photos"
name = "api:photo"
async def get(self, core: ApplicationCore, request: web.Request) -> web.Response:
"""Get a list of all photo resources."""
_LOGGER.debug("GET /api/photos")
await core.authentication.check_permission(request, "library:read")
user_id = await core.http.get_user_id(request)
if user_id is None:
raise web.HTTPForbidden()
limit = 50
if "limit" in request.query:
limit = int(request.query["limit"])
offset = 0
if "offset" in request.query:
offset = int(request.query["offset"])
_LOGGER.debug(
f"read {limit} photos for user_id {user_id} beginning with {offset}"
)
user_photos = await core.storage.read_photos(user_id, offset, limit)
results = []
_LOGGER.debug(f"found {len(user_photos)} photos")
for photo in user_photos:
results.append(
PhotoResponse(
id=photo.uuid,
name=photo.filename,
image_url=f"{core.config.external_url}:{core.config.port}/api/file/{photo.uuid}",
date_added=photo.date_added,
date_taken=photo.date_taken,
)
)
response = PhotosResponse(
offset=offset, limit=limit, size=len(results), results=results
)
return web.Response(
text=json.dumps(response, cls=PhotoEncoder), content_type="application/json"
)
class PhotoDetailsView(RequestView):
"""View to handle single photo requests."""
url = "/api/photo/{entity_id}"
name = "api:photo"
async def get(
self, core: ApplicationCore, request: web.Request, entity_id: str
) -> web.Response:
"""Return an entity."""
_LOGGER.debug(f"GET /api/photo/{entity_id}")
# TODO: add user_id to check if user has access to image
photo = await core.storage.read_photo(entity_id)
if photo is None:
raise web.HTTPNotFound
# photo owner
# TODO: get first-/lastname of owner
# owner = await core.authentication
_LOGGER.debug(f"photo {photo.uuid}")
file = os.path.join(photo.directory, photo.filename)
_LOGGER.debug(f"get additional data for {file} / {os.path.exists(file)}")
# photo creation time
fname = pathlib.Path(file)
mtime = datetime.fromtimestamp(fname.stat().st_mtime)
ctime = datetime.fromtimestamp(fname.stat().st_ctime)
# photo location
location = None
latitude = await core.storage.read("latitude")
longitude = await core.storage.read("longitude")
if latitude is not None and longitude is not None:
altitude = await core.storage.read("altitude")
if altitude is not None:
location = Location(
latitude=latitude, longitude=longitude, altitude=altitude
)
else:
location = Location(
latitude=latitude, longitude=longitude, altitude="0.0"
)
# photo tags
tags = await core.storage.read("tags")
result = PhotoDetailsResponse(
id=photo.uuid,
name=photo.filename,
owner=photo.owner,
created_at=ctime.isoformat(),
modified_at=mtime.isoformat(),
details=Details(
camera="Nikon Z7",
lens="Nikkor 200mm F1.8",
focal_length="200",
iso="400",
shutter_speed="1/2000",
aperture="4.0",
),
tags=tags,
location=location,
image_url=f"{core.config.external_url}/api/file/{entity_id}",
)
return web.Response(
text=json.dumps(result, cls=PhotoEncoder), content_type="application/json"
)
class PhotoView(RequestView):
"""View to handle photo file requests."""
requires_auth = True
url = "/api/file/{entity_id}"
name = "api:file"
async def get(
self, core: ApplicationCore, request: web.Request, entity_id: str
) -> web.Response:
"""Return an entity."""
_LOGGER.debug(f"GET /api/file/{entity_id}")
# TODO: parse params max-with / max-height =wmax-width-hmax-height (=w2048-h1024)
# -wmax-width (preserving the aspect ratio)
# -hmax-height (preserving the aspect ratio)
# -c crop images to max-width / max-height
# -d remove exif data
result = Session.query(Photo).filter(Photo.uuid == entity_id).first()
if result:
file = os.path.join(result.directory, result.filename)
if os.path.exists(os.path.join(file)):
return web.FileResponse(path=file, status=200)
else:
raise web.HTTPNotFound()
else:
raise web.HTTPNotFound()
async def post(self, core: ApplicationCore, request: web.Request) -> web.Response:
"""Upload new photo resource."""
user = request[KEY_USER_ID]
reader = await request.multipart()
field = await reader.next()
assert field.name == "data"
original_filename = field.filename
_LOGGER.warning(f"request: {original_filename}")
# TODO: get storage directory for user
path = os.path.join(f"./data/users/{user}/", original_filename)
if os.path.exists(path):
# TODO: handle filename already exists
_LOGGER.warning(f"file already exists! {path}")
filename = original_filename
size = 0
with open(os.path.join(f"./data/users/{user}/", filename), "wb") as f:
while True:
chunk = await field.read_chunk() # 8192 bytes by default.
if not chunk:
break
size += len(chunk)
f.write(chunk)
_LOGGER.error(f"added {filename} to data directory of {user}.")
# TODO: add new filepath to database
# TODO: return unique_id from database
new_entity_id = 123456
new_entity_created = True
status_code = HTTP_CREATED if new_entity_created else HTTP_OK
resp = self.json_message(
f"File successfully added with ID: {new_entity_id}", status_code
)
resp.headers.add("Location", f"/api/photo/{new_entity_id}")
return resp
async def delete(self, core: ApplicationCore, request: web.Request, entity_id: str):
"""Delete an entity."""
_LOGGER.debug(f"DELETE /api/file/{entity_id}")
# TODO: delete entity
return self.json_message(f"return DELETE {entity_id}")
class AlbumsView(RequestView):
"""View to handle albums requests."""
requires_auth = False
url = "/api/album/"
name = "api:albums"
async def get(self, request: web.Request) -> web.Response:
"""Retrieve if API is running."""
return self.json_message("return Albums")
class AlbumView(RequestView):
"""View to handle single album requests."""
requires_auth = False
url = "/api/album/{entity_id}"
name = "api:album"
async def get(self, request: web.Request, entity_id) -> web.Response:
"""Retrieve if API is running."""
return self.json_message(f"return Album {entity_id}")
async def post(self, request: web.Request) -> web.Response:
"""Create a new album."""
return self.json_message("create a new Album")
| 32.543253 | 101 | 0.612547 | import json
import logging
import os
import pathlib
from datetime import datetime
from aiohttp import web
from core.addons.api.dto.details import Details
from core.addons.api.dto.location import Location
from core.addons.api.dto.photo import PhotoDetailsResponse, PhotoEncoder, PhotoResponse
from core.addons.api.dto.photo_response import PhotosResponse
from core.base import Session
from core.core import ApplicationCore
from core.persistency.dto.photo import Photo
from core.webserver.request import KEY_USER_ID, RequestView
from core.webserver.status import HTTP_CREATED, HTTP_OK
_LOGGER = logging.getLogger(__name__)
logging.basicConfig(level=logging.DEBUG)
async def async_setup(core: ApplicationCore, config: dict) -> bool:
if config is not None and "cors" in config:
is_cors_enabled = config["cors"]
else:
is_cors_enabled = False
_LOGGER.debug(f"enable cors: {is_cors_enabled}")
core.http.register_request(APIStatusView())
core.http.register_request(PhotosView())
core.http.register_request(PhotoDetailsView())
core.http.register_request(PhotoView())
core.http.register_request(AlbumView())
return True
class APIStatusView(RequestView):
requires_auth = False
url = "/api"
name = "api:status"
async def get(self, core: ApplicationCore, request: web.Request):
return self.json_message("API running.")
async def head(self, core: ApplicationCore, request: web.Request):
return self.json_message("API running.")
class PhotosView(RequestView):
url = "/api/photos"
name = "api:photo"
async def get(self, core: ApplicationCore, request: web.Request) -> web.Response:
_LOGGER.debug("GET /api/photos")
await core.authentication.check_permission(request, "library:read")
user_id = await core.http.get_user_id(request)
if user_id is None:
raise web.HTTPForbidden()
limit = 50
if "limit" in request.query:
limit = int(request.query["limit"])
offset = 0
if "offset" in request.query:
offset = int(request.query["offset"])
_LOGGER.debug(
f"read {limit} photos for user_id {user_id} beginning with {offset}"
)
user_photos = await core.storage.read_photos(user_id, offset, limit)
results = []
_LOGGER.debug(f"found {len(user_photos)} photos")
for photo in user_photos:
results.append(
PhotoResponse(
id=photo.uuid,
name=photo.filename,
image_url=f"{core.config.external_url}:{core.config.port}/api/file/{photo.uuid}",
date_added=photo.date_added,
date_taken=photo.date_taken,
)
)
response = PhotosResponse(
offset=offset, limit=limit, size=len(results), results=results
)
return web.Response(
text=json.dumps(response, cls=PhotoEncoder), content_type="application/json"
)
class PhotoDetailsView(RequestView):
url = "/api/photo/{entity_id}"
name = "api:photo"
async def get(
self, core: ApplicationCore, request: web.Request, entity_id: str
) -> web.Response:
_LOGGER.debug(f"GET /api/photo/{entity_id}")
photo = await core.storage.read_photo(entity_id)
if photo is None:
raise web.HTTPNotFound
_LOGGER.debug(f"photo {photo.uuid}")
file = os.path.join(photo.directory, photo.filename)
_LOGGER.debug(f"get additional data for {file} / {os.path.exists(file)}")
fname = pathlib.Path(file)
mtime = datetime.fromtimestamp(fname.stat().st_mtime)
ctime = datetime.fromtimestamp(fname.stat().st_ctime)
location = None
latitude = await core.storage.read("latitude")
longitude = await core.storage.read("longitude")
if latitude is not None and longitude is not None:
altitude = await core.storage.read("altitude")
if altitude is not None:
location = Location(
latitude=latitude, longitude=longitude, altitude=altitude
)
else:
location = Location(
latitude=latitude, longitude=longitude, altitude="0.0"
)
tags = await core.storage.read("tags")
result = PhotoDetailsResponse(
id=photo.uuid,
name=photo.filename,
owner=photo.owner,
created_at=ctime.isoformat(),
modified_at=mtime.isoformat(),
details=Details(
camera="Nikon Z7",
lens="Nikkor 200mm F1.8",
focal_length="200",
iso="400",
shutter_speed="1/2000",
aperture="4.0",
),
tags=tags,
location=location,
image_url=f"{core.config.external_url}/api/file/{entity_id}",
)
return web.Response(
text=json.dumps(result, cls=PhotoEncoder), content_type="application/json"
)
class PhotoView(RequestView):
requires_auth = True
url = "/api/file/{entity_id}"
name = "api:file"
async def get(
self, core: ApplicationCore, request: web.Request, entity_id: str
) -> web.Response:
_LOGGER.debug(f"GET /api/file/{entity_id}")
result = Session.query(Photo).filter(Photo.uuid == entity_id).first()
if result:
file = os.path.join(result.directory, result.filename)
if os.path.exists(os.path.join(file)):
return web.FileResponse(path=file, status=200)
else:
raise web.HTTPNotFound()
else:
raise web.HTTPNotFound()
async def post(self, core: ApplicationCore, request: web.Request) -> web.Response:
user = request[KEY_USER_ID]
reader = await request.multipart()
field = await reader.next()
assert field.name == "data"
original_filename = field.filename
_LOGGER.warning(f"request: {original_filename}")
path = os.path.join(f"./data/users/{user}/", original_filename)
if os.path.exists(path):
_LOGGER.warning(f"file already exists! {path}")
filename = original_filename
size = 0
with open(os.path.join(f"./data/users/{user}/", filename), "wb") as f:
while True:
chunk = await field.read_chunk()
if not chunk:
break
size += len(chunk)
f.write(chunk)
_LOGGER.error(f"added {filename} to data directory of {user}.")
new_entity_id = 123456
new_entity_created = True
status_code = HTTP_CREATED if new_entity_created else HTTP_OK
resp = self.json_message(
f"File successfully added with ID: {new_entity_id}", status_code
)
resp.headers.add("Location", f"/api/photo/{new_entity_id}")
return resp
async def delete(self, core: ApplicationCore, request: web.Request, entity_id: str):
_LOGGER.debug(f"DELETE /api/file/{entity_id}")
return self.json_message(f"return DELETE {entity_id}")
class AlbumsView(RequestView):
requires_auth = False
url = "/api/album/"
name = "api:albums"
async def get(self, request: web.Request) -> web.Response:
return self.json_message("return Albums")
class AlbumView(RequestView):
requires_auth = False
url = "/api/album/{entity_id}"
name = "api:album"
async def get(self, request: web.Request, entity_id) -> web.Response:
return self.json_message(f"return Album {entity_id}")
async def post(self, request: web.Request) -> web.Response:
return self.json_message("create a new Album")
| true | true |
f725a39885307cc168891a50213872ddbdd21689 | 1,475 | py | Python | _unittests/ut_module/test_code_style.py | sdpython/papierstat | f69de884c59ada30b58224dca39f2a44d92122c1 | [
"MIT"
] | 7 | 2019-03-21T09:52:31.000Z | 2021-01-17T16:56:27.000Z | _unittests/ut_module/test_code_style.py | sdpython/papierstat | f69de884c59ada30b58224dca39f2a44d92122c1 | [
"MIT"
] | 33 | 2018-02-08T23:56:57.000Z | 2021-02-10T23:55:43.000Z | _unittests/ut_module/test_code_style.py | sdpython/papierstat | f69de884c59ada30b58224dca39f2a44d92122c1 | [
"MIT"
] | 1 | 2021-02-11T09:16:33.000Z | 2021-02-11T09:16:33.000Z | """
@brief test log(time=150s)
"""
import os
import unittest
from pyquickhelper.loghelper import fLOG
from pyquickhelper.pycode import check_pep8, ExtTestCase
class TestCodeStyle(ExtTestCase):
"""Test style."""
def test_style_src(self):
thi = os.path.abspath(os.path.dirname(__file__))
src_ = os.path.normpath(os.path.join(thi, "..", "..", "src"))
check_pep8(src_, fLOG=fLOG,
pylint_ignore=('C0103', 'C1801', 'R0201', 'R1705', 'W0108', 'W0613',
'C0111', 'W0223', 'W0201', 'W0212', 'C0415', 'C0209'),
skip=["Parameters differ from overridden 'fit' method",
"Module 'numpy.random' has no 'RandomState' member",
"Instance of 'SkLearnParameters' has no '",
" in module 'sklearn.cluster._k_means'",
"Instance of 'Struct' has no '",
])
def test_style_test(self):
thi = os.path.abspath(os.path.dirname(__file__))
test = os.path.normpath(os.path.join(thi, "..", ))
check_pep8(test, fLOG=fLOG, neg_pattern="temp_.*",
pylint_ignore=('C0103', 'C1801', 'R0201', 'R1705', 'W0108', 'W0613',
'C0111', 'W0612', 'E0632', 'C0415', 'C0209'),
skip=["Instance of 'tuple' has no ",
])
if __name__ == "__main__":
unittest.main()
| 38.815789 | 88 | 0.520678 | import os
import unittest
from pyquickhelper.loghelper import fLOG
from pyquickhelper.pycode import check_pep8, ExtTestCase
class TestCodeStyle(ExtTestCase):
def test_style_src(self):
thi = os.path.abspath(os.path.dirname(__file__))
src_ = os.path.normpath(os.path.join(thi, "..", "..", "src"))
check_pep8(src_, fLOG=fLOG,
pylint_ignore=('C0103', 'C1801', 'R0201', 'R1705', 'W0108', 'W0613',
'C0111', 'W0223', 'W0201', 'W0212', 'C0415', 'C0209'),
skip=["Parameters differ from overridden 'fit' method",
"Module 'numpy.random' has no 'RandomState' member",
"Instance of 'SkLearnParameters' has no '",
" in module 'sklearn.cluster._k_means'",
"Instance of 'Struct' has no '",
])
def test_style_test(self):
thi = os.path.abspath(os.path.dirname(__file__))
test = os.path.normpath(os.path.join(thi, "..", ))
check_pep8(test, fLOG=fLOG, neg_pattern="temp_.*",
pylint_ignore=('C0103', 'C1801', 'R0201', 'R1705', 'W0108', 'W0613',
'C0111', 'W0612', 'E0632', 'C0415', 'C0209'),
skip=["Instance of 'tuple' has no ",
])
if __name__ == "__main__":
unittest.main()
| true | true |
f725a3fd1b003a319b8f8190e60431bedbbcf49d | 1,698 | py | Python | src/LossFunctions/ActionCrossEntropy.py | Marcel-Rodekamp/MLP | 349ac8e10679e2ec53980908c580902996a493e7 | [
"MIT"
] | 1 | 2021-06-15T09:01:09.000Z | 2021-06-15T09:01:09.000Z | src/LossFunctions/ActionCrossEntropy.py | Marcel-Rodekamp/MLP | 349ac8e10679e2ec53980908c580902996a493e7 | [
"MIT"
] | null | null | null | src/LossFunctions/ActionCrossEntropy.py | Marcel-Rodekamp/MLP | 349ac8e10679e2ec53980908c580902996a493e7 | [
"MIT"
] | null | null | null | import torch
class ActionCrossEntropyFunction(torch.autograd.Function):
@staticmethod
def forward(self,input,target,action,force = None):
self.mb_size,self.dim = input.size()
# save the force for backward
self.force = force
# get action difference
action_input = torch.tensor( [action(input[i_mb,:].numpy()) for i_mb in range(self.mb_size)], dtype = input.dtype )
action_target = torch.tensor( [action(target[i_mb,:].numpy()) for i_mb in range(self.mb_size)], dtype = input.dtype )
output = action_target * action_input.log() + (1-action_target) * (1-action_input).log()
self.save_for_backward(input,action_input,action_target,output)
output = torch.sqrt(output.real**2+output.imag**2)
# average over batch and return
output = torch.mean(output)
return output
@staticmethod
def backward(self,grad_output):
input,action_input,action_target,cross_entropy = self.saved_tensors
action_grad_input = -torch.tensor( [self.force(input[i_mb,:].numpy()) for i_mb in range(self.mb_size)], dtype = input.dtype )
grad = torch.unsqueeze( cross_entropy.conj()*((action_target)/(action_input) + (1-action_target)/(1-action_input)), -1 ) * action_grad_input
return grad.conj(),None,None,None
ActionCrossEntropyFunc = ActionCrossEntropyFunction.apply
class ActionCrossEntropy(torch.nn.Module):
def __init__(self,action):
super(ActionCrossEntropy, self).__init__()
self.action = action.eval
self.force = action.force
def forward(self, input, target):
return ActionCrossEntropyFunc(input,target,self.action,self.force)
| 36.913043 | 148 | 0.689046 | import torch
class ActionCrossEntropyFunction(torch.autograd.Function):
@staticmethod
def forward(self,input,target,action,force = None):
self.mb_size,self.dim = input.size()
self.force = force
action_input = torch.tensor( [action(input[i_mb,:].numpy()) for i_mb in range(self.mb_size)], dtype = input.dtype )
action_target = torch.tensor( [action(target[i_mb,:].numpy()) for i_mb in range(self.mb_size)], dtype = input.dtype )
output = action_target * action_input.log() + (1-action_target) * (1-action_input).log()
self.save_for_backward(input,action_input,action_target,output)
output = torch.sqrt(output.real**2+output.imag**2)
output = torch.mean(output)
return output
@staticmethod
def backward(self,grad_output):
input,action_input,action_target,cross_entropy = self.saved_tensors
action_grad_input = -torch.tensor( [self.force(input[i_mb,:].numpy()) for i_mb in range(self.mb_size)], dtype = input.dtype )
grad = torch.unsqueeze( cross_entropy.conj()*((action_target)/(action_input) + (1-action_target)/(1-action_input)), -1 ) * action_grad_input
return grad.conj(),None,None,None
ActionCrossEntropyFunc = ActionCrossEntropyFunction.apply
class ActionCrossEntropy(torch.nn.Module):
def __init__(self,action):
super(ActionCrossEntropy, self).__init__()
self.action = action.eval
self.force = action.force
def forward(self, input, target):
return ActionCrossEntropyFunc(input,target,self.action,self.force)
| true | true |
f725a5330ea476bf3d80b1dcb996e9b5875e2f37 | 918 | py | Python | setup.py | NgHoangDat/lescode | f19d8f2ca47aee947d2b2d88e5008ce4a58faeb6 | [
"MIT"
] | 1 | 2020-06-17T03:33:58.000Z | 2020-06-17T03:33:58.000Z | setup.py | NgHoangDat/lescode | f19d8f2ca47aee947d2b2d88e5008ce4a58faeb6 | [
"MIT"
] | null | null | null | setup.py | NgHoangDat/lescode | f19d8f2ca47aee947d2b2d88e5008ce4a58faeb6 | [
"MIT"
] | null | null | null | import setuptools
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
__VERSION__ = "0.2.10"
setuptools.setup(
name="lescode",
packages=setuptools.find_packages(),
version=__VERSION__,
author="nghoangdat",
author_email="18.hoang.dat.12@gmail.com",
description="quiver",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/NgHoangDat/lescode.git",
download_url=f"https://github.com/NgHoangDat/lescode/archive/v{__VERSION__}.tar.gz",
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
install_requires=[
'dataclasses; python_version=="3.6"',
'msgpack',
'python-dateutil',
'pyyaml',
'toolz'
]
) | 27 | 88 | 0.641612 | import setuptools
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
__VERSION__ = "0.2.10"
setuptools.setup(
name="lescode",
packages=setuptools.find_packages(),
version=__VERSION__,
author="nghoangdat",
author_email="18.hoang.dat.12@gmail.com",
description="quiver",
long_description=long_description,
long_description_content_type="text/markdown",
url="https://github.com/NgHoangDat/lescode.git",
download_url=f"https://github.com/NgHoangDat/lescode/archive/v{__VERSION__}.tar.gz",
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
python_requires='>=3.6',
install_requires=[
'dataclasses; python_version=="3.6"',
'msgpack',
'python-dateutil',
'pyyaml',
'toolz'
]
) | true | true |
f725a7c0a43b428ee68f9ed16d64cc59f6ac188b | 11,328 | py | Python | haystack/utils/squad_data.py | ArzelaAscoIi/haystack | be8f50c9e3de4e264b3f345f5f4b9c9ec518ed08 | [
"Apache-2.0"
] | 1 | 2022-02-15T04:32:41.000Z | 2022-02-15T04:32:41.000Z | haystack/utils/squad_data.py | ArzelaAscoIi/haystack | be8f50c9e3de4e264b3f345f5f4b9c9ec518ed08 | [
"Apache-2.0"
] | null | null | null | haystack/utils/squad_data.py | ArzelaAscoIi/haystack | be8f50c9e3de4e264b3f345f5f4b9c9ec518ed08 | [
"Apache-2.0"
] | null | null | null | from typing import List
import logging
import json
import random
import pandas as pd
from tqdm import tqdm
from haystack.schema import Document, Label
from haystack.modeling.data_handler.processor import _read_squad_file
logging.basicConfig()
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
tqdm.pandas()
COLUMN_NAMES = ["title", "context", "question", "id", "answer_text", "answer_start", "is_impossible"]
class SquadData:
"""
This class is designed to manipulate data that is in SQuAD format
"""
def __init__(self, squad_data):
"""
:param squad_data: SQuAD format data, either as a dict with a `data` key, or just a list of SQuAD documents
"""
if type(squad_data) == dict:
self.version = squad_data.get("version")
self.data = squad_data["data"]
elif type(squad_data) == list:
self.version = None
self.data = squad_data
self.df = self.to_df(self.data)
def merge_from_file(self, filename: str):
"""Merge the contents of a SQuAD format json file with the data stored in this object"""
new_data = json.load(open(filename))["data"]
self.merge(new_data)
def merge(self, new_data: List):
"""
Merge data in SQuAD format with the data stored in this object
:param new_data: A list of SQuAD document data
"""
df_new = self.to_df(new_data)
self.df = pd.concat([df_new, self.df])
self.data = self.df_to_data(self.df)
@classmethod
def from_file(cls, filename: str):
"""
Create a SquadData object by providing the name of a SQuAD format json file
"""
data = json.load(open(filename))
return cls(data)
def save(self, filename: str):
"""
Write the data stored in this object to a json file.
"""
with open(filename, "w") as f:
squad_data = {"version": self.version, "data": self.data}
json.dump(squad_data, f, indent=2)
def to_dpr_dataset(self):
raise NotImplementedError(
"SquadData.to_dpr_dataset() not yet implemented. "
"For now, have a look at the script at haystack/retriever/squad_to_dpr.py"
)
def to_document_objs(self):
"""
Export all paragraphs stored in this object to haystack.Document objects.
"""
df_docs = self.df[["title", "context"]]
df_docs = df_docs.drop_duplicates()
record_dicts = df_docs.to_dict("records")
documents = [Document(content=rd["context"], id=rd["title"]) for rd in record_dicts]
return documents
# TODO refactor to new Label objects
def to_label_objs(self):
"""
Export all labels stored in this object to haystack.Label objects.
"""
df_labels = self.df[["id", "question", "answer_text", "answer_start"]]
record_dicts = df_labels.to_dict("records")
labels = [
Label(
query=rd["question"],
answer=rd["answer_text"],
is_correct_answer=True,
is_correct_document=True,
id=rd["id"],
origin=rd.get("origin", "SquadData tool"),
document_id=rd.get("document_id", None),
)
for rd in record_dicts
]
return labels
@staticmethod
def to_df(data):
"""Convert a list of SQuAD document dictionaries into a pandas dataframe (each row is one annotation)"""
flat = []
for document in data:
title = document["title"]
for paragraph in document["paragraphs"]:
context = paragraph["context"]
for question in paragraph["qas"]:
q = question["question"]
id = question["id"]
is_impossible = question["is_impossible"]
# For no_answer samples
if len(question["answers"]) == 0:
flat.append(
{
"title": title,
"context": context,
"question": q,
"id": id,
"answer_text": "",
"answer_start": None,
"is_impossible": is_impossible,
}
)
# For span answer samples
else:
for answer in question["answers"]:
answer_text = answer["text"]
answer_start = answer["answer_start"]
flat.append(
{
"title": title,
"context": context,
"question": q,
"id": id,
"answer_text": answer_text,
"answer_start": answer_start,
"is_impossible": is_impossible,
}
)
df = pd.DataFrame.from_records(flat)
return df
def count(self, unit="questions"):
"""
Count the samples in the data. Choose from unit = "paragraphs", "questions", "answers", "no_answers", "span_answers"
"""
c = 0
for document in self.data:
for paragraph in document["paragraphs"]:
if unit == "paragraphs":
c += 1
for question in paragraph["qas"]:
if unit == "questions":
c += 1
# Count no_answers
if len(question["answers"]) == 0:
if unit in ["answers", "no_answers"]:
c += 1
# Count span answers
else:
for answer in question["answers"]:
if unit in ["answers", "span_answers"]:
c += 1
return c
@classmethod
def df_to_data(cls, df):
"""
Convert a dataframe into SQuAD format data (list of SQuAD document dictionaries).
"""
logger.info("Converting data frame to squad format data")
# Aggregate the answers of each question
logger.info("Aggregating the answers of each question")
df_grouped_answers = df.groupby(["title", "context", "question", "id", "is_impossible"])
df_aggregated_answers = (
df[["title", "context", "question", "id", "is_impossible"]].drop_duplicates().reset_index()
)
answers = df_grouped_answers.progress_apply(cls._aggregate_answers).rename("answers")
answers = pd.DataFrame(answers).reset_index()
df_aggregated_answers = pd.merge(df_aggregated_answers, answers)
# Aggregate the questions of each passage
logger.info("Aggregating the questions of each paragraphs of each document")
df_grouped_questions = df_aggregated_answers.groupby(["title", "context"])
df_aggregated_questions = df[["title", "context"]].drop_duplicates().reset_index()
questions = df_grouped_questions.progress_apply(cls._aggregate_questions).rename("qas")
questions = pd.DataFrame(questions).reset_index()
df_aggregated_questions = pd.merge(df_aggregated_questions, questions)
logger.info("Aggregating the paragraphs of each document")
df_grouped_paragraphs = df_aggregated_questions.groupby(["title"])
df_aggregated_paragraphs = df[["title"]].drop_duplicates().reset_index()
paragraphs = df_grouped_paragraphs.progress_apply(cls._aggregate_passages).rename("paragraphs")
paragraphs = pd.DataFrame(paragraphs).reset_index()
df_aggregated_paragraphs = pd.merge(df_aggregated_paragraphs, paragraphs)
df_aggregated_paragraphs = df_aggregated_paragraphs[["title", "paragraphs"]]
ret = df_aggregated_paragraphs.to_dict("records")
return ret
@staticmethod
def _aggregate_passages(x):
x = x[["context", "qas"]]
ret = x.to_dict("records")
return ret
@staticmethod
def _aggregate_questions(x):
x = x[["question", "id", "answers", "is_impossible"]]
ret = x.to_dict("records")
return ret
@staticmethod
def _aggregate_answers(x):
x = x[["answer_text", "answer_start"]]
x = x.rename(columns={"answer_text": "text"})
# Span anwser
try:
x["answer_start"] = x["answer_start"].astype(int)
ret = x.to_dict("records")
# No answer
except ValueError:
ret = []
return ret
def set_data(self, data):
self.data = data
self.df = self.to_df(data)
def sample_questions(self, n):
"""
Return a sample of n questions in SQuAD format (list of SQuAD document dictionaries)
Note, that if the same question is asked on multiple different passages, this fn treats that
as a single question
"""
all_questions = self.get_all_questions()
sampled_questions = random.sample(all_questions, n)
df_sampled = self.df[self.df["question"].isin(sampled_questions)]
return self.df_to_data(df_sampled)
def get_all_paragraphs(self):
"""
Return all paragraph strings.
"""
return self.df["context"].unique().tolist()
def get_all_questions(self):
"""
Return all question strings. Note that if the same question appears for different paragraphs, it will be
returned multiple times by this fn
"""
df_questions = self.df[["title", "context", "question"]]
df_questions = df_questions.drop_duplicates()
questions = df_questions["question"].tolist()
return questions
def get_all_document_titles(self):
"""Return all document title strings"""
return self.df["title"].unique().tolist()
if __name__ == "__main__":
# Download the SQuAD dataset if it isn't at target directory
_read_squad_file("../data/squad20/train-v2.0.json")
filename1 = "../data/squad20/train-v2.0.json"
filename2 = "../data/squad20/dev-v2.0.json"
# Load file1 and take a sample of 10000 questions
sd = SquadData.from_file(filename1)
sample1 = sd.sample_questions(n=10000)
# Set sd to now contain the sample of 10000 questions
sd.set_data(sample1)
# Merge sd with file2 and take a sample of 100 questions
sd.merge_from_file(filename2)
sample2 = sd.sample_questions(n=100)
sd.set_data(sample2)
# Save this sample of 100
sd.save("../data/squad20/sample.json")
paragraphs = sd.get_all_paragraphs()
questions = sd.get_all_questions()
titles = sd.get_all_document_titles()
documents = sd.to_document_objs()
labels = sd.to_label_objs()
n_qs = sd.count(unit="questions")
n_as = sd.count(unit="no_answers")
n_ps = sd.count(unit="paragraphs")
print(n_qs)
print(n_as)
print(n_ps)
| 36.779221 | 124 | 0.566561 | from typing import List
import logging
import json
import random
import pandas as pd
from tqdm import tqdm
from haystack.schema import Document, Label
from haystack.modeling.data_handler.processor import _read_squad_file
logging.basicConfig()
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
tqdm.pandas()
COLUMN_NAMES = ["title", "context", "question", "id", "answer_text", "answer_start", "is_impossible"]
class SquadData:
def __init__(self, squad_data):
if type(squad_data) == dict:
self.version = squad_data.get("version")
self.data = squad_data["data"]
elif type(squad_data) == list:
self.version = None
self.data = squad_data
self.df = self.to_df(self.data)
def merge_from_file(self, filename: str):
new_data = json.load(open(filename))["data"]
self.merge(new_data)
def merge(self, new_data: List):
df_new = self.to_df(new_data)
self.df = pd.concat([df_new, self.df])
self.data = self.df_to_data(self.df)
@classmethod
def from_file(cls, filename: str):
data = json.load(open(filename))
return cls(data)
def save(self, filename: str):
with open(filename, "w") as f:
squad_data = {"version": self.version, "data": self.data}
json.dump(squad_data, f, indent=2)
def to_dpr_dataset(self):
raise NotImplementedError(
"SquadData.to_dpr_dataset() not yet implemented. "
"For now, have a look at the script at haystack/retriever/squad_to_dpr.py"
)
def to_document_objs(self):
df_docs = self.df[["title", "context"]]
df_docs = df_docs.drop_duplicates()
record_dicts = df_docs.to_dict("records")
documents = [Document(content=rd["context"], id=rd["title"]) for rd in record_dicts]
return documents
def to_label_objs(self):
df_labels = self.df[["id", "question", "answer_text", "answer_start"]]
record_dicts = df_labels.to_dict("records")
labels = [
Label(
query=rd["question"],
answer=rd["answer_text"],
is_correct_answer=True,
is_correct_document=True,
id=rd["id"],
origin=rd.get("origin", "SquadData tool"),
document_id=rd.get("document_id", None),
)
for rd in record_dicts
]
return labels
@staticmethod
def to_df(data):
flat = []
for document in data:
title = document["title"]
for paragraph in document["paragraphs"]:
context = paragraph["context"]
for question in paragraph["qas"]:
q = question["question"]
id = question["id"]
is_impossible = question["is_impossible"]
if len(question["answers"]) == 0:
flat.append(
{
"title": title,
"context": context,
"question": q,
"id": id,
"answer_text": "",
"answer_start": None,
"is_impossible": is_impossible,
}
)
else:
for answer in question["answers"]:
answer_text = answer["text"]
answer_start = answer["answer_start"]
flat.append(
{
"title": title,
"context": context,
"question": q,
"id": id,
"answer_text": answer_text,
"answer_start": answer_start,
"is_impossible": is_impossible,
}
)
df = pd.DataFrame.from_records(flat)
return df
def count(self, unit="questions"):
c = 0
for document in self.data:
for paragraph in document["paragraphs"]:
if unit == "paragraphs":
c += 1
for question in paragraph["qas"]:
if unit == "questions":
c += 1
if len(question["answers"]) == 0:
if unit in ["answers", "no_answers"]:
c += 1
else:
for answer in question["answers"]:
if unit in ["answers", "span_answers"]:
c += 1
return c
@classmethod
def df_to_data(cls, df):
logger.info("Converting data frame to squad format data")
logger.info("Aggregating the answers of each question")
df_grouped_answers = df.groupby(["title", "context", "question", "id", "is_impossible"])
df_aggregated_answers = (
df[["title", "context", "question", "id", "is_impossible"]].drop_duplicates().reset_index()
)
answers = df_grouped_answers.progress_apply(cls._aggregate_answers).rename("answers")
answers = pd.DataFrame(answers).reset_index()
df_aggregated_answers = pd.merge(df_aggregated_answers, answers)
logger.info("Aggregating the questions of each paragraphs of each document")
df_grouped_questions = df_aggregated_answers.groupby(["title", "context"])
df_aggregated_questions = df[["title", "context"]].drop_duplicates().reset_index()
questions = df_grouped_questions.progress_apply(cls._aggregate_questions).rename("qas")
questions = pd.DataFrame(questions).reset_index()
df_aggregated_questions = pd.merge(df_aggregated_questions, questions)
logger.info("Aggregating the paragraphs of each document")
df_grouped_paragraphs = df_aggregated_questions.groupby(["title"])
df_aggregated_paragraphs = df[["title"]].drop_duplicates().reset_index()
paragraphs = df_grouped_paragraphs.progress_apply(cls._aggregate_passages).rename("paragraphs")
paragraphs = pd.DataFrame(paragraphs).reset_index()
df_aggregated_paragraphs = pd.merge(df_aggregated_paragraphs, paragraphs)
df_aggregated_paragraphs = df_aggregated_paragraphs[["title", "paragraphs"]]
ret = df_aggregated_paragraphs.to_dict("records")
return ret
@staticmethod
def _aggregate_passages(x):
x = x[["context", "qas"]]
ret = x.to_dict("records")
return ret
@staticmethod
def _aggregate_questions(x):
x = x[["question", "id", "answers", "is_impossible"]]
ret = x.to_dict("records")
return ret
@staticmethod
def _aggregate_answers(x):
x = x[["answer_text", "answer_start"]]
x = x.rename(columns={"answer_text": "text"})
try:
x["answer_start"] = x["answer_start"].astype(int)
ret = x.to_dict("records")
except ValueError:
ret = []
return ret
def set_data(self, data):
self.data = data
self.df = self.to_df(data)
def sample_questions(self, n):
all_questions = self.get_all_questions()
sampled_questions = random.sample(all_questions, n)
df_sampled = self.df[self.df["question"].isin(sampled_questions)]
return self.df_to_data(df_sampled)
def get_all_paragraphs(self):
return self.df["context"].unique().tolist()
def get_all_questions(self):
df_questions = self.df[["title", "context", "question"]]
df_questions = df_questions.drop_duplicates()
questions = df_questions["question"].tolist()
return questions
def get_all_document_titles(self):
return self.df["title"].unique().tolist()
if __name__ == "__main__":
_read_squad_file("../data/squad20/train-v2.0.json")
filename1 = "../data/squad20/train-v2.0.json"
filename2 = "../data/squad20/dev-v2.0.json"
# Load file1 and take a sample of 10000 questions
sd = SquadData.from_file(filename1)
sample1 = sd.sample_questions(n=10000)
# Set sd to now contain the sample of 10000 questions
sd.set_data(sample1)
# Merge sd with file2 and take a sample of 100 questions
sd.merge_from_file(filename2)
sample2 = sd.sample_questions(n=100)
sd.set_data(sample2)
# Save this sample of 100
sd.save("../data/squad20/sample.json")
paragraphs = sd.get_all_paragraphs()
questions = sd.get_all_questions()
titles = sd.get_all_document_titles()
documents = sd.to_document_objs()
labels = sd.to_label_objs()
n_qs = sd.count(unit="questions")
n_as = sd.count(unit="no_answers")
n_ps = sd.count(unit="paragraphs")
print(n_qs)
print(n_as)
print(n_ps)
| true | true |
f725a83f718d805f89f9a0c04adb8de750d17bf3 | 7,720 | py | Python | tests/test_settings.py | zbyte64/django-dockit | 8d00a46cb0b6237de622fcb6816067078106a0c4 | [
"BSD-3-Clause"
] | 5 | 2015-02-25T17:01:48.000Z | 2021-06-03T07:46:47.000Z | tests/test_settings.py | zbyte64/django-dockit | 8d00a46cb0b6237de622fcb6816067078106a0c4 | [
"BSD-3-Clause"
] | 1 | 2015-03-11T15:19:55.000Z | 2015-04-13T04:14:24.000Z | tests/test_settings.py | zbyte64/django-dockit | 8d00a46cb0b6237de622fcb6816067078106a0c4 | [
"BSD-3-Clause"
] | null | null | null | # Django settings for {{ project_name }} project.
import os
PROJECT_DIR = os.path.dirname(os.path.abspath(__file__))
DEBUG = True
TEMPLATE_DEBUG = DEBUG
ADMINS = (
# ('Your Name', 'your_email@example.com'),
)
MANAGERS = ADMINS
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3', # Add 'postgresql_psycopg2', 'postgresql', 'mysql', 'sqlite3' or 'oracle'.
'NAME': ':memory:', # Or path to database file if using sqlite3.
'USER': '', # Not used with sqlite3.
'PASSWORD': '', # Not used with sqlite3.
'HOST': '', # Set to empty string for localhost. Not used with sqlite3.
'PORT': '', # Set to empty string for default. Not used with sqlite3.
}
}
# Local time zone for this installation. Choices can be found here:
# http://en.wikipedia.org/wiki/List_of_tz_zones_by_name
# although not all choices may be available on all operating systems.
# On Unix systems, a value of None will cause Django to use the same
# timezone as the operating system.
# If running in a Windows environment this must be set to the same as your
# system time zone.
TIME_ZONE = 'America/Chicago'
# Language code for this installation. All choices can be found here:
# http://www.i18nguy.com/unicode/language-identifiers.html
LANGUAGE_CODE = 'en-us'
SITE_ID = 1
# If you set this to False, Django will make some optimizations so as not
# to load the internationalization machinery.
USE_I18N = True
# If you set this to False, Django will not format dates, numbers and
# calendars according to the current locale
USE_L10N = True
# Absolute filesystem path to the directory that will hold user-uploaded files.
# Example: "/home/media/media.lawrence.com/media/"
MEDIA_ROOT = os.path.join(PROJECT_DIR, 'media')
# URL that handles the media served from MEDIA_ROOT. Make sure to use a
# trailing slash.
# Examples: "http://media.lawrence.com/media/", "http://example.com/media/"
MEDIA_URL = '/media/'
# Absolute path to the directory static files should be collected to.
# Don't put anything in this directory yourself; store your static files
# in apps' "static/" subdirectories and in STATICFILES_DIRS.
# Example: "/home/media/media.lawrence.com/static/"
STATIC_ROOT = ''
# URL prefix for static files.
# Example: "http://media.lawrence.com/static/"
STATIC_URL = '/static/'
# URL prefix for admin static files -- CSS, JavaScript and images.
# Make sure to use a trailing slash.
# Examples: "http://foo.com/static/admin/", "/static/admin/".
ADMIN_MEDIA_PREFIX = '/static/admin/'
# Additional locations of static files
STATICFILES_DIRS = (
# Put strings here, like "/home/html/static" or "C:/www/django/static".
# Always use forward slashes, even on Windows.
# Don't forget to use absolute paths, not relative paths.
)
# List of finder classes that know how to find static files in
# various locations.
STATICFILES_FINDERS = (
'django.contrib.staticfiles.finders.FileSystemFinder',
'django.contrib.staticfiles.finders.AppDirectoriesFinder',
# 'django.contrib.staticfiles.finders.DefaultStorageFinder',
)
# Make this unique, and don't share it with anybody.
SECRET_KEY = 'NOTASECRET'
# List of callables that know how to import templates from various sources.
TEMPLATE_LOADERS = (
'django.template.loaders.filesystem.Loader',
'django.template.loaders.app_directories.Loader',
# 'django.template.loaders.eggs.Loader',
)
MIDDLEWARE_CLASSES = (
'django.middleware.common.CommonMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
)
ROOT_URLCONF = 'tests.urls'
TEMPLATE_DIRS = (
# Put strings here, like "/home/html/django_templates" or "C:/www/django/templates".
# Always use forward slashes, even on Windows.
# Don't forget to use absolute paths, not relative paths.
)
INSTALLED_APPS = [
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.sites',
'django.contrib.messages',
'django.contrib.staticfiles',
'dockit',
'dockit.backends.djangodocument',
# Uncomment the next line to enable the admin:
'django.contrib.admin',
# Uncomment the next line to enable admin documentation:
# 'django.contrib.admindocs',
]
DOCKIT_BACKENDS = {
'default': {
'ENGINE': 'dockit.backends.djangodocument.backend.ModelDocumentStorage',
},
'djangodocument': {
'ENGINE': 'dockit.backends.djangodocument.backend.ModelDocumentStorage',
},
}
DOCKIT_INDEX_BACKENDS = {
'default': {
'ENGINE': 'dockit.backends.djangodocument.backend.ModelIndexStorage',
},
'djangodocument': {
'ENGINE': 'dockit.backends.djangodocument.backend.ModelIndexStorage',
},
}
try:
import pymongo
except ImportError:
pass
else:
from pymongo.errors import ConnectionFailure
try:
pymongo.MongoClient('localhost', 27017)
except ConnectionFailure:
pass
else:
INSTALLED_APPS.append('dockit.backends.mongo')
DOCKIT_BACKENDS['mongo'] = {
'ENGINE':'dockit.backends.mongo.backend.MongoDocumentStorage',
'HOST':'localhost',
'DB':'testdb',
'PORT': 27017,
}
DOCKIT_INDEX_BACKENDS['mongo'] = {
'ENGINE':'dockit.backends.mongo.backend.MongoIndexStorage',
'HOST':'localhost',
'DB':'testdb',
'PORT': 27017,
}
if 'TRAVIS' in os.environ:
DOCKIT_BACKENDS['mongo'] = {'ENGINE':'dockit.backends.mongo.backend.MongoDocumentStorage',
'USER':'travis',
'PASSWORD':'test',
'DB':'mydb_test',
'HOST':'127.0.0.1',
'PORT':27017,}
DOCKIT_INDEX_BACKENDS['mongo'] = {'ENGINE':'dockit.backends.mongo.backend.MongoIndexStorage',
'USER':'travis',
'PASSWORD':'test',
'DB':'mydb_test',
'HOST':'127.0.0.1',
'PORT':27017,}
if 'dockit.backends.mongo' not in INSTALLED_APPS:
INSTALLED_APPS.append('dockit.backends.mongo')
if os.environ.get('TASK_BACKEND', None) == 'celery':
DOCKIT_INDEX_BACKENDS['djangodocument']['INDEX_TASKS'] = 'dockit.backends.djangodocument.tasks.CeleryIndexTasks'
INSTALLED_APPS += ["djcelery"]
CELERY_ALWAYS_EAGER = True
import djcelery
djcelery.setup_loader()
if os.environ.get('TASK_BACKEND', None) == 'ztask':
DOCKIT_INDEX_BACKENDS['djangodocument']['INDEX_TASKS'] = 'dockit.backends.djangodocument.tasks.ZTaskIndexTasks'
INSTALLED_APPS += ["django_ztask"]
ZTASKD_ALWAYS_EAGER = True
# A sample logging configuration. The only tangible logging
# performed by this configuration is to send an email to
# the site admins on every HTTP 500 error.
# See http://docs.djangoproject.com/en/dev/topics/logging for
# more details on how to customize your logging configuration.
LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'handlers': {
'mail_admins': {
'level': 'ERROR',
'class': 'django.utils.log.AdminEmailHandler'
}
},
'loggers': {
'django.request': {
'handlers': ['mail_admins'],
'level': 'ERROR',
'propagate': True,
},
}
}
| 34.618834 | 122 | 0.651684 |
import os
PROJECT_DIR = os.path.dirname(os.path.abspath(__file__))
DEBUG = True
TEMPLATE_DEBUG = DEBUG
ADMINS = (
)
MANAGERS = ADMINS
DATABASES = {
'default': {
'ENGINE': 'django.db.backends.sqlite3',
'NAME': ':memory:',
'USER': '',
'PASSWORD': '',
'HOST': '',
'PORT': '',
}
}
TIME_ZONE = 'America/Chicago'
LANGUAGE_CODE = 'en-us'
SITE_ID = 1
USE_I18N = True
USE_L10N = True
MEDIA_ROOT = os.path.join(PROJECT_DIR, 'media')
MEDIA_URL = '/media/'
# in apps' "static/" subdirectories and in STATICFILES_DIRS.
STATIC_ROOT = ''
STATIC_URL = '/static/'
ADMIN_MEDIA_PREFIX = '/static/admin/'
STATICFILES_DIRS = (
)
# List of finder classes that know how to find static files in
# various locations.
STATICFILES_FINDERS = (
'django.contrib.staticfiles.finders.FileSystemFinder',
'django.contrib.staticfiles.finders.AppDirectoriesFinder',
# 'django.contrib.staticfiles.finders.DefaultStorageFinder',
)
# Make this unique, and don't share it with anybody.
SECRET_KEY = 'NOTASECRET'
TEMPLATE_LOADERS = (
'django.template.loaders.filesystem.Loader',
'django.template.loaders.app_directories.Loader',
)
MIDDLEWARE_CLASSES = (
'django.middleware.common.CommonMiddleware',
'django.contrib.sessions.middleware.SessionMiddleware',
'django.middleware.csrf.CsrfViewMiddleware',
'django.contrib.auth.middleware.AuthenticationMiddleware',
'django.contrib.messages.middleware.MessageMiddleware',
)
ROOT_URLCONF = 'tests.urls'
TEMPLATE_DIRS = (
)
INSTALLED_APPS = [
'django.contrib.auth',
'django.contrib.contenttypes',
'django.contrib.sessions',
'django.contrib.sites',
'django.contrib.messages',
'django.contrib.staticfiles',
'dockit',
'dockit.backends.djangodocument',
# Uncomment the next line to enable the admin:
'django.contrib.admin',
# Uncomment the next line to enable admin documentation:
# 'django.contrib.admindocs',
]
DOCKIT_BACKENDS = {
'default': {
'ENGINE': 'dockit.backends.djangodocument.backend.ModelDocumentStorage',
},
'djangodocument': {
'ENGINE': 'dockit.backends.djangodocument.backend.ModelDocumentStorage',
},
}
DOCKIT_INDEX_BACKENDS = {
'default': {
'ENGINE': 'dockit.backends.djangodocument.backend.ModelIndexStorage',
},
'djangodocument': {
'ENGINE': 'dockit.backends.djangodocument.backend.ModelIndexStorage',
},
}
try:
import pymongo
except ImportError:
pass
else:
from pymongo.errors import ConnectionFailure
try:
pymongo.MongoClient('localhost', 27017)
except ConnectionFailure:
pass
else:
INSTALLED_APPS.append('dockit.backends.mongo')
DOCKIT_BACKENDS['mongo'] = {
'ENGINE':'dockit.backends.mongo.backend.MongoDocumentStorage',
'HOST':'localhost',
'DB':'testdb',
'PORT': 27017,
}
DOCKIT_INDEX_BACKENDS['mongo'] = {
'ENGINE':'dockit.backends.mongo.backend.MongoIndexStorage',
'HOST':'localhost',
'DB':'testdb',
'PORT': 27017,
}
if 'TRAVIS' in os.environ:
DOCKIT_BACKENDS['mongo'] = {'ENGINE':'dockit.backends.mongo.backend.MongoDocumentStorage',
'USER':'travis',
'PASSWORD':'test',
'DB':'mydb_test',
'HOST':'127.0.0.1',
'PORT':27017,}
DOCKIT_INDEX_BACKENDS['mongo'] = {'ENGINE':'dockit.backends.mongo.backend.MongoIndexStorage',
'USER':'travis',
'PASSWORD':'test',
'DB':'mydb_test',
'HOST':'127.0.0.1',
'PORT':27017,}
if 'dockit.backends.mongo' not in INSTALLED_APPS:
INSTALLED_APPS.append('dockit.backends.mongo')
if os.environ.get('TASK_BACKEND', None) == 'celery':
DOCKIT_INDEX_BACKENDS['djangodocument']['INDEX_TASKS'] = 'dockit.backends.djangodocument.tasks.CeleryIndexTasks'
INSTALLED_APPS += ["djcelery"]
CELERY_ALWAYS_EAGER = True
import djcelery
djcelery.setup_loader()
if os.environ.get('TASK_BACKEND', None) == 'ztask':
DOCKIT_INDEX_BACKENDS['djangodocument']['INDEX_TASKS'] = 'dockit.backends.djangodocument.tasks.ZTaskIndexTasks'
INSTALLED_APPS += ["django_ztask"]
ZTASKD_ALWAYS_EAGER = True
# A sample logging configuration. The only tangible logging
# performed by this configuration is to send an email to
# the site admins on every HTTP 500 error.
# See http://docs.djangoproject.com/en/dev/topics/logging for
# more details on how to customize your logging configuration.
LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'handlers': {
'mail_admins': {
'level': 'ERROR',
'class': 'django.utils.log.AdminEmailHandler'
}
},
'loggers': {
'django.request': {
'handlers': ['mail_admins'],
'level': 'ERROR',
'propagate': True,
},
}
}
| true | true |
f725a8967049f2e2f616b7777ed95915bf059305 | 82 | py | Python | src/yeelight_atmosphere/__init__.py | NikSavilov/yeelight-atmosphere | 8860c2869380be50a6305b5b2aa77ed3636145d3 | [
"MIT"
] | null | null | null | src/yeelight_atmosphere/__init__.py | NikSavilov/yeelight-atmosphere | 8860c2869380be50a6305b5b2aa77ed3636145d3 | [
"MIT"
] | 7 | 2021-11-13T13:07:21.000Z | 2021-11-19T16:30:37.000Z | src/yeelight_atmosphere/__init__.py | NikSavilov/yeelight-atmosphere | 8860c2869380be50a6305b5b2aa77ed3636145d3 | [
"MIT"
] | null | null | null | """
Package allows to interact with Yeelight bulbs.
"""
__author__ = "Savilov N."
| 16.4 | 47 | 0.707317 | __author__ = "Savilov N."
| true | true |
f725a9201795c774934495e6a45ebb15b68ea359 | 5,930 | py | Python | doc/conf.py | open-dynamic-robot-initiative/mw_dual_motor_torque_ctrl | 765b2ac852952d0643caec652b2b92e9d7d18dd9 | [
"BSD-3-Clause"
] | 10 | 2020-07-06T01:06:58.000Z | 2022-03-13T23:37:46.000Z | doc/conf.py | open-dynamic-robot-initiative/mw_dual_motor_torque_ctrl | 765b2ac852952d0643caec652b2b92e9d7d18dd9 | [
"BSD-3-Clause"
] | 4 | 2019-08-30T06:37:14.000Z | 2021-10-05T12:28:39.000Z | doc/conf.py | open-dynamic-robot-initiative/mw_dual_motor_torque_ctrl | 765b2ac852952d0643caec652b2b92e9d7d18dd9 | [
"BSD-3-Clause"
] | 5 | 2021-01-01T16:03:55.000Z | 2022-02-17T22:02:35.000Z | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# ODRI Motor Board Firmware for CAN Communication documentation build
# configuration file, created by sphinx-quickstart on Thu Aug 12 17:06:32 2021.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
# import os
# import sys
# sys.path.insert(0, os.path.abspath('.'))
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#
# needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
"sphinx.ext.intersphinx",
"sphinx.ext.todo",
"sphinx.ext.mathjax",
"sphinx.ext.githubpages",
]
# Add any paths that contain templates here, relative to this directory.
templates_path = ["_templates"]
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
source_suffix = ".rst"
# The master toctree document.
master_doc = "index"
# General information about the project.
# project = "ODRI Motor Board Firmware for CAN Communication"
project = "ODRI Firmware for CAN"
copyright = "2021, Max Planck Gesellschaft"
author = "Felix Widmaier"
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = "1.0.0"
# The full version, including alpha/beta/rc tags.
release = "1.0.0"
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = "sphinx"
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = True
# -- Options for HTML output ----------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "alabaster"
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#
# html_theme_options = {}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
# Custom sidebar templates, must be a dictionary that maps document names
# to template names.
#
# This is required for the alabaster theme
# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars
html_sidebars = {
"**": [
"about.html",
"navigation.html",
"relations.html", # needs 'show_related': True theme option to display
"searchbox.html",
]
}
# -- Options for HTMLHelp output ------------------------------------------
# Output file base name for HTML help builder.
htmlhelp_basename = "ODRIMotorBoardFirmwareforCANCommunicationdoc"
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#
# 'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#
# 'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#
# 'preamble': '',
# Latex figure (float) alignment
#
# 'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(
master_doc,
"ODRIMotorBoardFirmwareforCANCommunication.tex",
"ODRI Motor Board Firmware for CAN Communication Documentation",
"Felix Widmaier",
"manual",
),
]
# -- Options for manual page output ---------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(
master_doc,
"odrimotorboardfirmwareforcancommunication",
"ODRI Motor Board Firmware for CAN Communication Documentation",
[author],
1,
)
]
# -- Options for Texinfo output -------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(
master_doc,
"ODRIMotorBoardFirmwareforCANCommunication",
"ODRI Motor Board Firmware for CAN Communication Documentation",
author,
"ODRIMotorBoardFirmwareforCANCommunication",
"One line description of project.",
"Miscellaneous",
),
]
# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {"https://docs.python.org/": None}
| 30.885417 | 79 | 0.679764 |
extensions = [
"sphinx.ext.intersphinx",
"sphinx.ext.todo",
"sphinx.ext.mathjax",
"sphinx.ext.githubpages",
]
templates_path = ["_templates"]
source_suffix = ".rst"
master_doc = "index"
project = "ODRI Firmware for CAN"
copyright = "2021, Max Planck Gesellschaft"
author = "Felix Widmaier"
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = "1.0.0"
# The full version, including alpha/beta/rc tags.
release = "1.0.0"
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = "sphinx"
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = True
# -- Options for HTML output ----------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = "alabaster"
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#
# html_theme_options = {}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
# Custom sidebar templates, must be a dictionary that maps document names
# to template names.
#
# This is required for the alabaster theme
# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars
html_sidebars = {
"**": [
"about.html",
"navigation.html",
"relations.html", # needs 'show_related': True theme option to display
"searchbox.html",
]
}
# -- Options for HTMLHelp output ------------------------------------------
# Output file base name for HTML help builder.
htmlhelp_basename = "ODRIMotorBoardFirmwareforCANCommunicationdoc"
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#
# 'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#
# 'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#
# 'preamble': '',
# Latex figure (float) alignment
#
# 'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(
master_doc,
"ODRIMotorBoardFirmwareforCANCommunication.tex",
"ODRI Motor Board Firmware for CAN Communication Documentation",
"Felix Widmaier",
"manual",
),
]
# -- Options for manual page output ---------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(
master_doc,
"odrimotorboardfirmwareforcancommunication",
"ODRI Motor Board Firmware for CAN Communication Documentation",
[author],
1,
)
]
# -- Options for Texinfo output -------------------------------------------
# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(
master_doc,
"ODRIMotorBoardFirmwareforCANCommunication",
"ODRI Motor Board Firmware for CAN Communication Documentation",
author,
"ODRIMotorBoardFirmwareforCANCommunication",
"One line description of project.",
"Miscellaneous",
),
]
# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {"https://docs.python.org/": None}
| true | true |
f725a9c6f543a4459d5a893dca60433b20e32993 | 4,539 | py | Python | sort.py | sagittarian/personal-sort | c7875141b4e15173e4a4a2267a23ff8236e6d729 | [
"MIT"
] | null | null | null | sort.py | sagittarian/personal-sort | c7875141b4e15173e4a4a2267a23ff8236e6d729 | [
"MIT"
] | null | null | null | sort.py | sagittarian/personal-sort | c7875141b4e15173e4a4a2267a23ff8236e6d729 | [
"MIT"
] | null | null | null | #!/usr/bin/env python3
'''A simple implementation of a sorting algorithm, meant to allow
people to manually rank a list of items using whatever subjective or
objective criteria they want.
This program can be called as a script and used interactively. You
can provide the list of things to sort as command line arguments, or
if there are no arguments provided, you can provide the list in stdin,
one item per line.
Example run:
$ ./sort.py 'ice cream' falafel hamburgers pizza
Which is greater, falafel or ice cream (<, =, or >)? <
Which is greater, hamburgers or ice cream (<, =, or >)? <
Which is greater, hamburgers or falafel (<, =, or >)? >
Which is greater, pizza or hamburgers (<, =, or >)? >
Which is greater, pizza or ice cream (<, =, or >)? <
* ice cream
* pizza
* hamburgers
* falafel
Author: Adam Mesha <adam@mesha.org>
License: MIT
'''
from functools import cmp_to_key
class memoize:
'''We really want to be sure that we don't ask people to compare the
same two items twice, so we cache the result.
'''
def __init__(self, func):
self.func = func
self.cache = {}
def __call__(self, *args):
key = tuple(args)
if key not in self.cache:
self.cache[key] = self.func(*args)
return self.cache[key]
@memoize
def cmpfunc(a, b):
result = None
s = 'Which is greater, {a} or {b} (<, =, or >)? '.format(a=a, b=b)
while result is None or result not in '<=>':
result = input(s).strip()
return '<=>'.index(result) - 1
keyfunc = cmp_to_key(cmpfunc)
def binary_insertion_sort(seq, keyfunc):
'''Insertion sort, using binary search to insert each element. Runs
in O(n**2) time, but the use case is when a human is manually
deciding on the ordering, so the most important thing is to reduce
the number of comparisons.
'''
def mv(srcidx, dstidx):
while srcidx > dstidx:
seq[srcidx], seq[srcidx - 1] = seq[srcidx - 1], seq[srcidx]
srcidx -= 1
i = 1
while i < len(seq):
lower = 0; upper = i
while lower < upper:
j = (upper + lower) // 2
key1, key2 = keyfunc(seq[i]), keyfunc(seq[j])
if key1 == key2:
mv(i, j+1) # XXX this is not stable
i += 1
break
if key1 < key2:
upper = j
else: # >
lower = j + 1
else:
mv(i, upper)
i += 1
class SortableWithHeuristic:
def __init__(self, val, heur):
self.val = val
self.heur = heur
def __str__(self):
return '{val}: {heur}'.format(val=self.val, heur=self.heur)
def __repr__(self):
return '{}(val={}, heur={})'.format(self.__class__.__name__,
repr(self.val),
repr(self.heur))
def get_heuristic_func(val):
result = None
s = 'Give an approximate numeric score to item {}: '.format(val)
while result is None:
try:
result = float(input(s).strip())
except ValueError:
pass
return result
def heuristic_sort(seq, get_heuristic_func, cmpfunc):
def swap(a, b):
seq[a], seq[b] = seq[b], seq[a]
idx = 0
while idx < len(seq):
val = seq[idx]
heur = get_heuristic_func(val)
seq[idx] = SortableWithHeuristic(val, heur)
# find the current location
j = idx
while j > 0 and seq[j].heur < seq[j-1].heur:
swap(j, j-1)
j -= 1
moved = False
while j < idx and cmpfunc(seq[j].val, seq[j+1].val) == 1:
swap(j, j+1)
j += 1
moved = True
if not moved:
while j > 0 and cmpfunc(seq[j].val, seq[j-1].val) == -1:
swap(j, j-1)
j -= 1
if 0 < j < idx:
seq[j].heur = (seq[j-1].heur + seq[j+1].heur) / 2
elif idx > 0:
if j == 0 and seq[j].heur > seq[j+1].heur:
seq[j].heur = seq[j+1].heur - 1
elif j == idx and seq[j].heur < seq[j-1].heur:
seq[j].heur = seq[j-1].heur + 1
idx += 1
def main():
import sys
seq = []
if len(sys.argv) > 1:
seq.extend(sys.argv[1:])
if not seq:
seq.extend(x.strip() for x in sys.stdin.readlines())
heuristic_sort(seq, get_heuristic_func, cmpfunc)
print('\n'.join('* {}'.format(item) for item in reversed(seq)))
if __name__ == '__main__':
main()
| 28.192547 | 72 | 0.548579 |
from functools import cmp_to_key
class memoize:
def __init__(self, func):
self.func = func
self.cache = {}
def __call__(self, *args):
key = tuple(args)
if key not in self.cache:
self.cache[key] = self.func(*args)
return self.cache[key]
@memoize
def cmpfunc(a, b):
result = None
s = 'Which is greater, {a} or {b} (<, =, or >)? '.format(a=a, b=b)
while result is None or result not in '<=>':
result = input(s).strip()
return '<=>'.index(result) - 1
keyfunc = cmp_to_key(cmpfunc)
def binary_insertion_sort(seq, keyfunc):
def mv(srcidx, dstidx):
while srcidx > dstidx:
seq[srcidx], seq[srcidx - 1] = seq[srcidx - 1], seq[srcidx]
srcidx -= 1
i = 1
while i < len(seq):
lower = 0; upper = i
while lower < upper:
j = (upper + lower) // 2
key1, key2 = keyfunc(seq[i]), keyfunc(seq[j])
if key1 == key2:
mv(i, j+1)
i += 1
break
if key1 < key2:
upper = j
else:
lower = j + 1
else:
mv(i, upper)
i += 1
class SortableWithHeuristic:
def __init__(self, val, heur):
self.val = val
self.heur = heur
def __str__(self):
return '{val}: {heur}'.format(val=self.val, heur=self.heur)
def __repr__(self):
return '{}(val={}, heur={})'.format(self.__class__.__name__,
repr(self.val),
repr(self.heur))
def get_heuristic_func(val):
result = None
s = 'Give an approximate numeric score to item {}: '.format(val)
while result is None:
try:
result = float(input(s).strip())
except ValueError:
pass
return result
def heuristic_sort(seq, get_heuristic_func, cmpfunc):
def swap(a, b):
seq[a], seq[b] = seq[b], seq[a]
idx = 0
while idx < len(seq):
val = seq[idx]
heur = get_heuristic_func(val)
seq[idx] = SortableWithHeuristic(val, heur)
j = idx
while j > 0 and seq[j].heur < seq[j-1].heur:
swap(j, j-1)
j -= 1
moved = False
while j < idx and cmpfunc(seq[j].val, seq[j+1].val) == 1:
swap(j, j+1)
j += 1
moved = True
if not moved:
while j > 0 and cmpfunc(seq[j].val, seq[j-1].val) == -1:
swap(j, j-1)
j -= 1
if 0 < j < idx:
seq[j].heur = (seq[j-1].heur + seq[j+1].heur) / 2
elif idx > 0:
if j == 0 and seq[j].heur > seq[j+1].heur:
seq[j].heur = seq[j+1].heur - 1
elif j == idx and seq[j].heur < seq[j-1].heur:
seq[j].heur = seq[j-1].heur + 1
idx += 1
def main():
import sys
seq = []
if len(sys.argv) > 1:
seq.extend(sys.argv[1:])
if not seq:
seq.extend(x.strip() for x in sys.stdin.readlines())
heuristic_sort(seq, get_heuristic_func, cmpfunc)
print('\n'.join('* {}'.format(item) for item in reversed(seq)))
if __name__ == '__main__':
main()
| true | true |
f725a9f9f6417c88c9e26be77336dc65fc531a7b | 1,577 | py | Python | preprocessing/homography_converter/uNetXST_homographies/2_F.py | LimJiaJing/Cam2BEV | 8177e13f7a3662daee28cce62f35b85f500941c0 | [
"MIT"
] | 335 | 2020-05-11T07:24:01.000Z | 2022-03-31T08:14:22.000Z | preprocessing/homography_converter/uNetXST_homographies/2_F.py | hoanganhpham1006/Cam2BEV | f788a9f58b464bc7b114e5a0dd1afcd6683f10e3 | [
"MIT"
] | 23 | 2020-07-12T23:42:31.000Z | 2022-03-26T09:42:01.000Z | preprocessing/homography_converter/uNetXST_homographies/2_F.py | hoanganhpham1006/Cam2BEV | f788a9f58b464bc7b114e5a0dd1afcd6683f10e3 | [
"MIT"
] | 63 | 2020-05-11T16:27:56.000Z | 2022-03-17T17:11:18.000Z | # ==============================================================================
# MIT License
#
# Copyright 2020 Institute for Automotive Engineering of RWTH Aachen University.
#
# 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 numpy as np
# for dataset 2_F
H = [
np.array([[0.03506686613905922, 27.971438297785962, -0.17694724954191404], [0.3821882391578238, 9.481642330993019e-17, 5.46222110929461], [25.000001047737943, 6.202207287472715e-15, 27.000001047737943]]) # front
]
| 50.870968 | 213 | 0.698795 |
import numpy as np
H = [
np.array([[0.03506686613905922, 27.971438297785962, -0.17694724954191404], [0.3821882391578238, 9.481642330993019e-17, 5.46222110929461], [25.000001047737943, 6.202207287472715e-15, 27.000001047737943]])
]
| true | true |
f725ab2fdd412236541d778007d38e8624e6c5f3 | 1,105 | py | Python | gcloud/periodictask/apps.py | springborland/bk-sops | a9057672c10efb5f2414a805a30ead4092429c76 | [
"Apache-2.0"
] | 1 | 2021-05-19T04:31:34.000Z | 2021-05-19T04:31:34.000Z | gcloud/periodictask/apps.py | sighttviewliu/bk-sops | 6bf2f38bd93990f20f7c3a4decafc310e09e679c | [
"Apache-2.0"
] | null | null | null | gcloud/periodictask/apps.py | sighttviewliu/bk-sops | 6bf2f38bd93990f20f7c3a4decafc310e09e679c | [
"Apache-2.0"
] | null | null | null | # -*- coding: utf-8 -*-
"""
Tencent is pleased to support the open source community by making 蓝鲸智云PaaS平台社区版 (BlueKing PaaS Community
Edition) available.
Copyright (C) 2017-2020 THL A29 Limited, a Tencent company. All rights reserved.
Licensed under the MIT License (the "License"); you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://opensource.org/licenses/MIT
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
"""
from django.apps import AppConfig
class PeriodicTaskConfig(AppConfig):
name = "gcloud.periodictask"
verbose_name = "GcloudPeriodicTask"
def ready(self):
from .signals.handlers import ( # noqa
pre_periodic_task_start_handler,
periodic_task_history_post_save_handler,
periodic_task_start_failed_handler,
)
| 40.925926 | 115 | 0.755656 |
from django.apps import AppConfig
class PeriodicTaskConfig(AppConfig):
name = "gcloud.periodictask"
verbose_name = "GcloudPeriodicTask"
def ready(self):
from .signals.handlers import (
pre_periodic_task_start_handler,
periodic_task_history_post_save_handler,
periodic_task_start_failed_handler,
)
| true | true |
f725abd59251190289dabcf79ac0661b33697890 | 1,839 | py | Python | alembic/versions/4f53ec506661_add_study_template.py | jonathanzong/dmca | 70157cff983310e5951024aa80e99e7a5404d758 | [
"MIT"
] | 2 | 2022-02-16T22:50:06.000Z | 2022-02-21T19:38:02.000Z | alembic/versions/4f53ec506661_add_study_template.py | jonathanzong/dmca | 70157cff983310e5951024aa80e99e7a5404d758 | [
"MIT"
] | 2 | 2022-02-01T05:48:07.000Z | 2022-02-01T05:49:29.000Z | alembic/versions/4f53ec506661_add_study_template.py | jonathanzong/bartleby | 70157cff983310e5951024aa80e99e7a5404d758 | [
"MIT"
] | null | null | null | """add study template
Revision ID: 4f53ec506661
Revises: 4302608638bc
Create Date: 2018-05-23 15:44:01.450488
"""
# revision identifiers, used by Alembic.
revision = '4f53ec506661'
down_revision = '4302608638bc'
branch_labels = None
depends_on = None
from alembic import op
import sqlalchemy as sa
def upgrade(engine_name):
globals()["upgrade_%s" % engine_name]()
def downgrade(engine_name):
globals()["downgrade_%s" % engine_name]()
def upgrade_development():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('twitter_user_recruitment_tweet_attempt', sa.Column('study_template', sa.String(length=64), nullable=True))
# ### end Alembic commands ###
def downgrade_development():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_column('twitter_user_recruitment_tweet_attempt', 'study_template')
# ### end Alembic commands ###
def upgrade_test():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('twitter_user_recruitment_tweet_attempt', sa.Column('study_template', sa.String(length=64), nullable=True))
# ### end Alembic commands ###
def downgrade_test():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_column('twitter_user_recruitment_tweet_attempt', 'study_template')
# ### end Alembic commands ###
def upgrade_production():
# ### commands auto generated by Alembic - please adjust! ###
op.add_column('twitter_user_recruitment_tweet_attempt', sa.Column('study_template', sa.String(length=64), nullable=True))
# ### end Alembic commands ###
def downgrade_production():
# ### commands auto generated by Alembic - please adjust! ###
op.drop_column('twitter_user_recruitment_tweet_attempt', 'study_template')
# ### end Alembic commands ###
| 28.292308 | 125 | 0.712887 |
revision = '4f53ec506661'
down_revision = '4302608638bc'
branch_labels = None
depends_on = None
from alembic import op
import sqlalchemy as sa
def upgrade(engine_name):
globals()["upgrade_%s" % engine_name]()
def downgrade(engine_name):
globals()["downgrade_%s" % engine_name]()
def upgrade_development():
| true | true |
f725acb2361d7d3cd6f93696255dbd053911f91e | 18,253 | py | Python | iotbx/regression/tst_map_model_manager_3.py | TiankunZhou/cctbx_project | 373f302f00c12d7239f8e37e3165e62bc1d852cc | [
"BSD-3-Clause-LBNL"
] | null | null | null | iotbx/regression/tst_map_model_manager_3.py | TiankunZhou/cctbx_project | 373f302f00c12d7239f8e37e3165e62bc1d852cc | [
"BSD-3-Clause-LBNL"
] | null | null | null | iotbx/regression/tst_map_model_manager_3.py | TiankunZhou/cctbx_project | 373f302f00c12d7239f8e37e3165e62bc1d852cc | [
"BSD-3-Clause-LBNL"
] | null | null | null | from __future__ import absolute_import, division, print_function
from cctbx.array_family import flex
import os, sys
from libtbx.utils import Sorry
from libtbx.test_utils import approx_equal
from mmtbx.model import manager as model_manager
from iotbx.data_manager import DataManager
def exercise(file_name, out = sys.stdout):
# Set up source data
if not os.path.isfile(file_name):
raise Sorry("Missing the file: %s" %(file_name)+"\n")
print ("Reading from %s" %(file_name))
from iotbx.map_manager import map_manager
m = map_manager(file_name)
# make a little model
sites_cart = flex.vec3_double( ((8, 10, 12), (14, 15, 16)))
model = model_manager.from_sites_cart(
atom_name = ' CA ',
resname = 'ALA',
chain_id = 'A',
b_iso = 30.,
occ = 1.,
scatterer = 'C',
sites_cart = sites_cart,
crystal_symmetry = m.crystal_symmetry())
# make a map_model_manager with lots of maps and model and ncs
from iotbx.map_model_manager import map_model_manager
from mmtbx.ncs.ncs import ncs
ncs_object=ncs()
ncs_object.set_unit_ncs()
mask_mm=m.deep_copy()
mask_mm.set_is_mask(True)
mam = map_model_manager(
map_manager = m,
ncs_object = ncs_object,
map_manager_1 = m.deep_copy(),
map_manager_2 = m.deep_copy(),
extra_map_manager_list = [m.deep_copy(),m.deep_copy(),m.deep_copy()],
extra_map_manager_id_list = ["extra_1","extra_2","map_manager_mask"],
model = model.deep_copy(),)
print (mam.map_manager())
print (mam.model())
print (mam.map_manager_1())
print (mam.map_manager_2())
print (mam.map_manager_mask())
print (mam.map_manager().ncs_object())
all_map_names=mam.map_id_list()
for id in all_map_names:
print("Map_manager %s: %s " %(id,mam.get_map_manager_by_id(id)))
dm = DataManager(['model','miller_array', 'real_map', 'phil','ncs_spec'])
dm.set_overwrite(True)
# Create a model with ncs
from iotbx.regression.ncs.tst_ncs import pdb_str_5
file_name='tst_mam.pdb'
f=open(file_name,'w')
print (pdb_str_5, file = f)
f.close()
# Generate map data from this model (it has ncs)
mmm=map_model_manager()
mmm.generate_map(box_cushion=0, file_name=file_name,n_residues=500)
ncs_mam=mmm.deep_copy()
ncs_mam_copy=mmm.deep_copy()
# Make sure this model has 126 sites (42 sites times 3-fold ncs)
assert ncs_mam.model().get_sites_cart().size() == 126
assert approx_equal (ncs_mam.model().get_sites_cart()[0],
(23.560999999999996, 8.159, 10.660000000000002))
# Get just unique part (42 sites)
unique_mam=ncs_mam.extract_all_maps_around_model(select_unique_by_ncs=True)
assert unique_mam.model().get_sites_cart().size() == 42
assert approx_equal (unique_mam.model().get_sites_cart()[0],
(18.740916666666664, 13.1794, 16.10544))
# Make sure that the extraction did not change the original but does change
# the extracted part
assert (unique_mam.model().get_sites_cart()[0] !=
ncs_mam.model().get_sites_cart()[0]) # it was a deep copy so original stays
# Shift back the extracted part and make sure it matches the original now
shifted_back_unique_model=mmm.get_model_from_other(unique_mam.deep_copy())
assert approx_equal (shifted_back_unique_model.get_sites_cart()[0],
(23.560999999999996, 8.158999999999997, 10.66))
# Change the extracted model
sites_cart=unique_mam.model().get_sites_cart()
sites_cart[0]=(1,1,1)
unique_mam.model().get_hierarchy().atoms().set_xyz(sites_cart)
# Note; setting xyz in hierarchy does not set xrs by itself. do that now:
unique_mam.model().set_sites_cart_from_hierarchy(multiply_ncs=False)
# Make sure we really changed it
assert approx_equal (unique_mam.model().get_sites_cart()[0], (1,1,1))
# Now propagate all the changes in this unique part to entire original model
# using NCS
ncs_mam.propagate_model_from_other(other = unique_mam,
model_id = 'model',
other_model_id = 'model')
# ...and check that copy 1 and copy 2 both change
assert approx_equal (ncs_mam.model().get_sites_cart()[0],
(5.820083333333333, -4.020400000000001, -4.445440000000001))
assert approx_equal (ncs_mam.model().get_sites_cart()[42],
(38.41904613024224, 17.233251085893276, 2.5547442135142524))
# Find ncs from map or model
nn=ncs_mam_copy
nn.write_map('ncs.ccp4')
nn.write_model('ncs.pdb')
ncs_object=nn.get_ncs_from_model()
dm.write_ncs_spec_file(ncs_object,'ncs.ncs_spec')
print ("NCS from map",ncs_object)
nn.set_ncs_object(ncs_object)
print ("NCS now: ",nn.ncs_object())
nn.get_ncs_from_map(ncs_object=ncs_object)
print ("ncs cc:",nn.ncs_cc())
assert approx_equal(nn.ncs_cc(),0.961915979834,eps=0.01)
# Make a deep_copy
dc=mam.deep_copy()
new_mam=mam.deep_copy()
assert mam.map_manager().map_data()[0]==new_mam.map_manager().map_data()[0]
# Make a customized_copy
new_mam=mam.customized_copy(model_dict={'model':mam.model()})
assert new_mam.model() is mam.model()
assert not new_mam.map_dict() is mam.map_dict()
new_mam=mam.customized_copy(model_dict={'model':mam.model()},
map_dict=mam.map_dict())
assert new_mam.model() is mam.model()
assert new_mam.map_dict() is mam.map_dict()
print (mam)
# Add a map
mam = dc.deep_copy()
print (mam.map_id_list())
assert len(mam.map_id_list()) == 6
mam.add_map_manager_by_id(mam.map_manager().deep_copy(),'new_map_manager')
print (mam.map_id_list())
assert len(mam.map_id_list()) == 7
# duplicate a map
mam = dc.deep_copy()
print (mam.map_id_list())
assert len(mam.map_id_list()) == 6
mam.duplicate_map_manager('map_manager','new_map_manager')
print (mam.map_id_list())
assert len(mam.map_id_list()) == 7
# resolution_filter a map
mam = dc.deep_copy()
print (mam.map_id_list())
mam.duplicate_map_manager('map_manager','new_map_manager')
mam.resolution_filter(map_id='new_map_manager',d_min=3.5,d_max=6)
# Add a model
mam = dc.deep_copy()
print (mam.model_id_list())
assert len(mam.model_id_list()) == 1
mam.add_model_by_id(mam.model().deep_copy(),'new_model')
print (mam.model_id_list())
assert len(mam.model_id_list()) == 2
# Initialize a map
mam1=new_mam.deep_copy()
mam1.initialize_maps(map_value=6)
assert mam1.map_manager().map_data()[225] == 6
# Create mask around density and apply to all maps
mam1=new_mam.deep_copy()
mam1.mask_all_maps_around_density(solvent_content=0.5,
soft_mask=True,)
s = (mam1.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (1024,2048))
# Create mask around edges and apply to all maps
mam1=new_mam.deep_copy()
mam1.mask_all_maps_around_edges()
s = (mam1.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (1176,2048))
# Create a soft mask around model and apply to all maps
new_mam.mask_all_maps_around_atoms(mask_atoms_atom_radius=8,
soft_mask=True)
s = (new_mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (1944,2048))
# Create a soft mask around model and do not do anything with it
new_mam.create_mask_around_atoms(mask_atoms_atom_radius=8,
soft_mask=True)
s = (new_mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (1944,2048))
# Create a soft mask around model and do not do anything with it, wrapping =true
dummy_mam=new_mam.deep_copy()
dummy_mam.map_manager().set_wrapping(True)
dummy_mam.create_mask_around_atoms(mask_atoms_atom_radius=8,
soft_mask=True)
s = (dummy_mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (1944,2048))
# Create a sharp mask around model and do not do anything with it
new_mam.create_mask_around_atoms(soft_mask=False,
mask_atoms_atom_radius=8)
s = (new_mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (138,2048))
# Mask around edges and do not do anything with it
mam=dc.deep_copy()
mam.create_mask_around_edges()
s = (mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (1176,2048))
# Mask around density and to not do anything with it
mam=dc.deep_copy()
mam.create_mask_around_density(soft_mask=False)
s = (mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (1000,2048))
# Apply the current mask to one map
mam.apply_mask_to_map('map_manager')
s = (mam.map_manager().map_data() > 0.)
assert approx_equal( (s.count(True),s.size()), (640,2048))
s = (mam.map_manager().map_data() != 0.)
assert approx_equal( (s.count(True),s.size()), (1000,2048))
assert approx_equal ((mam.map_manager().map_data()[225]),-0.0418027862906)
# Apply any mask to one map
mam.apply_mask_to_map('map_manager',mask_id='mask')
s = (mam.map_manager().map_data() > 0.)
assert approx_equal( (s.count(True),s.size()), (640,2048))
s = (mam.map_manager().map_data() != 0.)
assert approx_equal( (s.count(True),s.size()), (1000,2048))
assert approx_equal ((mam.map_manager().map_data()[225]),-0.0418027862906)
# Apply the mask to all maps
mam.apply_mask_to_maps()
s = (mam.map_manager().map_data() > 0.)
assert approx_equal( (s.count(True),s.size()), (640,2048))
s = (mam.map_manager().map_data() != 0.)
assert approx_equal( (s.count(True),s.size()), (1000,2048))
assert approx_equal ((mam.map_manager().map_data()[225]),-0.0418027862906)
# Apply the mask to all maps, setting outside value to mean inside
mam.apply_mask_to_maps(set_outside_to_mean_inside=True)
s = (mam.map_manager().map_data() > 0.)
assert approx_equal( (s.count(True),s.size()), (1688,2048))
s = (mam.map_manager().map_data() != 0.)
assert approx_equal( (s.count(True),s.size()), (2048,2048))
assert approx_equal ((mam.map_manager().map_data()[2047]),-0.0759598612785)
s = (mam.get_map_manager_by_id('mask').map_data() > 0).as_1d()
inside = mam.map_manager().map_data().as_1d().select(s)
outside = mam.map_manager().map_data().as_1d().select(~s)
assert approx_equal ((inside.min_max_mean().max,outside.min_max_mean().max),
(0.335603952408,0.0239064293122))
# Make a new map and model, get mam and box with selection
mmm=map_model_manager()
mmm.generate_map(box_cushion=0,wrapping=True)
mam=mmm
mam_dc=mam.deep_copy()
new_mm_1=mam.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((18, 25, 20),(18, 25, 20)))
# Get local fsc or randomized map
dc=mam_dc.deep_copy()
dc.map_manager().set_wrapping(False)
map_coeffs = dc.map_manager().map_as_fourier_coefficients(d_min=3)
from cctbx.development.create_models_or_maps import generate_map
new_mm_1 = generate_map(map_coeffs=map_coeffs,
d_min=3,
low_resolution_real_space_noise_fraction=1,
high_resolution_real_space_noise_fraction=50,
map_manager=dc.map_manager(),
random_seed=124321)
new_mm_2 = generate_map(map_coeffs=map_coeffs,
d_min=3,
low_resolution_real_space_noise_fraction=1,
high_resolution_real_space_noise_fraction=50,
map_manager=dc.map_manager(),
random_seed=734119)
dc.add_map_manager_by_id(new_mm_1,'map_manager_1')
dc.add_map_manager_by_id(new_mm_2,'map_manager_2')
cc=dc.map_map_cc()
fsc_curve=dc.map_map_fsc()
dc.set_log(sys.stdout)
dc.local_fsc(n_boxes = 1)
# Get map-map FSC
dc=mam_dc.deep_copy()
dc.duplicate_map_manager(map_id='map_manager',new_map_id='filtered')
dc.resolution_filter(d_min=3.5, d_max=10, map_id='filtered')
dc.create_mask_around_atoms()
fsc_curve=dc.map_map_fsc(
map_id_1='map_manager',map_id_2='filtered',mask_id='mask',
resolution=3.5,fsc_cutoff = 0.97)
assert approx_equal(fsc_curve.d_min, 3.93793648601,eps=0.01)
assert approx_equal (fsc_curve.fsc.fsc[-1],0.707536576779)
# Get map-map CC
dc=mam_dc.deep_copy()
dc.duplicate_map_manager(map_id='map_manager',new_map_id='filtered')
dc.resolution_filter(d_min=3.5, d_max=6, map_id='filtered')
cc=dc.map_map_cc('map_manager','filtered')
assert approx_equal(cc , 0.676687646486)
# Get map-map CC with mask
dc=mam_dc.deep_copy()
dc.duplicate_map_manager(map_id='map_manager',new_map_id='filtered')
dc.create_mask_around_density(mask_id='filtered')
cc=dc.map_map_cc('map_manager','filtered',mask_id='mask')
assert approx_equal(cc , 0.422523947639)
# box around model
mam=mam_dc.deep_copy()
mam.box_all_maps_around_model_and_shift_origin(
selection_string="resseq 221:221")
new_mm_1=mam.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((18, 25, 20),(24, 20, 20)))
# extract_around_model (get new mam)
new_mam_dc=mam_dc.extract_all_maps_around_model(
selection_string="resseq 221:221")
new_mm_1a=new_mam_dc.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1a.map_data().all()),
((18, 25, 20),(24, 20, 20)))
assert approx_equal(new_mm_1.map_data(),new_mm_1a.map_data())
# box around_density
mam2=mam_dc.deep_copy()
mam2.box_all_maps_around_density_and_shift_origin(box_cushion=0)
new_mm_2=mam2.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_2.map_data().all()),
((18, 25, 20),(16, 23, 18)))
# extract_around_density (get new mam)
mam2=mam_dc.deep_copy()
mam2_b=mam2.extract_all_maps_around_density(box_cushion=0)
new_mm_2=mam2_b.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_2.map_data().all()),
((18, 25, 20),(16, 23, 18)))
# Repeat as map_model_manager:
mmm=mam_dc.as_map_model_manager().deep_copy()
mmm.box_all_maps_around_model_and_shift_origin(
selection_string="resseq 221:221")
new_mm_1a=mmm.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1a.map_data().all()),
((24, 20, 20),(24, 20, 20)))
assert approx_equal(new_mm_1.map_data(),new_mm_1a.map_data())
# box around density
mam.box_all_maps_around_density_and_shift_origin(box_cushion=0)
new_mm_1=mam.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((24, 20 , 20),(22, 18, 18)))
# extract around density (get new mam)
mam1=mam_dc.deep_copy()
mam1.extract_all_maps_around_density(box_cushion=0)
new_mm_1=mam1.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((24, 20 , 20),(18, 25, 20)))
# create mask around density, then box around mask (i.e., box around density)
mam.create_mask_around_density(soft_mask=False)
mam.box_all_maps_around_mask_and_shift_origin(box_cushion=3)
new_mm_1=mam.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((24, 20 , 20),(22, 18, 18)))
# box with bounds
mam.box_all_maps_with_bounds_and_shift_origin(lower_bounds=(10,10,10),
upper_bounds=(15,15,15))
new_mm_1=mam.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((24, 20, 20),(6, 6, 6)))
# extract with bounds
mam=mam_dc.deep_copy()
mam_1=mam.extract_all_maps_with_bounds(lower_bounds=(10,10,10),
upper_bounds=(15,15,15))
new_mm_1=mam_1.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((24, 20, 20),(6, 6, 6)))
# box with unique
mam=mam_dc.deep_copy()
mam.box_all_maps_around_unique_and_shift_origin(
molecular_mass=2500,resolution=3)
new_mm_1=mam.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((24, 20, 20),(18, 25, 20)))
# extract with unique
mam=mam_dc.deep_copy()
mam_1=mam.extract_all_maps_around_unique(
molecular_mass=2500,resolution=3)
new_mm_1=mam_1.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((24, 20, 20),(18, 25, 20)))
# extract a box and then restore model into same reference as current mam
mam=mam_dc.deep_copy()
mam.box_all_maps_with_bounds_and_shift_origin(lower_bounds=(2,2,2),
upper_bounds=(17,17,17))
print("mam:",mam.model().get_sites_cart()[0],mam.map_manager().origin_is_zero())
# extract a box
box_mam=mam.extract_all_maps_with_bounds(lower_bounds=(10,10,10),
upper_bounds=(15,15,15))
box_model=box_mam.model()
matched_box_model=mam.get_model_from_other(box_mam)
assert approx_equal(matched_box_model.get_sites_cart()[0],mam.model().get_sites_cart()[0])
# Convert a map to fourier coefficients
mam=mam_dc.deep_copy()
ma=mam.map_as_fourier_coefficients(d_min=3)
assert approx_equal(ma.d_min(),3.01655042414)
mam.add_map_from_fourier_coefficients(ma, map_id='new_map_manager')
cc=flex.linear_correlation(
mam.get_map_manager_by_id('map_manager').map_data().as_1d(),
mam.get_map_manager_by_id('new_map_manager').map_data().as_1d()).coefficient()
assert (cc >= 0.99)
# Get map-model CC
dc=mam_dc.extract_all_maps_around_model(
selection_string="(name ca or name cb or name c or name o) "+
"and resseq 221:221", box_cushion=0)
cc=dc.map_model_cc(resolution=3)
assert approx_equal (cc, 0.817089390421)
# Remove model outside map
dc.remove_model_outside_map(boundary=0)
assert (mam_dc.model().get_sites_cart().size(),
dc.model().get_sites_cart().size()) == (86, 4)
# shift a model to match the map
dc=mam_dc.extract_all_maps_around_model(
selection_string="(name ca or name cb or name c or name o) "+
"and resseq 221:221", box_cushion=0)
actual_model=dc.model().deep_copy()
working_model=dc.model().deep_copy()
working_model.set_shift_cart((0,0,0))
working_model.set_sites_cart(working_model.get_sites_cart()-actual_model.shift_cart())
dc.shift_any_model_to_match(working_model)
assert approx_equal (actual_model.get_sites_cart()[0],working_model.get_sites_cart()[0])
if __name__ == "__main__":
args = sys.argv[1:]
import libtbx.load_env
if not args:
file_name = libtbx.env.under_dist(
module_name = "iotbx",
path = "ccp4_map/tst_input.map")
args = [file_name]
if libtbx.env.has_module("phenix"):
exercise(file_name = args[0])
else:
print("Skipped: Requires phenix module")
print ("OK")
| 38.027083 | 92 | 0.718402 | from __future__ import absolute_import, division, print_function
from cctbx.array_family import flex
import os, sys
from libtbx.utils import Sorry
from libtbx.test_utils import approx_equal
from mmtbx.model import manager as model_manager
from iotbx.data_manager import DataManager
def exercise(file_name, out = sys.stdout):
if not os.path.isfile(file_name):
raise Sorry("Missing the file: %s" %(file_name)+"\n")
print ("Reading from %s" %(file_name))
from iotbx.map_manager import map_manager
m = map_manager(file_name)
sites_cart = flex.vec3_double( ((8, 10, 12), (14, 15, 16)))
model = model_manager.from_sites_cart(
atom_name = ' CA ',
resname = 'ALA',
chain_id = 'A',
b_iso = 30.,
occ = 1.,
scatterer = 'C',
sites_cart = sites_cart,
crystal_symmetry = m.crystal_symmetry())
from iotbx.map_model_manager import map_model_manager
from mmtbx.ncs.ncs import ncs
ncs_object=ncs()
ncs_object.set_unit_ncs()
mask_mm=m.deep_copy()
mask_mm.set_is_mask(True)
mam = map_model_manager(
map_manager = m,
ncs_object = ncs_object,
map_manager_1 = m.deep_copy(),
map_manager_2 = m.deep_copy(),
extra_map_manager_list = [m.deep_copy(),m.deep_copy(),m.deep_copy()],
extra_map_manager_id_list = ["extra_1","extra_2","map_manager_mask"],
model = model.deep_copy(),)
print (mam.map_manager())
print (mam.model())
print (mam.map_manager_1())
print (mam.map_manager_2())
print (mam.map_manager_mask())
print (mam.map_manager().ncs_object())
all_map_names=mam.map_id_list()
for id in all_map_names:
print("Map_manager %s: %s " %(id,mam.get_map_manager_by_id(id)))
dm = DataManager(['model','miller_array', 'real_map', 'phil','ncs_spec'])
dm.set_overwrite(True)
from iotbx.regression.ncs.tst_ncs import pdb_str_5
file_name='tst_mam.pdb'
f=open(file_name,'w')
print (pdb_str_5, file = f)
f.close()
mmm=map_model_manager()
mmm.generate_map(box_cushion=0, file_name=file_name,n_residues=500)
ncs_mam=mmm.deep_copy()
ncs_mam_copy=mmm.deep_copy()
assert ncs_mam.model().get_sites_cart().size() == 126
assert approx_equal (ncs_mam.model().get_sites_cart()[0],
(23.560999999999996, 8.159, 10.660000000000002))
unique_mam=ncs_mam.extract_all_maps_around_model(select_unique_by_ncs=True)
assert unique_mam.model().get_sites_cart().size() == 42
assert approx_equal (unique_mam.model().get_sites_cart()[0],
(18.740916666666664, 13.1794, 16.10544))
assert (unique_mam.model().get_sites_cart()[0] !=
ncs_mam.model().get_sites_cart()[0])
shifted_back_unique_model=mmm.get_model_from_other(unique_mam.deep_copy())
assert approx_equal (shifted_back_unique_model.get_sites_cart()[0],
(23.560999999999996, 8.158999999999997, 10.66))
sites_cart=unique_mam.model().get_sites_cart()
sites_cart[0]=(1,1,1)
unique_mam.model().get_hierarchy().atoms().set_xyz(sites_cart)
unique_mam.model().set_sites_cart_from_hierarchy(multiply_ncs=False)
assert approx_equal (unique_mam.model().get_sites_cart()[0], (1,1,1))
ncs_mam.propagate_model_from_other(other = unique_mam,
model_id = 'model',
other_model_id = 'model')
assert approx_equal (ncs_mam.model().get_sites_cart()[0],
(5.820083333333333, -4.020400000000001, -4.445440000000001))
assert approx_equal (ncs_mam.model().get_sites_cart()[42],
(38.41904613024224, 17.233251085893276, 2.5547442135142524))
nn=ncs_mam_copy
nn.write_map('ncs.ccp4')
nn.write_model('ncs.pdb')
ncs_object=nn.get_ncs_from_model()
dm.write_ncs_spec_file(ncs_object,'ncs.ncs_spec')
print ("NCS from map",ncs_object)
nn.set_ncs_object(ncs_object)
print ("NCS now: ",nn.ncs_object())
nn.get_ncs_from_map(ncs_object=ncs_object)
print ("ncs cc:",nn.ncs_cc())
assert approx_equal(nn.ncs_cc(),0.961915979834,eps=0.01)
dc=mam.deep_copy()
new_mam=mam.deep_copy()
assert mam.map_manager().map_data()[0]==new_mam.map_manager().map_data()[0]
new_mam=mam.customized_copy(model_dict={'model':mam.model()})
assert new_mam.model() is mam.model()
assert not new_mam.map_dict() is mam.map_dict()
new_mam=mam.customized_copy(model_dict={'model':mam.model()},
map_dict=mam.map_dict())
assert new_mam.model() is mam.model()
assert new_mam.map_dict() is mam.map_dict()
print (mam)
mam = dc.deep_copy()
print (mam.map_id_list())
assert len(mam.map_id_list()) == 6
mam.add_map_manager_by_id(mam.map_manager().deep_copy(),'new_map_manager')
print (mam.map_id_list())
assert len(mam.map_id_list()) == 7
mam = dc.deep_copy()
print (mam.map_id_list())
assert len(mam.map_id_list()) == 6
mam.duplicate_map_manager('map_manager','new_map_manager')
print (mam.map_id_list())
assert len(mam.map_id_list()) == 7
mam = dc.deep_copy()
print (mam.map_id_list())
mam.duplicate_map_manager('map_manager','new_map_manager')
mam.resolution_filter(map_id='new_map_manager',d_min=3.5,d_max=6)
mam = dc.deep_copy()
print (mam.model_id_list())
assert len(mam.model_id_list()) == 1
mam.add_model_by_id(mam.model().deep_copy(),'new_model')
print (mam.model_id_list())
assert len(mam.model_id_list()) == 2
mam1=new_mam.deep_copy()
mam1.initialize_maps(map_value=6)
assert mam1.map_manager().map_data()[225] == 6
mam1=new_mam.deep_copy()
mam1.mask_all_maps_around_density(solvent_content=0.5,
soft_mask=True,)
s = (mam1.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (1024,2048))
mam1=new_mam.deep_copy()
mam1.mask_all_maps_around_edges()
s = (mam1.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (1176,2048))
new_mam.mask_all_maps_around_atoms(mask_atoms_atom_radius=8,
soft_mask=True)
s = (new_mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (1944,2048))
new_mam.create_mask_around_atoms(mask_atoms_atom_radius=8,
soft_mask=True)
s = (new_mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (1944,2048))
dummy_mam=new_mam.deep_copy()
dummy_mam.map_manager().set_wrapping(True)
dummy_mam.create_mask_around_atoms(mask_atoms_atom_radius=8,
soft_mask=True)
s = (dummy_mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (1944,2048))
new_mam.create_mask_around_atoms(soft_mask=False,
mask_atoms_atom_radius=8)
s = (new_mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (138,2048))
mam=dc.deep_copy()
mam.create_mask_around_edges()
s = (mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (1176,2048))
mam=dc.deep_copy()
mam.create_mask_around_density(soft_mask=False)
s = (mam.get_map_manager_by_id('mask').map_data() > 0.5)
assert approx_equal( (s.count(True),s.size()), (1000,2048))
mam.apply_mask_to_map('map_manager')
s = (mam.map_manager().map_data() > 0.)
assert approx_equal( (s.count(True),s.size()), (640,2048))
s = (mam.map_manager().map_data() != 0.)
assert approx_equal( (s.count(True),s.size()), (1000,2048))
assert approx_equal ((mam.map_manager().map_data()[225]),-0.0418027862906)
mam.apply_mask_to_map('map_manager',mask_id='mask')
s = (mam.map_manager().map_data() > 0.)
assert approx_equal( (s.count(True),s.size()), (640,2048))
s = (mam.map_manager().map_data() != 0.)
assert approx_equal( (s.count(True),s.size()), (1000,2048))
assert approx_equal ((mam.map_manager().map_data()[225]),-0.0418027862906)
mam.apply_mask_to_maps()
s = (mam.map_manager().map_data() > 0.)
assert approx_equal( (s.count(True),s.size()), (640,2048))
s = (mam.map_manager().map_data() != 0.)
assert approx_equal( (s.count(True),s.size()), (1000,2048))
assert approx_equal ((mam.map_manager().map_data()[225]),-0.0418027862906)
mam.apply_mask_to_maps(set_outside_to_mean_inside=True)
s = (mam.map_manager().map_data() > 0.)
assert approx_equal( (s.count(True),s.size()), (1688,2048))
s = (mam.map_manager().map_data() != 0.)
assert approx_equal( (s.count(True),s.size()), (2048,2048))
assert approx_equal ((mam.map_manager().map_data()[2047]),-0.0759598612785)
s = (mam.get_map_manager_by_id('mask').map_data() > 0).as_1d()
inside = mam.map_manager().map_data().as_1d().select(s)
outside = mam.map_manager().map_data().as_1d().select(~s)
assert approx_equal ((inside.min_max_mean().max,outside.min_max_mean().max),
(0.335603952408,0.0239064293122))
mmm=map_model_manager()
mmm.generate_map(box_cushion=0,wrapping=True)
mam=mmm
mam_dc=mam.deep_copy()
new_mm_1=mam.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((18, 25, 20),(18, 25, 20)))
dc=mam_dc.deep_copy()
dc.map_manager().set_wrapping(False)
map_coeffs = dc.map_manager().map_as_fourier_coefficients(d_min=3)
from cctbx.development.create_models_or_maps import generate_map
new_mm_1 = generate_map(map_coeffs=map_coeffs,
d_min=3,
low_resolution_real_space_noise_fraction=1,
high_resolution_real_space_noise_fraction=50,
map_manager=dc.map_manager(),
random_seed=124321)
new_mm_2 = generate_map(map_coeffs=map_coeffs,
d_min=3,
low_resolution_real_space_noise_fraction=1,
high_resolution_real_space_noise_fraction=50,
map_manager=dc.map_manager(),
random_seed=734119)
dc.add_map_manager_by_id(new_mm_1,'map_manager_1')
dc.add_map_manager_by_id(new_mm_2,'map_manager_2')
cc=dc.map_map_cc()
fsc_curve=dc.map_map_fsc()
dc.set_log(sys.stdout)
dc.local_fsc(n_boxes = 1)
dc=mam_dc.deep_copy()
dc.duplicate_map_manager(map_id='map_manager',new_map_id='filtered')
dc.resolution_filter(d_min=3.5, d_max=10, map_id='filtered')
dc.create_mask_around_atoms()
fsc_curve=dc.map_map_fsc(
map_id_1='map_manager',map_id_2='filtered',mask_id='mask',
resolution=3.5,fsc_cutoff = 0.97)
assert approx_equal(fsc_curve.d_min, 3.93793648601,eps=0.01)
assert approx_equal (fsc_curve.fsc.fsc[-1],0.707536576779)
dc=mam_dc.deep_copy()
dc.duplicate_map_manager(map_id='map_manager',new_map_id='filtered')
dc.resolution_filter(d_min=3.5, d_max=6, map_id='filtered')
cc=dc.map_map_cc('map_manager','filtered')
assert approx_equal(cc , 0.676687646486)
dc=mam_dc.deep_copy()
dc.duplicate_map_manager(map_id='map_manager',new_map_id='filtered')
dc.create_mask_around_density(mask_id='filtered')
cc=dc.map_map_cc('map_manager','filtered',mask_id='mask')
assert approx_equal(cc , 0.422523947639)
mam=mam_dc.deep_copy()
mam.box_all_maps_around_model_and_shift_origin(
selection_string="resseq 221:221")
new_mm_1=mam.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((18, 25, 20),(24, 20, 20)))
new_mam_dc=mam_dc.extract_all_maps_around_model(
selection_string="resseq 221:221")
new_mm_1a=new_mam_dc.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1a.map_data().all()),
((18, 25, 20),(24, 20, 20)))
assert approx_equal(new_mm_1.map_data(),new_mm_1a.map_data())
mam2=mam_dc.deep_copy()
mam2.box_all_maps_around_density_and_shift_origin(box_cushion=0)
new_mm_2=mam2.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_2.map_data().all()),
((18, 25, 20),(16, 23, 18)))
mam2=mam_dc.deep_copy()
mam2_b=mam2.extract_all_maps_around_density(box_cushion=0)
new_mm_2=mam2_b.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_2.map_data().all()),
((18, 25, 20),(16, 23, 18)))
mmm=mam_dc.as_map_model_manager().deep_copy()
mmm.box_all_maps_around_model_and_shift_origin(
selection_string="resseq 221:221")
new_mm_1a=mmm.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1a.map_data().all()),
((24, 20, 20),(24, 20, 20)))
assert approx_equal(new_mm_1.map_data(),new_mm_1a.map_data())
mam.box_all_maps_around_density_and_shift_origin(box_cushion=0)
new_mm_1=mam.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((24, 20 , 20),(22, 18, 18)))
mam1=mam_dc.deep_copy()
mam1.extract_all_maps_around_density(box_cushion=0)
new_mm_1=mam1.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((24, 20 , 20),(18, 25, 20)))
mam.create_mask_around_density(soft_mask=False)
mam.box_all_maps_around_mask_and_shift_origin(box_cushion=3)
new_mm_1=mam.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((24, 20 , 20),(22, 18, 18)))
mam.box_all_maps_with_bounds_and_shift_origin(lower_bounds=(10,10,10),
upper_bounds=(15,15,15))
new_mm_1=mam.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((24, 20, 20),(6, 6, 6)))
mam=mam_dc.deep_copy()
mam_1=mam.extract_all_maps_with_bounds(lower_bounds=(10,10,10),
upper_bounds=(15,15,15))
new_mm_1=mam_1.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((24, 20, 20),(6, 6, 6)))
mam=mam_dc.deep_copy()
mam.box_all_maps_around_unique_and_shift_origin(
molecular_mass=2500,resolution=3)
new_mm_1=mam.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((24, 20, 20),(18, 25, 20)))
mam=mam_dc.deep_copy()
mam_1=mam.extract_all_maps_around_unique(
molecular_mass=2500,resolution=3)
new_mm_1=mam_1.map_manager()
assert approx_equal( (mmm.map_data().all(),new_mm_1.map_data().all()),
((24, 20, 20),(18, 25, 20)))
mam=mam_dc.deep_copy()
mam.box_all_maps_with_bounds_and_shift_origin(lower_bounds=(2,2,2),
upper_bounds=(17,17,17))
print("mam:",mam.model().get_sites_cart()[0],mam.map_manager().origin_is_zero())
box_mam=mam.extract_all_maps_with_bounds(lower_bounds=(10,10,10),
upper_bounds=(15,15,15))
box_model=box_mam.model()
matched_box_model=mam.get_model_from_other(box_mam)
assert approx_equal(matched_box_model.get_sites_cart()[0],mam.model().get_sites_cart()[0])
mam=mam_dc.deep_copy()
ma=mam.map_as_fourier_coefficients(d_min=3)
assert approx_equal(ma.d_min(),3.01655042414)
mam.add_map_from_fourier_coefficients(ma, map_id='new_map_manager')
cc=flex.linear_correlation(
mam.get_map_manager_by_id('map_manager').map_data().as_1d(),
mam.get_map_manager_by_id('new_map_manager').map_data().as_1d()).coefficient()
assert (cc >= 0.99)
dc=mam_dc.extract_all_maps_around_model(
selection_string="(name ca or name cb or name c or name o) "+
"and resseq 221:221", box_cushion=0)
cc=dc.map_model_cc(resolution=3)
assert approx_equal (cc, 0.817089390421)
dc.remove_model_outside_map(boundary=0)
assert (mam_dc.model().get_sites_cart().size(),
dc.model().get_sites_cart().size()) == (86, 4)
dc=mam_dc.extract_all_maps_around_model(
selection_string="(name ca or name cb or name c or name o) "+
"and resseq 221:221", box_cushion=0)
actual_model=dc.model().deep_copy()
working_model=dc.model().deep_copy()
working_model.set_shift_cart((0,0,0))
working_model.set_sites_cart(working_model.get_sites_cart()-actual_model.shift_cart())
dc.shift_any_model_to_match(working_model)
assert approx_equal (actual_model.get_sites_cart()[0],working_model.get_sites_cart()[0])
if __name__ == "__main__":
args = sys.argv[1:]
import libtbx.load_env
if not args:
file_name = libtbx.env.under_dist(
module_name = "iotbx",
path = "ccp4_map/tst_input.map")
args = [file_name]
if libtbx.env.has_module("phenix"):
exercise(file_name = args[0])
else:
print("Skipped: Requires phenix module")
print ("OK")
| true | true |
f725ad5a075727936af4a26120e61b5c25cd3bc9 | 193,257 | py | Python | backend/mips_to_c/src/translate.py | Rainchus/decomp.me | 607aab29a18656dcbc0443505ef2944c10afa085 | [
"MIT"
] | null | null | null | backend/mips_to_c/src/translate.py | Rainchus/decomp.me | 607aab29a18656dcbc0443505ef2944c10afa085 | [
"MIT"
] | null | null | null | backend/mips_to_c/src/translate.py | Rainchus/decomp.me | 607aab29a18656dcbc0443505ef2944c10afa085 | [
"MIT"
] | null | null | null | import abc
from collections import defaultdict
from contextlib import contextmanager
from dataclasses import dataclass, field, replace
import math
import struct
import sys
import traceback
import typing
from typing import (
AbstractSet,
Callable,
Collection,
DefaultDict,
Dict,
Iterator,
List,
Mapping,
Optional,
Set,
Tuple,
Union,
)
from .c_types import CType, TypeMap
from .demangle_codewarrior import parse as demangle_codewarrior_parse, CxxSymbol
from .error import DecompFailure, static_assert_unreachable
from .flow_graph import (
ArchFlowGraph,
FlowGraph,
Function,
Node,
ReturnNode,
SwitchNode,
TerminalNode,
locs_clobbered_until_dominator,
)
from .ir_pattern import IrPattern, simplify_ir_patterns
from .options import CodingStyle, Formatter, Options, Target
from .parse_file import AsmData, AsmDataEntry
from .parse_instruction import (
ArchAsm,
Argument,
AsmAddressMode,
AsmGlobalSymbol,
AsmLiteral,
BinOp,
Instruction,
InstrProcessingFailure,
Macro,
Register,
StackLocation,
current_instr,
)
from .types import (
AccessPath,
FunctionParam,
FunctionSignature,
StructDeclaration,
Type,
TypePool,
)
InstrSet = Collection[str]
InstrMap = Mapping[str, Callable[["InstrArgs"], "Expression"]]
StmtInstrMap = Mapping[str, Callable[["InstrArgs"], "Statement"]]
CmpInstrMap = Mapping[str, Callable[["InstrArgs"], "Condition"]]
StoreInstrMap = Mapping[str, Callable[["InstrArgs"], Optional["StoreStmt"]]]
MaybeInstrMap = Mapping[str, Callable[["InstrArgs"], Optional["Expression"]]]
PairInstrMap = Mapping[str, Callable[["InstrArgs"], Tuple["Expression", "Expression"]]]
ImplicitInstrMap = Mapping[str, Tuple[Register, Callable[["InstrArgs"], "Expression"]]]
PpcCmpInstrMap = Mapping[str, Callable[["InstrArgs", str], "Expression"]]
class Arch(ArchFlowGraph):
instrs_ignore: InstrSet = set()
instrs_store: StoreInstrMap = {}
instrs_store_update: StoreInstrMap = {}
instrs_load_update: InstrMap = {}
instrs_branches: CmpInstrMap = {}
instrs_float_branches: InstrSet = set()
instrs_float_comp: CmpInstrMap = {}
instrs_ppc_compare: PpcCmpInstrMap = {}
instrs_jumps: InstrSet = set()
instrs_fn_call: InstrSet = set()
instrs_no_dest: StmtInstrMap = {}
instrs_hi_lo: PairInstrMap = {}
instrs_source_first: InstrMap = {}
instrs_destination_first: InstrMap = {}
instrs_implicit_destination: ImplicitInstrMap = {}
@abc.abstractmethod
def function_abi(
self,
fn_sig: FunctionSignature,
likely_regs: Dict[Register, bool],
*,
for_call: bool,
) -> "Abi":
"""
Compute stack positions/registers used by a function based on its type
information. Also computes a list of registers that may contain arguments,
if the function has varargs or an unknown/incomplete type.
"""
...
@abc.abstractmethod
def function_return(self, expr: "Expression") -> Dict[Register, "Expression"]:
"""
Compute register location(s) & values that will hold the return value
of the function call `expr`.
This must have a value for each register in `all_return_regs` in order to stay
consistent with `Instruction.outputs`. This is why we can't use the
function's return type, even though it may be more accurate.
"""
...
# These are defined here to avoid a circular import in flow_graph.py
ir_patterns: List[IrPattern] = []
def simplify_ir(self, flow_graph: FlowGraph) -> None:
simplify_ir_patterns(self, flow_graph, self.ir_patterns)
ASSOCIATIVE_OPS: Set[str] = {"+", "&&", "||", "&", "|", "^", "*"}
COMPOUND_ASSIGNMENT_OPS: Set[str] = {"+", "-", "*", "/", "%", "&", "|", "^", "<<", ">>"}
PSEUDO_FUNCTION_OPS: Set[str] = {"MULT_HI", "MULTU_HI", "DMULT_HI", "DMULTU_HI", "CLZ"}
def as_type(expr: "Expression", type: Type, silent: bool) -> "Expression":
type = type.weaken_void_ptr()
ptr_target_type = type.get_pointer_target()
if expr.type.unify(type):
if silent or isinstance(expr, Literal):
return expr
elif ptr_target_type is not None:
ptr_target_type_size = ptr_target_type.get_size_bytes()
field_path, field_type, _ = expr.type.get_deref_field(
0, target_size=ptr_target_type_size
)
if field_path is not None and field_type.unify(ptr_target_type):
expr = AddressOf(
StructAccess(
struct_var=expr,
offset=0,
target_size=ptr_target_type_size,
field_path=field_path,
stack_info=None,
type=field_type,
),
type=type,
)
if silent:
return expr
return Cast(expr=expr, reinterpret=True, silent=False, type=type)
def as_f32(expr: "Expression") -> "Expression":
return as_type(expr, Type.f32(), True)
def as_f64(expr: "Expression") -> "Expression":
return as_type(expr, Type.f64(), True)
def as_sintish(expr: "Expression", *, silent: bool = False) -> "Expression":
return as_type(expr, Type.sintish(), silent)
def as_uintish(expr: "Expression") -> "Expression":
return as_type(expr, Type.uintish(), False)
def as_u32(expr: "Expression") -> "Expression":
return as_type(expr, Type.u32(), False)
def as_s64(expr: "Expression", *, silent: bool = False) -> "Expression":
return as_type(expr, Type.s64(), silent)
def as_u64(expr: "Expression", *, silent: bool = False) -> "Expression":
return as_type(expr, Type.u64(), silent)
def as_intish(expr: "Expression") -> "Expression":
return as_type(expr, Type.intish(), True)
def as_int64(expr: "Expression") -> "Expression":
return as_type(expr, Type.int64(), True)
def as_intptr(expr: "Expression") -> "Expression":
return as_type(expr, Type.intptr(), True)
def as_ptr(expr: "Expression") -> "Expression":
return as_type(expr, Type.ptr(), True)
def as_function_ptr(expr: "Expression") -> "Expression":
return as_type(expr, Type.ptr(Type.function()), True)
@dataclass
class StackInfo:
function: Function
global_info: "GlobalInfo"
flow_graph: FlowGraph
allocated_stack_size: int = 0
is_leaf: bool = True
is_variadic: bool = False
uses_framepointer: bool = False
subroutine_arg_top: int = 0
callee_save_regs: Set[Register] = field(default_factory=set)
callee_save_reg_region: Tuple[int, int] = (0, 0)
unique_type_map: Dict[Tuple[str, object], "Type"] = field(default_factory=dict)
local_vars: List["LocalVar"] = field(default_factory=list)
temp_vars: List["EvalOnceStmt"] = field(default_factory=list)
phi_vars: List["PhiExpr"] = field(default_factory=list)
reg_vars: Dict[Register, "RegisterVar"] = field(default_factory=dict)
used_reg_vars: Set[Register] = field(default_factory=set)
arguments: List["PassedInArg"] = field(default_factory=list)
temp_name_counter: Dict[str, int] = field(default_factory=dict)
nonzero_accesses: Set["Expression"] = field(default_factory=set)
param_names: Dict[int, str] = field(default_factory=dict)
stack_pointer_type: Optional[Type] = None
replace_first_arg: Optional[Tuple[str, Type]] = None
weak_stack_var_types: Dict[int, Type] = field(default_factory=dict)
weak_stack_var_locations: Set[int] = field(default_factory=set)
def temp_var(self, prefix: str) -> str:
counter = self.temp_name_counter.get(prefix, 0) + 1
self.temp_name_counter[prefix] = counter
return prefix + (f"_{counter}" if counter > 1 else "")
def in_subroutine_arg_region(self, location: int) -> bool:
if self.global_info.arch.arch == Target.ArchEnum.PPC:
return False
if self.is_leaf:
return False
assert self.subroutine_arg_top is not None
return location < self.subroutine_arg_top
def in_callee_save_reg_region(self, location: int) -> bool:
lower_bound, upper_bound = self.callee_save_reg_region
if lower_bound <= location < upper_bound:
return True
# PPC saves LR in the header of the previous stack frame
if (
self.global_info.arch.arch == Target.ArchEnum.PPC
and location == self.allocated_stack_size + 4
):
return True
return False
def location_above_stack(self, location: int) -> bool:
return location >= self.allocated_stack_size
def add_known_param(self, offset: int, name: Optional[str], type: Type) -> None:
# A common pattern in C for OOP-style polymorphism involves casting a general "base" struct
# to a specific "class" struct, where the first member of the class struct is the base struct.
#
# For the first argument of the function, if it is a pointer to a base struct, and there
# exists a class struct named after the first part of the function name, assume that
# this pattern is being used. Internally, treat the argument as a pointer to the *class*
# struct, even though it is only a pointer to the *base* struct in the provided context.
if offset == 0 and type.is_pointer() and self.replace_first_arg is None:
namespace = self.function.name.partition("_")[0]
base_struct_type = type.get_pointer_target()
self_struct = self.global_info.typepool.get_struct_by_tag_name(
namespace, self.global_info.typemap
)
if (
self_struct is not None
and base_struct_type is not None
and base_struct_type.is_struct()
):
# Check if `self_struct_type` contains a `base_struct_type` at offset 0
self_struct_type = Type.struct(self_struct)
field_path, field_type, _ = self_struct_type.get_field(
offset=0, target_size=base_struct_type.get_size_bytes()
)
if (
field_path is not None
and field_type.unify(base_struct_type)
and not self_struct_type.unify(base_struct_type)
):
# Success, it looks like `self_struct_type` extends `base_struct_type`.
# By default, name the local var `self`, unless the argument name is `thisx` then use `this`
self.replace_first_arg = (name or "_self", type)
name = "this" if name == "thisx" else "self"
type = Type.ptr(Type.struct(self_struct))
if name:
self.param_names[offset] = name
_, arg = self.get_argument(offset)
self.add_argument(arg)
arg.type.unify(type)
def get_param_name(self, offset: int) -> Optional[str]:
return self.param_names.get(offset)
def add_local_var(self, var: "LocalVar") -> None:
if any(v.value == var.value for v in self.local_vars):
return
self.local_vars.append(var)
# Make sure the local vars stay sorted in order on the stack.
self.local_vars.sort(key=lambda v: v.value)
def add_argument(self, arg: "PassedInArg") -> None:
if any(a.value == arg.value for a in self.arguments):
return
self.arguments.append(arg)
self.arguments.sort(key=lambda a: a.value)
def get_argument(self, location: int) -> Tuple["Expression", "PassedInArg"]:
real_location = location & -4
arg = PassedInArg(
real_location,
copied=True,
stack_info=self,
type=self.unique_type_for("arg", real_location, Type.any_reg()),
)
if real_location == location - 3:
return as_type(arg, Type.int_of_size(8), True), arg
if real_location == location - 2:
return as_type(arg, Type.int_of_size(16), True), arg
return arg, arg
def record_struct_access(self, ptr: "Expression", location: int) -> None:
if location:
self.nonzero_accesses.add(unwrap_deep(ptr))
def has_nonzero_access(self, ptr: "Expression") -> bool:
return unwrap_deep(ptr) in self.nonzero_accesses
def unique_type_for(self, category: str, key: object, default: Type) -> "Type":
key = (category, key)
if key not in self.unique_type_map:
self.unique_type_map[key] = default
return self.unique_type_map[key]
def saved_reg_symbol(self, reg_name: str) -> "GlobalSymbol":
sym_name = "saved_reg_" + reg_name
type = self.unique_type_for("saved_reg", sym_name, Type.any_reg())
return GlobalSymbol(symbol_name=sym_name, type=type)
def should_save(self, expr: "Expression", offset: Optional[int]) -> bool:
expr = early_unwrap(expr)
if isinstance(expr, GlobalSymbol) and (
expr.symbol_name.startswith("saved_reg_") or expr.symbol_name == "sp"
):
return True
if (
isinstance(expr, PassedInArg)
and not expr.copied
and (offset is None or offset == self.allocated_stack_size + expr.value)
):
return True
return False
def get_stack_var(self, location: int, *, store: bool) -> "Expression":
# See `get_stack_info` for explanation
if self.in_callee_save_reg_region(location):
# Some annoying bookkeeping instruction. To avoid
# further special-casing, just return whatever - it won't matter.
return LocalVar(location, type=Type.any_reg(), path=None)
elif self.location_above_stack(location):
ret, arg = self.get_argument(location - self.allocated_stack_size)
if not store:
self.add_argument(arg)
return ret
elif self.in_subroutine_arg_region(location):
return SubroutineArg(location, type=Type.any_reg())
else:
# Local variable
assert self.stack_pointer_type is not None
field_path, field_type, _ = self.stack_pointer_type.get_deref_field(
location, target_size=None
)
# Some variables on the stack are compiler-managed, and aren't declared
# in the original source. These variables can have different types inside
# different blocks, so we track their types but assume that they may change
# on each store.
# TODO: Because the types are tracked in StackInfo instead of RegInfo, it is
# possible that a load could incorrectly use a weak type from a sibling node
# instead of a parent node. A more correct implementation would use similar
# logic to the PhiExpr system. In practice however, storing types in StackInfo
# works well enough because nodes are traversed approximately depth-first.
# TODO: Maybe only do this for certain configurable regions?
# Get the previous type stored in `location`
previous_stored_type = self.weak_stack_var_types.get(location)
if previous_stored_type is not None:
# Check if the `field_type` is compatible with the type of the last store
if not previous_stored_type.unify(field_type):
# The types weren't compatible: mark this `location` as "weak"
# This marker is only used to annotate the output
self.weak_stack_var_locations.add(location)
if store:
# If there's already been a store to `location`, then return a fresh type
field_type = Type.any_field()
else:
# Use the type of the last store instead of the one from `get_deref_field()`
field_type = previous_stored_type
# Track the type last stored at `location`
if store:
self.weak_stack_var_types[location] = field_type
return LocalVar(location, type=field_type, path=field_path)
def maybe_get_register_var(self, reg: Register) -> Optional["RegisterVar"]:
return self.reg_vars.get(reg)
def add_register_var(self, reg: Register, name: str) -> None:
type = Type.floatish() if reg.is_float() else Type.intptr()
self.reg_vars[reg] = RegisterVar(reg=reg, type=type, name=name)
def use_register_var(self, var: "RegisterVar") -> None:
self.used_reg_vars.add(var.reg)
def is_stack_reg(self, reg: Register) -> bool:
if reg == self.global_info.arch.stack_pointer_reg:
return True
if reg == self.global_info.arch.frame_pointer_reg:
return self.uses_framepointer
return False
def get_struct_type_map(self) -> Dict["Expression", Dict[int, Type]]:
"""Reorganize struct information in unique_type_map by var & offset"""
struct_type_map: Dict[Expression, Dict[int, Type]] = {}
for (category, key), type in self.unique_type_map.items():
if category != "struct":
continue
var, offset = typing.cast(Tuple[Expression, int], key)
if var not in struct_type_map:
struct_type_map[var] = {}
struct_type_map[var][offset] = type
return struct_type_map
def __str__(self) -> str:
return "\n".join(
[
f"Stack info for function {self.function.name}:",
f"Allocated stack size: {self.allocated_stack_size}",
f"Leaf? {self.is_leaf}",
f"Bounds of callee-saved vars region: {self.callee_save_reg_region}",
f"Callee save registers: {self.callee_save_regs}",
]
)
def get_stack_info(
function: Function,
global_info: "GlobalInfo",
flow_graph: FlowGraph,
) -> StackInfo:
arch = global_info.arch
info = StackInfo(function, global_info, flow_graph)
# The goal here is to pick out special instructions that provide information
# about this function's stack setup.
#
# IDO puts local variables *above* the saved registers on the stack, but
# GCC puts local variables *below* the saved registers.
# To support both, we explicitly determine both the upper & lower bounds of the
# saved registers. Then, we estimate the boundary of the subroutine arguments
# by finding the lowest stack offset that is loaded from or computed. (This
# assumes that the compiler will never reuse a section of stack for *both*
# a local variable *and* a subroutine argument.) Anything within the stack frame,
# but outside of these two regions, is considered a local variable.
callee_saved_offsets: List[int] = []
# Track simple literal values stored into registers: MIPS compilers need a temp
# reg to move the stack pointer more than 0x7FFF bytes.
temp_reg_values: Dict[Register, int] = {}
for inst in flow_graph.entry_node().block.instructions:
arch_mnemonic = inst.arch_mnemonic(arch)
if inst.mnemonic in arch.instrs_fn_call:
break
elif arch_mnemonic == "mips:addiu" and inst.args[0] == arch.stack_pointer_reg:
# Moving the stack pointer on MIPS
assert isinstance(inst.args[2], AsmLiteral)
info.allocated_stack_size = abs(inst.args[2].signed_value())
elif (
arch_mnemonic == "mips:subu"
and inst.args[0] == arch.stack_pointer_reg
and inst.args[1] == arch.stack_pointer_reg
and inst.args[2] in temp_reg_values
):
# Moving the stack pointer more than 0x7FFF on MIPS
# TODO: This instruction needs to be ignored later in translation, in the
# same way that `addiu $sp, $sp, N` is ignored in handle_addi_real
assert isinstance(inst.args[2], Register)
info.allocated_stack_size = temp_reg_values[inst.args[2]]
elif arch_mnemonic == "ppc:stwu" and inst.args[0] == arch.stack_pointer_reg:
# Moving the stack pointer on PPC
assert isinstance(inst.args[1], AsmAddressMode)
assert isinstance(inst.args[1].lhs, AsmLiteral)
info.allocated_stack_size = abs(inst.args[1].lhs.signed_value())
elif (
arch_mnemonic == "mips:move"
and inst.args[0] == arch.frame_pointer_reg
and inst.args[1] == arch.stack_pointer_reg
):
# "move fp, sp" very likely means the code is compiled with frame
# pointers enabled; thus fp should be treated the same as sp.
info.uses_framepointer = True
elif (
arch_mnemonic
in [
"mips:sw",
"mips:swc1",
"mips:sdc1",
"ppc:stw",
"ppc:stmw",
"ppc:stfd",
"ppc:psq_st",
]
and isinstance(inst.args[0], Register)
and inst.args[0] in arch.saved_regs
and isinstance(inst.args[1], AsmAddressMode)
and inst.args[1].rhs == arch.stack_pointer_reg
and (
inst.args[0] not in info.callee_save_regs
or arch_mnemonic == "ppc:psq_st"
)
):
# Initial saving of callee-save register onto the stack.
if inst.args[0] in (arch.return_address_reg, Register("r0")):
# Saving the return address on the stack.
info.is_leaf = False
# The registers & their stack accesses must be matched up in ArchAsm.parse
for reg, mem in zip(inst.inputs, inst.outputs):
if isinstance(reg, Register) and isinstance(mem, StackLocation):
assert mem.symbolic_offset is None
stack_offset = mem.offset
if arch_mnemonic != "ppc:psq_st":
# psq_st instructions store the same register as stfd, just
# as packed singles instead. Prioritize the stfd.
info.callee_save_regs.add(reg)
callee_saved_offsets.append(stack_offset)
elif arch_mnemonic == "ppc:mflr" and inst.args[0] == Register("r0"):
info.is_leaf = False
elif arch_mnemonic == "mips:li" and inst.args[0] in arch.temp_regs:
assert isinstance(inst.args[0], Register)
assert isinstance(inst.args[1], AsmLiteral)
temp_reg_values[inst.args[0]] = inst.args[1].value
elif (
arch_mnemonic == "mips:ori"
and inst.args[0] == inst.args[1]
and inst.args[0] in temp_reg_values
):
assert isinstance(inst.args[0], Register)
assert isinstance(inst.args[2], AsmLiteral)
temp_reg_values[inst.args[0]] |= inst.args[2].value
if not info.is_leaf:
# Iterate over the whole function, not just the first basic block,
# to estimate the boundary for the subroutine argument region
info.subroutine_arg_top = info.allocated_stack_size
for node in flow_graph.nodes:
for inst in node.block.instructions:
arch_mnemonic = inst.arch_mnemonic(arch)
if (
arch_mnemonic in ["mips:lw", "mips:lwc1", "mips:ldc1", "ppc:lwz"]
and isinstance(inst.args[1], AsmAddressMode)
and inst.args[1].rhs == arch.stack_pointer_reg
and inst.args[1].lhs_as_literal() >= 16
):
info.subroutine_arg_top = min(
info.subroutine_arg_top, inst.args[1].lhs_as_literal()
)
elif (
arch_mnemonic == "mips:addiu"
and inst.args[0] != arch.stack_pointer_reg
and inst.args[1] == arch.stack_pointer_reg
and isinstance(inst.args[2], AsmLiteral)
and inst.args[2].value < info.allocated_stack_size
):
info.subroutine_arg_top = min(
info.subroutine_arg_top, inst.args[2].value
)
# Compute the bounds of the callee-saved register region, including padding
if callee_saved_offsets:
callee_saved_offsets.sort()
bottom = callee_saved_offsets[0]
# Both IDO & GCC save registers in two subregions:
# (a) One for double-sized registers
# (b) One for word-sized registers, padded to a multiple of 8 bytes
# IDO has (a) lower than (b); GCC has (b) lower than (a)
# Check that there are no gaps in this region, other than a single
# 4-byte word between subregions.
top = bottom
internal_padding_added = False
for offset in callee_saved_offsets:
if offset != top:
if not internal_padding_added and offset == top + 4:
internal_padding_added = True
else:
raise DecompFailure(
f"Gap in callee-saved word stack region. "
f"Saved: {callee_saved_offsets}, "
f"gap at: {offset} != {top}."
)
top = offset + 4
info.callee_save_reg_region = (bottom, top)
# Subroutine arguments must be at the very bottom of the stack, so they
# must come after the callee-saved region
info.subroutine_arg_top = min(info.subroutine_arg_top, bottom)
# Use a struct to represent the stack layout. If the struct is provided in the context,
# its fields will be used for variable types & names.
stack_struct_name = f"_mips2c_stack_{function.name}"
stack_struct = global_info.typepool.get_struct_by_tag_name(
stack_struct_name, global_info.typemap
)
if stack_struct is not None:
if stack_struct.size != info.allocated_stack_size:
raise DecompFailure(
f"Function {function.name} has a provided stack type {stack_struct_name} "
f"with size {stack_struct.size}, but the detected stack size was "
f"{info.allocated_stack_size}."
)
else:
stack_struct = StructDeclaration.unknown(
global_info.typepool,
size=info.allocated_stack_size,
tag_name=stack_struct_name,
)
# Mark the struct as a stack struct so we never try to use a reference to the struct itself
stack_struct.is_stack = True
stack_struct.new_field_prefix = "sp"
# This acts as the type of the $sp register
info.stack_pointer_type = Type.ptr(Type.struct(stack_struct))
return info
def format_hex(val: int) -> str:
return format(val, "x").upper()
def escape_byte(b: int) -> bytes:
table = {
b"\0": b"\\0",
b"\b": b"\\b",
b"\f": b"\\f",
b"\n": b"\\n",
b"\r": b"\\r",
b"\t": b"\\t",
b"\v": b"\\v",
b"\\": b"\\\\",
b'"': b'\\"',
}
bs = bytes([b])
if bs in table:
return table[bs]
if b < 0x20 or b in (0xFF, 0x7F):
return f"\\x{b:02x}".encode("ascii")
return bs
@dataclass(eq=False)
class Var:
stack_info: StackInfo = field(repr=False)
prefix: str
num_usages: int = 0
name: Optional[str] = None
def format(self, fmt: Formatter) -> str:
if self.name is None:
self.name = self.stack_info.temp_var(self.prefix)
return self.name
def __str__(self) -> str:
return "<temp>"
class Expression(abc.ABC):
type: Type
@abc.abstractmethod
def dependencies(self) -> List["Expression"]:
...
def use(self) -> None:
"""Mark an expression as "will occur in the output". Various subclasses
override this to provide special behavior; for instance, EvalOnceExpr
checks if it occurs more than once in the output and if so emits a temp.
It is important to get the number of use() calls correct:
* if use() is called but the expression is not emitted, it may cause
function calls to be silently dropped.
* if use() is not called but the expression is emitted, it may cause phi
variables to be printed as unnamed-phi($reg), without any assignment
to that phi.
* if use() is called once but the expression is emitted twice, it may
cause function calls to be duplicated."""
for expr in self.dependencies():
expr.use()
@abc.abstractmethod
def format(self, fmt: Formatter) -> str:
...
def __str__(self) -> str:
"""Stringify an expression for debug purposes. The output can change
depending on when this is called, e.g. because of EvalOnceExpr state.
To avoid using it by accident, output is quoted."""
fmt = Formatter(debug=True)
return '"' + self.format(fmt) + '"'
class Condition(Expression):
@abc.abstractmethod
def negated(self) -> "Condition":
...
class Statement(abc.ABC):
@abc.abstractmethod
def should_write(self) -> bool:
...
@abc.abstractmethod
def format(self, fmt: Formatter) -> str:
...
def __str__(self) -> str:
"""Stringify a statement for debug purposes. The output can change
depending on when this is called, e.g. because of EvalOnceExpr state.
To avoid using it by accident, output is quoted."""
fmt = Formatter(debug=True)
return '"' + self.format(fmt) + '"'
@dataclass(frozen=True, eq=False)
class ErrorExpr(Condition):
desc: Optional[str] = None
type: Type = field(default_factory=Type.any_reg)
def dependencies(self) -> List[Expression]:
return []
def negated(self) -> "Condition":
return self
def format(self, fmt: Formatter) -> str:
if self.desc is not None:
return f"MIPS2C_ERROR({self.desc})"
return "MIPS2C_ERROR()"
@dataclass(frozen=True)
class CommentExpr(Expression):
expr: Expression
type: Type = field(compare=False)
prefix: Optional[str] = None
suffix: Optional[str] = None
def dependencies(self) -> List[Expression]:
return [self.expr]
def format(self, fmt: Formatter) -> str:
expr_str = self.expr.format(fmt)
if fmt.coding_style.comment_style == CodingStyle.CommentStyle.NONE:
return expr_str
prefix_str = f"/* {self.prefix} */ " if self.prefix is not None else ""
suffix_str = f" /* {self.suffix} */" if self.suffix is not None else ""
return f"{prefix_str}{expr_str}{suffix_str}"
@staticmethod
def wrap(
expr: Expression, prefix: Optional[str] = None, suffix: Optional[str] = None
) -> Expression:
if prefix is None and suffix is None:
return expr
return CommentExpr(expr=expr, type=expr.type, prefix=prefix, suffix=suffix)
@dataclass(frozen=True, eq=False)
class SecondF64Half(Expression):
type: Type = field(default_factory=Type.any_reg)
def dependencies(self) -> List[Expression]:
return []
def format(self, fmt: Formatter) -> str:
return "(second half of f64)"
@dataclass(frozen=True, eq=False)
class CarryBit(Expression):
type: Type = field(default_factory=Type.intish)
def dependencies(self) -> List[Expression]:
return []
def format(self, fmt: Formatter) -> str:
return "MIPS2C_CARRY"
@staticmethod
def add_to(expr: Expression) -> "BinaryOp":
return fold_divmod(BinaryOp.intptr(expr, "+", CarryBit()))
@staticmethod
def sub_from(expr: Expression) -> "BinaryOp":
return BinaryOp.intptr(expr, "-", UnaryOp("!", CarryBit(), type=Type.intish()))
@dataclass(frozen=True, eq=False)
class BinaryOp(Condition):
left: Expression
op: str
right: Expression
type: Type
@staticmethod
def int(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_intish(left), op=op, right=as_intish(right), type=Type.intish()
)
@staticmethod
def int64(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_int64(left), op=op, right=as_int64(right), type=Type.int64()
)
@staticmethod
def intptr(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_intptr(left), op=op, right=as_intptr(right), type=Type.intptr()
)
@staticmethod
def icmp(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_intptr(left), op=op, right=as_intptr(right), type=Type.bool()
)
@staticmethod
def scmp(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_sintish(left, silent=True),
op=op,
right=as_sintish(right, silent=True),
type=Type.bool(),
)
@staticmethod
def sintptr_cmp(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_type(left, Type.sintptr(), False),
op=op,
right=as_type(right, Type.sintptr(), False),
type=Type.bool(),
)
@staticmethod
def ucmp(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_uintish(left), op=op, right=as_uintish(right), type=Type.bool()
)
@staticmethod
def uintptr_cmp(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_type(left, Type.uintptr(), False),
op=op,
right=as_type(right, Type.uintptr(), False),
type=Type.bool(),
)
@staticmethod
def fcmp(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_f32(left),
op=op,
right=as_f32(right),
type=Type.bool(),
)
@staticmethod
def dcmp(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_f64(left),
op=op,
right=as_f64(right),
type=Type.bool(),
)
@staticmethod
def sint(
left: Expression, op: str, right: Expression, *, silent: bool = False
) -> "BinaryOp":
return BinaryOp(
left=as_sintish(left, silent=silent),
op=op,
right=as_sintish(right, silent=silent),
type=Type.s32(),
)
@staticmethod
def uint(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_uintish(left), op=op, right=as_uintish(right), type=Type.u32()
)
@staticmethod
def s64(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(left=as_s64(left), op=op, right=as_s64(right), type=Type.s64())
@staticmethod
def u64(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(left=as_u64(left), op=op, right=as_u64(right), type=Type.u64())
@staticmethod
def f32(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_f32(left),
op=op,
right=as_f32(right),
type=Type.f32(),
)
@staticmethod
def f64(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_f64(left),
op=op,
right=as_f64(right),
type=Type.f64(),
)
def is_comparison(self) -> bool:
return self.op in ["==", "!=", ">", "<", ">=", "<="]
def is_floating(self) -> bool:
return self.left.type.is_float() and self.right.type.is_float()
def negated(self) -> "Condition":
if (
self.op in ["&&", "||"]
and isinstance(self.left, Condition)
and isinstance(self.right, Condition)
):
# DeMorgan's Laws
return BinaryOp(
left=self.left.negated(),
op={"&&": "||", "||": "&&"}[self.op],
right=self.right.negated(),
type=Type.bool(),
)
if not self.is_comparison() or (
self.is_floating() and self.op in ["<", ">", "<=", ">="]
):
# Floating-point comparisons cannot be negated in any nice way,
# due to nans.
return UnaryOp("!", self, type=Type.bool())
return BinaryOp(
left=self.left,
op={"==": "!=", "!=": "==", ">": "<=", "<": ">=", ">=": "<", "<=": ">"}[
self.op
],
right=self.right,
type=Type.bool(),
)
def dependencies(self) -> List[Expression]:
return [self.left, self.right]
def format(self, fmt: Formatter) -> str:
left_expr = late_unwrap(self.left)
right_expr = late_unwrap(self.right)
if (
self.is_comparison()
and isinstance(left_expr, Literal)
and not isinstance(right_expr, Literal)
):
return BinaryOp(
left=right_expr,
op=self.op.translate(str.maketrans("<>", "><")),
right=left_expr,
type=self.type,
).format(fmt)
if (
not self.is_floating()
and isinstance(right_expr, Literal)
and right_expr.value < 0
):
if self.op == "+":
neg = Literal(value=-right_expr.value, type=right_expr.type)
sub = BinaryOp(op="-", left=left_expr, right=neg, type=self.type)
return sub.format(fmt)
if self.op in ("&", "|"):
neg = Literal(value=~right_expr.value, type=right_expr.type)
right = UnaryOp("~", neg, type=Type.any_reg())
expr = BinaryOp(op=self.op, left=left_expr, right=right, type=self.type)
return expr.format(fmt)
# For commutative, left-associative operations, strip unnecessary parentheses.
lhs = left_expr.format(fmt)
if (
isinstance(left_expr, BinaryOp)
and left_expr.op == self.op
and self.op in ASSOCIATIVE_OPS
):
lhs = lhs[1:-1]
# For certain operators, use base-10 (decimal) for the RHS
if self.op in ("/", "%") and isinstance(right_expr, Literal):
rhs = right_expr.format(fmt, force_dec=True)
else:
rhs = right_expr.format(fmt)
# These aren't real operators (or functions); format them as a fn call
if self.op in PSEUDO_FUNCTION_OPS:
return f"{self.op}({lhs}, {rhs})"
return f"({lhs} {self.op} {rhs})"
@dataclass(frozen=True, eq=False)
class TernaryOp(Expression):
cond: Condition
left: Expression
right: Expression
type: Type
def dependencies(self) -> List[Expression]:
return [self.cond, self.left, self.right]
def format(self, fmt: Formatter) -> str:
cond_str = simplify_condition(self.cond).format(fmt)
left_str = self.left.format(fmt)
right_str = self.right.format(fmt)
return f"({cond_str} ? {left_str} : {right_str})"
@dataclass(frozen=True, eq=False)
class UnaryOp(Condition):
op: str
expr: Expression
type: Type
def dependencies(self) -> List[Expression]:
return [self.expr]
@staticmethod
def sint(op: str, expr: Expression) -> "UnaryOp":
expr = as_sintish(expr, silent=True)
return UnaryOp(
op=op,
expr=expr,
type=expr.type,
)
def negated(self) -> "Condition":
if self.op == "!" and isinstance(self.expr, (UnaryOp, BinaryOp)):
return self.expr
return UnaryOp("!", self, type=Type.bool())
def format(self, fmt: Formatter) -> str:
# These aren't real operators (or functions); format them as a fn call
if self.op in PSEUDO_FUNCTION_OPS:
return f"{self.op}({self.expr.format(fmt)})"
return f"{self.op}{self.expr.format(fmt)}"
@dataclass(frozen=True, eq=False)
class ExprCondition(Condition):
expr: Expression
type: Type
is_negated: bool = False
def dependencies(self) -> List[Expression]:
return [self.expr]
def negated(self) -> "Condition":
return ExprCondition(self.expr, self.type, not self.is_negated)
def format(self, fmt: Formatter) -> str:
neg = "!" if self.is_negated else ""
return f"{neg}{self.expr.format(fmt)}"
@dataclass(frozen=True, eq=False)
class CommaConditionExpr(Condition):
statements: List["Statement"]
condition: "Condition"
type: Type = Type.bool()
def dependencies(self) -> List[Expression]:
assert False, "CommaConditionExpr should not be used within translate.py"
return []
def negated(self) -> "Condition":
return CommaConditionExpr(self.statements, self.condition.negated())
def format(self, fmt: Formatter) -> str:
comma_joined = ", ".join(
stmt.format(fmt).rstrip(";") for stmt in self.statements
)
return f"({comma_joined}, {self.condition.format(fmt)})"
@dataclass(frozen=True, eq=False)
class Cast(Expression):
expr: Expression
type: Type
reinterpret: bool = False
silent: bool = True
def dependencies(self) -> List[Expression]:
return [self.expr]
def use(self) -> None:
# Try to unify, to make stringification output better.
self.expr.type.unify(self.type)
super().use()
def needed_for_store(self) -> bool:
if not self.reinterpret:
# int <-> float casts should be emitted even for stores.
return True
if not self.expr.type.unify(self.type):
# Emit casts when types fail to unify.
return True
return False
def is_trivial(self) -> bool:
return (
self.reinterpret
and self.expr.type.is_float() == self.type.is_float()
and is_trivial_expression(self.expr)
)
def format(self, fmt: Formatter) -> str:
if self.reinterpret and self.expr.type.is_float() != self.type.is_float():
# This shouldn't happen, but mark it in the output if it does.
if fmt.valid_syntax:
return (
f"MIPS2C_BITWISE({self.type.format(fmt)}, {self.expr.format(fmt)})"
)
return f"(bitwise {self.type.format(fmt)}) {self.expr.format(fmt)}"
if self.reinterpret and (
self.silent
or (is_type_obvious(self.expr) and self.expr.type.unify(self.type))
):
return self.expr.format(fmt)
if fmt.skip_casts:
return self.expr.format(fmt)
# Function casts require special logic because function calls have
# higher precedence than casts
fn_sig = self.type.get_function_pointer_signature()
if fn_sig:
prototype_sig = self.expr.type.get_function_pointer_signature()
if not prototype_sig or not prototype_sig.unify_with_args(fn_sig):
# A function pointer cast is required if the inner expr is not
# a function pointer, or has incompatible argument types
return f"(({self.type.format(fmt)}) {self.expr.format(fmt)})"
if not prototype_sig.return_type.unify(fn_sig.return_type):
# Only cast the return value of the call
return f"({fn_sig.return_type.format(fmt)}) {self.expr.format(fmt)}"
# No cast needed
return self.expr.format(fmt)
return f"({self.type.format(fmt)}) {self.expr.format(fmt)}"
@dataclass(frozen=True, eq=False)
class FuncCall(Expression):
function: Expression
args: List[Expression]
type: Type
def dependencies(self) -> List[Expression]:
return self.args + [self.function]
def format(self, fmt: Formatter) -> str:
# TODO: The function type may have a different number of params than it had
# when the FuncCall was created. Should we warn that there may be the wrong
# number of arguments at this callsite?
args = ", ".join(format_expr(arg, fmt) for arg in self.args)
return f"{self.function.format(fmt)}({args})"
@dataclass(frozen=True, eq=True)
class LocalVar(Expression):
value: int
type: Type = field(compare=False)
path: Optional[AccessPath] = field(compare=False)
def dependencies(self) -> List[Expression]:
return []
def format(self, fmt: Formatter) -> str:
fallback_name = f"unksp{format_hex(self.value)}"
if self.path is None:
return fallback_name
name = StructAccess.access_path_to_field_name(self.path, fmt)
if name.startswith("->"):
return name[2:]
return fallback_name
def toplevel_decl(self, fmt: Formatter) -> Optional[str]:
"""Return a declaration for this LocalVar, if required."""
# If len(self.path) > 2, then this local is an inner field of another
# local, so it doesn't need to be declared.
if (
self.path is None
or len(self.path) != 2
or not isinstance(self.path[1], str)
):
return None
return self.type.to_decl(self.path[1], fmt)
@dataclass(frozen=True, eq=False)
class RegisterVar(Expression):
reg: Register
name: str
type: Type
def dependencies(self) -> List[Expression]:
return []
def format(self, fmt: Formatter) -> str:
return self.name
@dataclass(frozen=True, eq=True)
class PassedInArg(Expression):
value: int
copied: bool = field(compare=False)
stack_info: StackInfo = field(compare=False, repr=False)
type: Type = field(compare=False)
def dependencies(self) -> List[Expression]:
return []
def format(self, fmt: Formatter) -> str:
assert self.value % 4 == 0
name = self.stack_info.get_param_name(self.value)
return name or f"arg{format_hex(self.value // 4)}"
@dataclass(frozen=True, eq=True)
class SubroutineArg(Expression):
value: int
type: Type = field(compare=False)
def dependencies(self) -> List[Expression]:
return []
def format(self, fmt: Formatter) -> str:
return f"subroutine_arg{format_hex(self.value // 4)}"
@dataclass(eq=True, unsafe_hash=True)
class StructAccess(Expression):
# Represents struct_var->offset.
# This has eq=True since it represents a live expression and not an access
# at a certain point in time -- this sometimes helps get rid of phi nodes.
# prevent_later_uses makes sure it's not used after writes/function calls
# that may invalidate it.
struct_var: Expression
offset: int
target_size: Optional[int]
field_path: Optional[AccessPath] = field(compare=False)
stack_info: Optional[StackInfo] = field(compare=False, repr=False)
type: Type = field(compare=False)
checked_late_field_path: bool = field(default=False, compare=False)
def __post_init__(self) -> None:
# stack_info is used to resolve field_path late
assert (
self.stack_info is not None or self.field_path is not None
), "Must provide at least one of (stack_info, field_path)"
self.assert_valid_field_path(self.field_path)
@staticmethod
def assert_valid_field_path(path: Optional[AccessPath]) -> None:
assert path is None or (
path and isinstance(path[0], int)
), "The first element of the field path, if present, must be an int"
@classmethod
def access_path_to_field_name(cls, path: AccessPath, fmt: Formatter) -> str:
"""
Convert an access path into a dereferencing field name, like the following examples:
- `[0, "foo", 3, "bar"]` into `"->foo[3].bar"`
- `[0, 3, "bar"]` into `"[0][3].bar"`
- `[0, 1, 2]` into `"[0][1][2]"
- `[0]` into `"[0]"`
The path must have at least one element, and the first element must be an int.
"""
cls.assert_valid_field_path(path)
output = ""
# Replace an initial "[0]." with "->"
if len(path) >= 2 and path[0] == 0 and isinstance(path[1], str):
output += f"->{path[1]}"
path = path[2:]
for p in path:
if isinstance(p, str):
output += f".{p}"
elif isinstance(p, int):
output += f"[{fmt.format_int(p)}]"
else:
static_assert_unreachable(p)
return output
def dependencies(self) -> List[Expression]:
return [self.struct_var]
def make_reference(self) -> Optional["StructAccess"]:
field_path = self.late_field_path()
if field_path and len(field_path) >= 2 and field_path[-1] == 0:
return replace(self, field_path=field_path[:-1])
return None
def late_field_path(self) -> Optional[AccessPath]:
# If we didn't have a type at the time when the struct access was
# constructed, but now we do, compute field name.
if self.field_path is None and not self.checked_late_field_path:
var = late_unwrap(self.struct_var)
# Format var to recursively resolve any late_field_path it has to
# potentially improve var.type before we look up our field name
var.format(Formatter())
field_path, field_type, _ = var.type.get_deref_field(
self.offset, target_size=self.target_size
)
if field_path is not None:
self.assert_valid_field_path(field_path)
self.field_path = field_path
self.type.unify(field_type)
self.checked_late_field_path = True
return self.field_path
def late_has_known_type(self) -> bool:
if self.late_field_path() is not None:
return True
assert (
self.stack_info is not None
), "StructAccess must have stack_info if field_path isn't set"
if self.offset == 0:
var = late_unwrap(self.struct_var)
if (
not self.stack_info.has_nonzero_access(var)
and isinstance(var, AddressOf)
and isinstance(var.expr, GlobalSymbol)
and var.expr.type_provided
):
return True
return False
def format(self, fmt: Formatter) -> str:
var = late_unwrap(self.struct_var)
has_nonzero_access = False
if self.stack_info is not None:
has_nonzero_access = self.stack_info.has_nonzero_access(var)
field_path = self.late_field_path()
if field_path is not None and field_path != [0]:
has_nonzero_access = True
elif fmt.valid_syntax and (self.offset != 0 or has_nonzero_access):
offset_str = fmt.format_int(self.offset)
return f"MIPS2C_FIELD({var.format(fmt)}, {Type.ptr(self.type).format(fmt)}, {offset_str})"
else:
prefix = "unk" + ("_" if fmt.coding_style.unknown_underscore else "")
field_path = [0, prefix + format_hex(self.offset)]
field_name = self.access_path_to_field_name(field_path, fmt)
# Rewrite `(&x)->y` to `x.y` by stripping `AddressOf` & setting deref=False
deref = True
if (
isinstance(var, AddressOf)
and not var.expr.type.is_array()
and field_name.startswith("->")
):
var = var.expr
field_name = field_name.replace("->", ".", 1)
deref = False
# Rewrite `x->unk0` to `*x` and `x.unk0` to `x`, unless has_nonzero_access
if self.offset == 0 and not has_nonzero_access:
return f"{'*' if deref else ''}{var.format(fmt)}"
return f"{parenthesize_for_struct_access(var, fmt)}{field_name}"
@dataclass(frozen=True, eq=True)
class ArrayAccess(Expression):
# Represents ptr[index]. eq=True for symmetry with StructAccess.
ptr: Expression
index: Expression
type: Type = field(compare=False)
def dependencies(self) -> List[Expression]:
return [self.ptr, self.index]
def format(self, fmt: Formatter) -> str:
base = parenthesize_for_struct_access(self.ptr, fmt)
index = format_expr(self.index, fmt)
return f"{base}[{index}]"
@dataclass(eq=False)
class GlobalSymbol(Expression):
symbol_name: str
type: Type
asm_data_entry: Optional[AsmDataEntry] = None
symbol_in_context: bool = False
type_provided: bool = False
initializer_in_typemap: bool = False
demangled_str: Optional[str] = None
def dependencies(self) -> List[Expression]:
return []
def is_string_constant(self) -> bool:
ent = self.asm_data_entry
if not ent or not ent.is_string:
return False
return len(ent.data) == 1 and isinstance(ent.data[0], bytes)
def format_string_constant(self, fmt: Formatter) -> str:
assert self.is_string_constant(), "checked by caller"
assert self.asm_data_entry and isinstance(self.asm_data_entry.data[0], bytes)
has_trailing_null = False
data = self.asm_data_entry.data[0]
while data and data[-1] == 0:
data = data[:-1]
has_trailing_null = True
data = b"".join(map(escape_byte, data))
strdata = data.decode("utf-8", "backslashreplace")
ret = f'"{strdata}"'
if not has_trailing_null:
ret += " /* not null-terminated */"
return ret
def format(self, fmt: Formatter) -> str:
return self.symbol_name
def potential_array_dim(self, element_size: int) -> Tuple[int, int]:
"""
Using the size of the symbol's `asm_data_entry` and a potential array element
size, return the corresponding array dimension and number of "extra" bytes left
at the end of the symbol's data.
If the extra bytes are nonzero, then it's likely that `element_size` is incorrect.
"""
# If we don't have the .data/.rodata entry for this symbol, we can't guess
# its array dimension. Jump tables are ignored and not treated as arrays.
if self.asm_data_entry is None or self.asm_data_entry.is_jtbl:
return 0, element_size
min_data_size, max_data_size = self.asm_data_entry.size_range_bytes()
if element_size > max_data_size:
# The type is too big for the data (not an array)
return 0, max_data_size
# Check if it's possible that this symbol is not an array, and is just 1 element
if min_data_size <= element_size <= max_data_size and not self.type.is_array():
return 1, 0
array_dim, extra_bytes = divmod(min_data_size, element_size)
if extra_bytes != 0:
# If it's not possible to make an exact multiple of element_size by incorporating
# bytes from the padding, then indicate that in the return value.
padding_bytes = element_size - extra_bytes
if min_data_size + padding_bytes > max_data_size:
return array_dim, extra_bytes
# Include potential padding in the array. Although this is unlikely to match the original C,
# it's much easier to manually remove all or some of these elements than to add them back in.
return max_data_size // element_size, 0
@dataclass(frozen=True, eq=True)
class Literal(Expression):
value: int
type: Type = field(compare=False, default_factory=Type.any)
elide_cast: bool = field(compare=False, default=False)
def dependencies(self) -> List[Expression]:
return []
def format(self, fmt: Formatter, force_dec: bool = False) -> str:
enum_name = self.type.get_enum_name(self.value)
if enum_name is not None:
return enum_name
if self.type.is_likely_float():
if self.type.get_size_bits() == 64:
return format_f64_imm(self.value)
else:
return format_f32_imm(self.value) + "f"
if self.type.is_pointer() and self.value == 0:
return "NULL"
prefix = ""
suffix = ""
if not fmt.skip_casts and not self.elide_cast:
if self.type.is_pointer():
prefix = f"({self.type.format(fmt)})"
if self.type.is_unsigned():
suffix = "U"
if force_dec:
value = str(self.value)
else:
size_bits = self.type.get_size_bits()
v = self.value
# The top 2 bits are tested rather than just the sign bit
# to help prevent N64 VRAM pointers (0x80000000+) turning negative
if (
self.type.is_signed()
and size_bits
and v & (1 << (size_bits - 1))
and v > (3 << (size_bits - 2))
and v < 2 ** size_bits
):
v -= 1 << size_bits
value = fmt.format_int(v, size_bits=size_bits)
return prefix + value + suffix
def likely_partial_offset(self) -> bool:
return self.value % 2 ** 15 in (0, 2 ** 15 - 1) and self.value < 0x1000000
@dataclass(frozen=True, eq=True)
class AddressOf(Expression):
expr: Expression
type: Type = field(compare=False, default_factory=Type.ptr)
def dependencies(self) -> List[Expression]:
return [self.expr]
def format(self, fmt: Formatter) -> str:
if isinstance(self.expr, GlobalSymbol):
if self.expr.is_string_constant():
return self.expr.format_string_constant(fmt)
if self.expr.type.is_array():
return f"{self.expr.format(fmt)}"
if self.expr.type.is_function():
# Functions are automatically converted to function pointers
# without an explicit `&` by the compiler
return f"{self.expr.format(fmt)}"
if isinstance(self.expr, StructAccess):
# Simplify `&x[0]` into `x`
ref = self.expr.make_reference()
if ref:
return f"{ref.format(fmt)}"
return f"&{self.expr.format(fmt)}"
@dataclass(frozen=True)
class Lwl(Expression):
load_expr: Expression
key: Tuple[int, object]
type: Type = field(compare=False, default_factory=Type.any_reg)
def dependencies(self) -> List[Expression]:
return [self.load_expr]
def format(self, fmt: Formatter) -> str:
return f"MIPS2C_LWL({self.load_expr.format(fmt)})"
@dataclass(frozen=True)
class Load3Bytes(Expression):
load_expr: Expression
type: Type = field(compare=False, default_factory=Type.any_reg)
def dependencies(self) -> List[Expression]:
return [self.load_expr]
def format(self, fmt: Formatter) -> str:
if fmt.valid_syntax:
return f"MIPS2C_FIRST3BYTES({self.load_expr.format(fmt)})"
return f"(first 3 bytes) {self.load_expr.format(fmt)}"
@dataclass(frozen=True)
class UnalignedLoad(Expression):
load_expr: Expression
type: Type = field(compare=False, default_factory=Type.any_reg)
def dependencies(self) -> List[Expression]:
return [self.load_expr]
def format(self, fmt: Formatter) -> str:
if fmt.valid_syntax:
return f"MIPS2C_UNALIGNED32({self.load_expr.format(fmt)})"
return f"(unaligned s32) {self.load_expr.format(fmt)}"
@dataclass(frozen=False, eq=False)
class EvalOnceExpr(Expression):
wrapped_expr: Expression
var: Var
type: Type
# True for function calls/errors
emit_exactly_once: bool
# Mutable state:
# True if this EvalOnceExpr should be totally transparent and not emit a variable,
# It may dynamically change from true to false due to forced emissions.
# Initially, it is based on is_trivial_expression.
trivial: bool
# True if this EvalOnceExpr must emit a variable (see RegMeta.force)
forced_emit: bool = False
# The number of expressions that depend on this EvalOnceExpr; we emit a variable
# if this is > 1.
num_usages: int = 0
def dependencies(self) -> List[Expression]:
# (this is a bit iffy since state can change over time, but improves uses_expr)
if self.need_decl():
return []
return [self.wrapped_expr]
def use(self) -> None:
self.num_usages += 1
if self.trivial or (self.num_usages == 1 and not self.emit_exactly_once):
self.wrapped_expr.use()
def force(self) -> None:
# Transition to non-trivial, and mark as used multiple times to force a var.
# TODO: If it was originally trivial, we may previously have marked its
# wrappee used multiple times, even though we now know that it should
# have been marked just once... We could fix that by moving marking of
# trivial EvalOnceExpr's to the very end. At least the consequences of
# getting this wrong are pretty mild -- it just causes extraneous var
# emission in rare cases.
self.trivial = False
self.forced_emit = True
self.use()
self.use()
def need_decl(self) -> bool:
return self.num_usages > 1 and not self.trivial
def format(self, fmt: Formatter) -> str:
if not self.need_decl():
return self.wrapped_expr.format(fmt)
else:
return self.var.format(fmt)
@dataclass(frozen=False, eq=False)
class PhiExpr(Expression):
reg: Register
node: Node
type: Type
used_phis: List["PhiExpr"]
name: Optional[str] = None
num_usages: int = 0
replacement_expr: Optional[Expression] = None
used_by: Optional["PhiExpr"] = None
def dependencies(self) -> List[Expression]:
return []
def get_var_name(self) -> str:
return self.name or f"unnamed-phi({self.reg.register_name})"
def use(self, from_phi: Optional["PhiExpr"] = None) -> None:
if self.num_usages == 0:
self.used_phis.append(self)
self.used_by = from_phi
self.num_usages += 1
if self.used_by != from_phi:
self.used_by = None
if self.replacement_expr is not None:
self.replacement_expr.use()
def propagates_to(self) -> "PhiExpr":
"""Compute the phi that stores to this phi should propagate to. This is
usually the phi itself, but if the phi is only once for the purpose of
computing another phi, we forward the store there directly. This is
admittedly a bit sketchy, in case the phi is in scope here and used
later on... but we have that problem with regular phi assignments as
well."""
if self.used_by is None or self.replacement_expr is not None:
return self
return self.used_by.propagates_to()
def format(self, fmt: Formatter) -> str:
if self.replacement_expr:
return self.replacement_expr.format(fmt)
return self.get_var_name()
@dataclass
class SwitchControl:
control_expr: Expression
jump_table: Optional[GlobalSymbol] = None
offset: int = 0
is_irregular: bool = False
def matches_guard_condition(self, cond: Condition) -> bool:
"""
Return True if `cond` is one of:
- `((control_expr + (-offset)) >= len(jump_table))`, if `offset != 0`
- `(control_expr >= len(jump_table))`, if `offset == 0`
These are the appropriate bounds checks before using `jump_table`.
"""
cmp_expr = simplify_condition(cond)
if not isinstance(cmp_expr, BinaryOp) or cmp_expr.op not in (">=", ">"):
return False
cmp_exclusive = cmp_expr.op == ">"
# The LHS may have been wrapped in a u32 cast
left_expr = late_unwrap(cmp_expr.left)
if isinstance(left_expr, Cast):
left_expr = late_unwrap(left_expr.expr)
if self.offset != 0:
if (
not isinstance(left_expr, BinaryOp)
or late_unwrap(left_expr.left) != late_unwrap(self.control_expr)
or left_expr.op != "+"
or late_unwrap(left_expr.right) != Literal(-self.offset)
):
return False
elif left_expr != late_unwrap(self.control_expr):
return False
right_expr = late_unwrap(cmp_expr.right)
if (
self.jump_table is None
or self.jump_table.asm_data_entry is None
or not self.jump_table.asm_data_entry.is_jtbl
or not isinstance(right_expr, Literal)
):
return False
# Count the number of labels (exclude padding bytes)
jump_table_len = sum(
isinstance(e, str) for e in self.jump_table.asm_data_entry.data
)
return right_expr.value + int(cmp_exclusive) == jump_table_len
@staticmethod
def irregular_from_expr(control_expr: Expression) -> "SwitchControl":
"""
Return a SwitchControl representing a "irregular" switch statement.
The switch does not have a single jump table; instead it is a series of
if statements & other switches.
"""
return SwitchControl(
control_expr=control_expr,
jump_table=None,
offset=0,
is_irregular=True,
)
@staticmethod
def from_expr(expr: Expression) -> "SwitchControl":
"""
Try to convert `expr` into a SwitchControl from one of the following forms:
- `*(&jump_table + (control_expr * 4))`
- `*(&jump_table + ((control_expr + (-offset)) * 4))`
If `offset` is not present, it defaults to 0.
If `expr` does not match, return a thin wrapper around the input expression,
with `jump_table` set to `None`.
"""
# The "error" expression we use if we aren't able to parse `expr`
error_expr = SwitchControl(expr)
# Match `*(&jump_table + (control_expr * 4))`
struct_expr = early_unwrap(expr)
if not isinstance(struct_expr, StructAccess) or struct_expr.offset != 0:
return error_expr
add_expr = early_unwrap(struct_expr.struct_var)
if not isinstance(add_expr, BinaryOp) or add_expr.op != "+":
return error_expr
# Check for either `*(&jump_table + (control_expr * 4))` and `*((control_expr * 4) + &jump_table)`
left_expr, right_expr = early_unwrap(add_expr.left), early_unwrap(
add_expr.right
)
if isinstance(left_expr, AddressOf) and isinstance(
left_expr.expr, GlobalSymbol
):
jtbl_addr_expr, mul_expr = left_expr, right_expr
elif isinstance(right_expr, AddressOf) and isinstance(
right_expr.expr, GlobalSymbol
):
mul_expr, jtbl_addr_expr = left_expr, right_expr
else:
return error_expr
jump_table = jtbl_addr_expr.expr
assert isinstance(jump_table, GlobalSymbol)
if (
not isinstance(mul_expr, BinaryOp)
or mul_expr.op != "*"
or early_unwrap(mul_expr.right) != Literal(4)
):
return error_expr
control_expr = mul_expr.left
# Optionally match `control_expr + (-offset)`
offset = 0
uw_control_expr = early_unwrap(control_expr)
if isinstance(uw_control_expr, BinaryOp) and uw_control_expr.op == "+":
offset_lit = early_unwrap(uw_control_expr.right)
if isinstance(offset_lit, Literal):
control_expr = uw_control_expr.left
offset = -offset_lit.value
# Check that it is really a jump table
if jump_table.asm_data_entry is None or not jump_table.asm_data_entry.is_jtbl:
return error_expr
return SwitchControl(control_expr, jump_table, offset)
@dataclass
class EvalOnceStmt(Statement):
expr: EvalOnceExpr
def need_decl(self) -> bool:
return self.expr.need_decl()
def should_write(self) -> bool:
if self.expr.emit_exactly_once:
return self.expr.num_usages != 1
else:
return self.need_decl()
def format(self, fmt: Formatter) -> str:
val_str = format_expr(elide_casts_for_store(self.expr.wrapped_expr), fmt)
if self.expr.emit_exactly_once and self.expr.num_usages == 0:
return f"{val_str};"
return f"{self.expr.var.format(fmt)} = {val_str};"
@dataclass
class SetPhiStmt(Statement):
phi: PhiExpr
expr: Expression
def should_write(self) -> bool:
expr = self.expr
if isinstance(expr, PhiExpr) and expr.propagates_to() != expr:
# When we have phi1 = phi2, and phi2 is only used in this place,
# the SetPhiStmt for phi2 will store directly to phi1 and we can
# skip this store.
assert expr.propagates_to() == self.phi.propagates_to()
return False
if late_unwrap(expr) == self.phi.propagates_to():
# Elide "phi = phi".
return False
return True
def format(self, fmt: Formatter) -> str:
return format_assignment(self.phi.propagates_to(), self.expr, fmt)
@dataclass
class ExprStmt(Statement):
expr: Expression
def should_write(self) -> bool:
return True
def format(self, fmt: Formatter) -> str:
return f"{format_expr(self.expr, fmt)};"
@dataclass
class StoreStmt(Statement):
source: Expression
dest: Expression
def should_write(self) -> bool:
return True
def format(self, fmt: Formatter) -> str:
dest = self.dest
source = self.source
if (
isinstance(dest, StructAccess) and dest.late_has_known_type()
) or isinstance(dest, (ArrayAccess, LocalVar, RegisterVar, SubroutineArg)):
# Known destination; fine to elide some casts.
source = elide_casts_for_store(source)
return format_assignment(dest, source, fmt)
@dataclass
class CommentStmt(Statement):
contents: str
def should_write(self) -> bool:
return True
def format(self, fmt: Formatter) -> str:
return f"// {self.contents}"
def error_stmt(msg: str) -> ExprStmt:
return ExprStmt(ErrorExpr(msg))
@dataclass(frozen=True)
class AddressMode:
offset: int
rhs: Register
def __str__(self) -> str:
if self.offset:
return f"{self.offset}({self.rhs})"
else:
return f"({self.rhs})"
@dataclass(frozen=True)
class RawSymbolRef:
offset: int
sym: AsmGlobalSymbol
def __str__(self) -> str:
if self.offset:
return f"{self.sym.symbol_name} + {self.offset}"
else:
return self.sym.symbol_name
@dataclass
class RegMeta:
# True if this regdata is unchanged from the start of the block
inherited: bool = False
# True if this regdata is read by some later node
is_read: bool = False
# True if the value derives solely from function call return values
function_return: bool = False
# True if the value derives solely from regdata's with is_read = True,
# function_return = True, or is a passed in argument
uninteresting: bool = False
# True if the regdata must be replaced by variable if it is ever read
force: bool = False
# True if the regdata was assigned by an Instruction marked as in_pattern;
# it was part of a matched IR pattern but couldn't be elided at the time
in_pattern: bool = False
@dataclass
class RegData:
value: Expression
meta: RegMeta
@dataclass
class RegInfo:
stack_info: StackInfo = field(repr=False)
contents: Dict[Register, RegData] = field(default_factory=dict)
read_inherited: Set[Register] = field(default_factory=set)
_active_instr: Optional[Instruction] = None
def __getitem__(self, key: Register) -> Expression:
if self._active_instr is not None and key not in self._active_instr.inputs:
lineno = self._active_instr.meta.lineno
return ErrorExpr(f"Read from unset register {key} on line {lineno}")
if key == Register("zero"):
return Literal(0)
data = self.contents.get(key)
if data is None:
return ErrorExpr(f"Read from unset register {key}")
ret = data.value
data.meta.is_read = True
if data.meta.inherited:
self.read_inherited.add(key)
if isinstance(ret, PassedInArg) and not ret.copied:
# Create a new argument object to better distinguish arguments we
# are called with from arguments passed to subroutines. Also, unify
# the argument's type with what we can guess from the register used.
val, arg = self.stack_info.get_argument(ret.value)
self.stack_info.add_argument(arg)
val.type.unify(ret.type)
return val
if data.meta.force:
assert isinstance(ret, EvalOnceExpr)
ret.force()
return ret
def __contains__(self, key: Register) -> bool:
return key in self.contents
def __setitem__(self, key: Register, value: Expression) -> None:
self.set_with_meta(key, value, RegMeta())
def set_with_meta(self, key: Register, value: Expression, meta: RegMeta) -> None:
if self._active_instr is not None and key not in self._active_instr.outputs:
raise DecompFailure(f"Undeclared write to {key} in {self._active_instr}")
self.unchecked_set_with_meta(key, value, meta)
def unchecked_set_with_meta(
self, key: Register, value: Expression, meta: RegMeta
) -> None:
assert key != Register("zero")
self.contents[key] = RegData(value, meta)
def __delitem__(self, key: Register) -> None:
assert key != Register("zero")
del self.contents[key]
def get_raw(self, key: Register) -> Optional[Expression]:
data = self.contents.get(key)
return data.value if data is not None else None
def get_meta(self, key: Register) -> Optional[RegMeta]:
data = self.contents.get(key)
return data.meta if data is not None else None
@contextmanager
def current_instr(self, instr: Instruction) -> Iterator[None]:
self._active_instr = instr
try:
with current_instr(instr):
yield
finally:
self._active_instr = None
def __str__(self) -> str:
return ", ".join(
f"{k}: {v.value}"
for k, v in sorted(self.contents.items(), key=lambda x: x[0].register_name)
if not self.stack_info.should_save(v.value, None)
)
@dataclass
class BlockInfo:
"""
Contains translated assembly code (to_write), the block's branch condition,
and block's final register states.
"""
to_write: List[Statement]
return_value: Optional[Expression]
switch_control: Optional[SwitchControl]
branch_condition: Optional[Condition]
final_register_states: RegInfo
has_function_call: bool
def __str__(self) -> str:
newline = "\n\t"
return "\n".join(
[
f"Statements: {newline.join(str(w) for w in self.statements_to_write())}",
f"Branch condition: {self.branch_condition}",
f"Final register states: {self.final_register_states}",
]
)
def statements_to_write(self) -> List[Statement]:
return [st for st in self.to_write if st.should_write()]
def get_block_info(node: Node) -> BlockInfo:
ret = node.block.block_info
assert isinstance(ret, BlockInfo)
return ret
@dataclass
class InstrArgs:
raw_args: List[Argument]
regs: RegInfo = field(repr=False)
stack_info: StackInfo = field(repr=False)
def raw_arg(self, index: int) -> Argument:
assert index >= 0
if index >= len(self.raw_args):
raise DecompFailure(
f"Too few arguments for instruction, expected at least {index + 1}"
)
return self.raw_args[index]
def reg_ref(self, index: int) -> Register:
ret = self.raw_arg(index)
if not isinstance(ret, Register):
raise DecompFailure(
f"Expected instruction argument to be a register, but found {ret}"
)
return ret
def imm_value(self, index: int) -> int:
arg = self.full_imm(index)
assert isinstance(arg, Literal)
return arg.value
def reg(self, index: int) -> Expression:
return self.regs[self.reg_ref(index)]
def dreg(self, index: int) -> Expression:
"""Extract a double from a register. This may involve reading both the
mentioned register and the next."""
reg = self.reg_ref(index)
if not reg.is_float():
raise DecompFailure(
f"Expected instruction argument {reg} to be a float register"
)
ret = self.regs[reg]
# PPC: FPR's hold doubles (64 bits), so we don't need to do anything special
if self.stack_info.global_info.arch.arch == Target.ArchEnum.PPC:
return ret
# MIPS: Look at the paired FPR to get the full 64-bit value
if not isinstance(ret, Literal) or ret.type.get_size_bits() == 64:
return ret
reg_num = int(reg.register_name[1:])
if reg_num % 2 != 0:
raise DecompFailure(
"Tried to use a double-precision instruction with odd-numbered float "
f"register {reg}"
)
other = self.regs[Register(f"f{reg_num+1}")]
if not isinstance(other, Literal) or other.type.get_size_bits() == 64:
raise DecompFailure(
f"Unable to determine a value for double-precision register {reg} "
"whose second half is non-static. This is a mips_to_c restriction "
"which may be lifted in the future."
)
value = ret.value | (other.value << 32)
return Literal(value, type=Type.f64())
def cmp_reg(self, key: str) -> Condition:
cond = self.regs[Register(key)]
if not isinstance(cond, Condition):
cond = BinaryOp.icmp(cond, "!=", Literal(0))
return cond
def full_imm(self, index: int) -> Expression:
arg = strip_macros(self.raw_arg(index))
ret = literal_expr(arg, self.stack_info)
return ret
def imm(self, index: int) -> Expression:
ret = self.full_imm(index)
if isinstance(ret, Literal):
return Literal(((ret.value + 0x8000) & 0xFFFF) - 0x8000)
return ret
def unsigned_imm(self, index: int) -> Expression:
ret = self.full_imm(index)
if isinstance(ret, Literal):
return Literal(ret.value & 0xFFFF)
return ret
def hi_imm(self, index: int) -> Argument:
arg = self.raw_arg(index)
if not isinstance(arg, Macro) or arg.macro_name not in ("hi", "ha", "h"):
raise DecompFailure(
f"Got lui/lis instruction with macro other than %hi/@ha/@h: {arg}"
)
return arg.argument
def shifted_imm(self, index: int) -> Expression:
# TODO: Should this be part of hi_imm? Do we need to handle @ha?
raw_imm = self.unsigned_imm(index)
assert isinstance(raw_imm, Literal)
return Literal(raw_imm.value << 16)
def memory_ref(self, index: int) -> Union[AddressMode, RawSymbolRef]:
ret = strip_macros(self.raw_arg(index))
# In MIPS, we want to allow "lw $v0, symbol + 4", which is outputted by
# some disassemblers (like IDA) even though it isn't valid assembly.
# For PPC, we want to allow "lwz $r1, symbol@sda21($r13)" where $r13 is
# assumed to point to the start of a small data area (SDA).
if isinstance(ret, AsmGlobalSymbol):
return RawSymbolRef(offset=0, sym=ret)
if (
isinstance(ret, BinOp)
and ret.op in "+-"
and isinstance(ret.lhs, AsmGlobalSymbol)
and isinstance(ret.rhs, AsmLiteral)
):
sign = 1 if ret.op == "+" else -1
return RawSymbolRef(offset=(ret.rhs.value * sign), sym=ret.lhs)
if not isinstance(ret, AsmAddressMode):
raise DecompFailure(
"Expected instruction argument to be of the form offset($register), "
f"but found {ret}"
)
if not isinstance(ret.lhs, AsmLiteral):
raise DecompFailure(
f"Unable to parse offset for instruction argument {ret}. "
"Expected a constant or a %lo macro."
)
return AddressMode(offset=ret.lhs.signed_value(), rhs=ret.rhs)
def count(self) -> int:
return len(self.raw_args)
def deref(
arg: Union[AddressMode, RawSymbolRef, Expression],
regs: RegInfo,
stack_info: StackInfo,
*,
size: int,
store: bool = False,
) -> Expression:
if isinstance(arg, Expression):
offset = 0
var = arg
elif isinstance(arg, AddressMode):
offset = arg.offset
if stack_info.is_stack_reg(arg.rhs):
return stack_info.get_stack_var(offset, store=store)
var = regs[arg.rhs]
else:
offset = arg.offset
var = stack_info.global_info.address_of_gsym(arg.sym.symbol_name)
# Struct member is being dereferenced.
# Cope slightly better with raw pointers.
if isinstance(var, Literal) and var.value % (2 ** 16) == 0:
var = Literal(var.value + offset, type=var.type)
offset = 0
# Handle large struct offsets.
uw_var = early_unwrap(var)
if isinstance(uw_var, BinaryOp) and uw_var.op == "+":
for base, addend in [(uw_var.left, uw_var.right), (uw_var.right, uw_var.left)]:
if isinstance(addend, Literal) and addend.likely_partial_offset():
offset += addend.value
var = base
uw_var = early_unwrap(var)
break
var.type.unify(Type.ptr())
stack_info.record_struct_access(var, offset)
field_name: Optional[str] = None
type: Type = stack_info.unique_type_for("struct", (uw_var, offset), Type.any())
# Struct access with type information.
array_expr = array_access_from_add(
var, offset, stack_info, target_size=size, ptr=False
)
if array_expr is not None:
return array_expr
field_path, field_type, _ = var.type.get_deref_field(offset, target_size=size)
if field_path is not None:
field_type.unify(type)
type = field_type
else:
field_path = None
return StructAccess(
struct_var=var,
offset=offset,
target_size=size,
field_path=field_path,
stack_info=stack_info,
type=type,
)
def is_trivial_expression(expr: Expression) -> bool:
# Determine whether an expression should be evaluated only once or not.
if isinstance(
expr,
(
EvalOnceExpr,
Literal,
GlobalSymbol,
LocalVar,
PassedInArg,
PhiExpr,
RegisterVar,
SubroutineArg,
),
):
return True
if isinstance(expr, AddressOf):
return all(is_trivial_expression(e) for e in expr.dependencies())
if isinstance(expr, Cast):
return expr.is_trivial()
return False
def is_type_obvious(expr: Expression) -> bool:
"""
Determine whether an expression's type is "obvious", e.g. because the
expression refers to a variable which has a declaration. With perfect type
information this this function would not be needed.
This function may produce wrong results while code is being generated,
since at that point we don't know the final status of EvalOnceExpr's.
"""
if isinstance(
expr,
(
Cast,
Literal,
AddressOf,
LocalVar,
PhiExpr,
PassedInArg,
RegisterVar,
FuncCall,
),
):
return True
if isinstance(expr, EvalOnceExpr):
if expr.need_decl():
return True
return is_type_obvious(expr.wrapped_expr)
return False
def simplify_condition(expr: Expression) -> Expression:
"""
Simplify a boolean expression.
This function may produce wrong results while code is being generated,
since at that point we don't know the final status of EvalOnceExpr's.
"""
if isinstance(expr, EvalOnceExpr) and not expr.need_decl():
return simplify_condition(expr.wrapped_expr)
if isinstance(expr, UnaryOp):
inner = simplify_condition(expr.expr)
if expr.op == "!" and isinstance(inner, Condition):
return inner.negated()
return UnaryOp(expr=inner, op=expr.op, type=expr.type)
if isinstance(expr, BinaryOp):
left = simplify_condition(expr.left)
right = simplify_condition(expr.right)
if isinstance(left, BinaryOp) and left.is_comparison() and right == Literal(0):
if expr.op == "==":
return simplify_condition(left.negated())
if expr.op == "!=":
return left
if (
expr.is_comparison()
and isinstance(left, Literal)
and not isinstance(right, Literal)
):
return BinaryOp(
left=right,
op=expr.op.translate(str.maketrans("<>", "><")),
right=left,
type=expr.type,
)
return BinaryOp(left=left, op=expr.op, right=right, type=expr.type)
return expr
def balanced_parentheses(string: str) -> bool:
"""
Check if parentheses in a string are balanced, ignoring any non-parenthesis
characters. E.g. true for "(x())yz", false for ")(" or "(".
"""
bal = 0
for c in string:
if c == "(":
bal += 1
elif c == ")":
if bal == 0:
return False
bal -= 1
return bal == 0
def format_expr(expr: Expression, fmt: Formatter) -> str:
"""Stringify an expression, stripping unnecessary parentheses around it."""
ret = expr.format(fmt)
if ret.startswith("(") and balanced_parentheses(ret[1:-1]):
return ret[1:-1]
return ret
def format_assignment(dest: Expression, source: Expression, fmt: Formatter) -> str:
"""Stringify `dest = source;`."""
dest = late_unwrap(dest)
source = late_unwrap(source)
if isinstance(source, BinaryOp) and source.op in COMPOUND_ASSIGNMENT_OPS:
rhs = None
if late_unwrap(source.left) == dest:
rhs = source.right
elif late_unwrap(source.right) == dest and source.op in ASSOCIATIVE_OPS:
rhs = source.left
if rhs is not None:
return f"{dest.format(fmt)} {source.op}= {format_expr(rhs, fmt)};"
return f"{dest.format(fmt)} = {format_expr(source, fmt)};"
def parenthesize_for_struct_access(expr: Expression, fmt: Formatter) -> str:
# Nested dereferences may need to be parenthesized. All other
# expressions will already have adequate parentheses added to them.
s = expr.format(fmt)
if (
s.startswith("*")
or s.startswith("&")
or (isinstance(expr, Cast) and expr.needed_for_store())
):
return f"({s})"
return s
def elide_casts_for_store(expr: Expression) -> Expression:
uw_expr = late_unwrap(expr)
if isinstance(uw_expr, Cast) and not uw_expr.needed_for_store():
return elide_casts_for_store(uw_expr.expr)
if isinstance(uw_expr, Literal) and uw_expr.type.is_int():
# Avoid suffixes for unsigned ints
return replace(uw_expr, elide_cast=True)
return uw_expr
def uses_expr(expr: Expression, expr_filter: Callable[[Expression], bool]) -> bool:
if expr_filter(expr):
return True
for e in expr.dependencies():
if uses_expr(e, expr_filter):
return True
return False
def late_unwrap(expr: Expression) -> Expression:
"""
Unwrap EvalOnceExpr's, stopping at variable boundaries.
This function may produce wrong results while code is being generated,
since at that point we don't know the final status of EvalOnceExpr's.
"""
if isinstance(expr, EvalOnceExpr) and not expr.need_decl():
return late_unwrap(expr.wrapped_expr)
if isinstance(expr, PhiExpr) and expr.replacement_expr is not None:
return late_unwrap(expr.replacement_expr)
return expr
def early_unwrap(expr: Expression) -> Expression:
"""
Unwrap EvalOnceExpr's, even past variable boundaries.
This is fine to use even while code is being generated, but disrespects decisions
to use a temp for a value, so use with care.
"""
if (
isinstance(expr, EvalOnceExpr)
and not expr.forced_emit
and not expr.emit_exactly_once
):
return early_unwrap(expr.wrapped_expr)
return expr
def early_unwrap_ints(expr: Expression) -> Expression:
"""
Unwrap EvalOnceExpr's, even past variable boundaries or through int Cast's
This is a bit sketchier than early_unwrap(), but can be used for pattern matching.
"""
uw_expr = early_unwrap(expr)
if isinstance(uw_expr, Cast) and uw_expr.reinterpret and uw_expr.type.is_int():
return early_unwrap_ints(uw_expr.expr)
return uw_expr
def unwrap_deep(expr: Expression) -> Expression:
"""
Unwrap EvalOnceExpr's, even past variable boundaries.
This is generally a sketchy thing to do, try to avoid it. In particular:
- the returned expression is not usable for emission, because it may contain
accesses at an earlier point in time or an expression that should not be repeated.
- just because unwrap_deep(a) == unwrap_deep(b) doesn't mean a and b are
interchangable, because they may be computed in different places.
"""
if isinstance(expr, EvalOnceExpr):
return unwrap_deep(expr.wrapped_expr)
return expr
def literal_expr(arg: Argument, stack_info: StackInfo) -> Expression:
if isinstance(arg, AsmGlobalSymbol):
return stack_info.global_info.address_of_gsym(arg.symbol_name)
if isinstance(arg, AsmLiteral):
return Literal(arg.value)
if isinstance(arg, BinOp):
lhs = literal_expr(arg.lhs, stack_info)
rhs = literal_expr(arg.rhs, stack_info)
return BinaryOp.int(left=lhs, op=arg.op, right=rhs)
raise DecompFailure(f"Instruction argument {arg} must be a literal")
def imm_add_32(expr: Expression) -> Expression:
if isinstance(expr, Literal):
return as_intish(Literal(expr.value + 32))
else:
return BinaryOp.int(expr, "+", Literal(32))
def fn_op(fn_name: str, args: List[Expression], type: Type) -> FuncCall:
fn_sig = FunctionSignature(
return_type=type,
params=[FunctionParam(type=arg.type) for arg in args],
params_known=True,
is_variadic=False,
)
return FuncCall(
function=GlobalSymbol(symbol_name=fn_name, type=Type.function(fn_sig)),
args=args,
type=type,
)
def void_fn_op(fn_name: str, args: List[Expression]) -> ExprStmt:
fn_call = fn_op(fn_name, args, Type.any_reg())
fn_call.use()
return ExprStmt(fn_call)
def load_upper(args: InstrArgs) -> Expression:
arg = args.raw_arg(1)
if not isinstance(arg, Macro):
assert not isinstance(
arg, Literal
), "normalize_instruction should convert lui/lis <literal> to li"
raise DecompFailure(
f"lui/lis argument must be a literal or %hi/@ha macro, found {arg}"
)
hi_arg = args.hi_imm(1)
if (
isinstance(hi_arg, BinOp)
and hi_arg.op in "+-"
and isinstance(hi_arg.lhs, AsmGlobalSymbol)
and isinstance(hi_arg.rhs, AsmLiteral)
):
sym = hi_arg.lhs
offset = hi_arg.rhs.value * (-1 if hi_arg.op == "-" else 1)
elif isinstance(hi_arg, AsmGlobalSymbol):
sym = hi_arg
offset = 0
else:
raise DecompFailure(f"Invalid %hi/@ha argument {hi_arg}")
stack_info = args.stack_info
source = stack_info.global_info.address_of_gsym(sym.symbol_name)
imm = Literal(offset)
return handle_addi_real(args.reg_ref(0), None, source, imm, stack_info)
def handle_convert(expr: Expression, dest_type: Type, source_type: Type) -> Cast:
# int <-> float casts should be explicit
silent = dest_type.data().kind != source_type.data().kind
expr.type.unify(source_type)
return Cast(expr=expr, type=dest_type, silent=silent, reinterpret=False)
def handle_la(args: InstrArgs) -> Expression:
target = args.memory_ref(1)
stack_info = args.stack_info
if isinstance(target, AddressMode):
return handle_addi(
InstrArgs(
raw_args=[args.reg_ref(0), target.rhs, AsmLiteral(target.offset)],
regs=args.regs,
stack_info=args.stack_info,
)
)
var = stack_info.global_info.address_of_gsym(target.sym.symbol_name)
return add_imm(var, Literal(target.offset), stack_info)
def handle_or(left: Expression, right: Expression) -> Expression:
if left == right:
# `or $rD, $rS, $rS` can be used to move $rS into $rD
return left
if isinstance(left, Literal) and isinstance(right, Literal):
if (((left.value & 0xFFFF) == 0 and (right.value & 0xFFFF0000) == 0)) or (
(right.value & 0xFFFF) == 0 and (left.value & 0xFFFF0000) == 0
):
return Literal(value=(left.value | right.value))
# Regular bitwise OR.
return BinaryOp.int(left=left, op="|", right=right)
def handle_sltu(args: InstrArgs) -> Expression:
right = args.reg(2)
if args.reg_ref(1) == Register("zero"):
# (0U < x) is equivalent to (x != 0)
uw_right = early_unwrap(right)
if isinstance(uw_right, BinaryOp) and uw_right.op == "^":
# ((a ^ b) != 0) is equivalent to (a != b)
return BinaryOp.icmp(uw_right.left, "!=", uw_right.right)
return BinaryOp.icmp(right, "!=", Literal(0))
else:
left = args.reg(1)
return BinaryOp.ucmp(left, "<", right)
def handle_sltiu(args: InstrArgs) -> Expression:
left = args.reg(1)
right = args.imm(2)
if isinstance(right, Literal):
value = right.value & 0xFFFFFFFF
if value == 1:
# (x < 1U) is equivalent to (x == 0)
uw_left = early_unwrap(left)
if isinstance(uw_left, BinaryOp) and uw_left.op == "^":
# ((a ^ b) == 0) is equivalent to (a == b)
return BinaryOp.icmp(uw_left.left, "==", uw_left.right)
return BinaryOp.icmp(left, "==", Literal(0))
else:
right = Literal(value)
return BinaryOp.ucmp(left, "<", right)
def handle_addi(args: InstrArgs) -> Expression:
stack_info = args.stack_info
source_reg = args.reg_ref(1)
source = args.reg(1)
imm = args.imm(2)
# `(x + 0xEDCC)` is emitted as `((x + 0x10000) - 0x1234)`,
# i.e. as an `addis` followed by an `addi`
uw_source = early_unwrap(source)
if (
isinstance(uw_source, BinaryOp)
and uw_source.op == "+"
and isinstance(uw_source.right, Literal)
and uw_source.right.value % 0x10000 == 0
and isinstance(imm, Literal)
):
return add_imm(
uw_source.left, Literal(imm.value + uw_source.right.value), stack_info
)
return handle_addi_real(args.reg_ref(0), source_reg, source, imm, stack_info)
def handle_addis(args: InstrArgs) -> Expression:
stack_info = args.stack_info
source_reg = args.reg_ref(1)
source = args.reg(1)
imm = args.shifted_imm(2)
return handle_addi_real(args.reg_ref(0), source_reg, source, imm, stack_info)
def handle_addi_real(
output_reg: Register,
source_reg: Optional[Register],
source: Expression,
imm: Expression,
stack_info: StackInfo,
) -> Expression:
if source_reg is not None and stack_info.is_stack_reg(source_reg):
# Adding to sp, i.e. passing an address.
assert isinstance(imm, Literal)
if stack_info.is_stack_reg(output_reg):
# Changing sp. Just ignore that.
return source
# Keep track of all local variables that we take addresses of.
var = stack_info.get_stack_var(imm.value, store=False)
if isinstance(var, LocalVar):
stack_info.add_local_var(var)
return AddressOf(var, type=var.type.reference())
else:
return add_imm(source, imm, stack_info)
def add_imm(source: Expression, imm: Expression, stack_info: StackInfo) -> Expression:
if imm == Literal(0):
# addiu $reg1, $reg2, 0 is a move
# (this happens when replacing %lo(...) by 0)
return source
elif source.type.is_pointer_or_array():
# Pointer addition (this may miss some pointers that get detected later;
# unfortunately that's hard to do anything about with mips_to_c's single-pass
# architecture).
if isinstance(imm, Literal) and not imm.likely_partial_offset():
array_access = array_access_from_add(
source, imm.value, stack_info, target_size=None, ptr=True
)
if array_access is not None:
return array_access
field_path, field_type, _ = source.type.get_deref_field(
imm.value, target_size=None
)
if field_path is not None:
return AddressOf(
StructAccess(
struct_var=source,
offset=imm.value,
target_size=None,
field_path=field_path,
stack_info=stack_info,
type=field_type,
),
type=field_type.reference(),
)
if isinstance(imm, Literal):
target = source.type.get_pointer_target()
if target:
target_size = target.get_size_bytes()
if target_size and imm.value % target_size == 0:
# Pointer addition.
return BinaryOp(
left=source, op="+", right=as_intish(imm), type=source.type
)
return BinaryOp(left=source, op="+", right=as_intish(imm), type=Type.ptr())
elif isinstance(source, Literal) and isinstance(imm, Literal):
return Literal(source.value + imm.value)
else:
# Regular binary addition.
return BinaryOp.intptr(left=source, op="+", right=imm)
def handle_load(args: InstrArgs, type: Type) -> Expression:
# For now, make the cast silent so that output doesn't become cluttered.
# Though really, it would be great to expose the load types somehow...
size = type.get_size_bytes()
assert size is not None
expr = deref(args.memory_ref(1), args.regs, args.stack_info, size=size)
# Detect rodata constants
if isinstance(expr, StructAccess) and expr.offset == 0:
target = early_unwrap(expr.struct_var)
if (
isinstance(target, AddressOf)
and isinstance(target.expr, GlobalSymbol)
and type.is_likely_float()
):
sym_name = target.expr.symbol_name
ent = args.stack_info.global_info.asm_data_value(sym_name)
if (
ent
and ent.data
and isinstance(ent.data[0], bytes)
and len(ent.data[0]) >= size
and ent.is_readonly
and type.unify(target.expr.type)
):
data = ent.data[0][:size]
val: int
if size == 4:
(val,) = struct.unpack(">I", data)
else:
(val,) = struct.unpack(">Q", data)
return Literal(value=val, type=type)
return as_type(expr, type, silent=True)
def deref_unaligned(
arg: Union[AddressMode, RawSymbolRef],
regs: RegInfo,
stack_info: StackInfo,
*,
store: bool = False,
) -> Expression:
# We don't know the correct size pass to deref. Passing None would signal that we
# are taking an address, cause us to prefer entire substructs as referenced fields,
# which would be confusing. Instead, we lie and pass 1. Hopefully nothing bad will
# happen...
return deref(arg, regs, stack_info, size=1, store=store)
def handle_lwl(args: InstrArgs) -> Expression:
# Unaligned load for the left part of a register (lwl can technically merge with
# a pre-existing lwr, but doesn't in practice, so we treat this as a standard
# destination-first operation)
ref = args.memory_ref(1)
expr = deref_unaligned(ref, args.regs, args.stack_info)
key: Tuple[int, object]
if isinstance(ref, AddressMode):
key = (ref.offset, args.regs[ref.rhs])
else:
key = (ref.offset, ref.sym)
return Lwl(expr, key)
def handle_lwr(args: InstrArgs) -> Expression:
# Unaligned load for the right part of a register. This lwr may merge with an
# existing lwl, if it loads from the same target but with an offset that's +3.
uw_old_value = early_unwrap(args.reg(0))
ref = args.memory_ref(1)
lwl_key: Tuple[int, object]
if isinstance(ref, AddressMode):
lwl_key = (ref.offset - 3, args.regs[ref.rhs])
else:
lwl_key = (ref.offset - 3, ref.sym)
if isinstance(uw_old_value, Lwl) and uw_old_value.key[0] == lwl_key[0]:
return UnalignedLoad(uw_old_value.load_expr)
if ref.offset % 4 == 2:
left_mem_ref = replace(ref, offset=ref.offset - 2)
load_expr = deref_unaligned(left_mem_ref, args.regs, args.stack_info)
return Load3Bytes(load_expr)
return ErrorExpr("Unable to handle lwr; missing a corresponding lwl")
def make_store(args: InstrArgs, type: Type) -> Optional[StoreStmt]:
size = type.get_size_bytes()
assert size is not None
stack_info = args.stack_info
source_reg = args.reg_ref(0)
source_raw = args.regs.get_raw(source_reg)
if type.is_likely_float() and size == 8:
source_val = args.dreg(0)
else:
source_val = args.reg(0)
target = args.memory_ref(1)
is_stack = isinstance(target, AddressMode) and stack_info.is_stack_reg(target.rhs)
if (
is_stack
and source_raw is not None
and stack_info.should_save(source_raw, target.offset)
):
# Elide register preserval.
return None
dest = deref(target, args.regs, stack_info, size=size, store=True)
dest.type.unify(type)
return StoreStmt(source=as_type(source_val, type, silent=is_stack), dest=dest)
def make_storex(args: InstrArgs, type: Type) -> Optional[StoreStmt]:
# "indexed stores" like `stwx rS, rA, rB` write `rS` into `(rA + rB)`
size = type.get_size_bytes()
assert size is not None
source = args.reg(0)
ptr = BinaryOp.intptr(left=args.reg(1), op="+", right=args.reg(2))
# TODO: Can we assume storex's are never used to save registers to the stack?
dest = deref(ptr, args.regs, args.stack_info, size=size, store=True)
dest.type.unify(type)
return StoreStmt(source=as_type(source, type, silent=False), dest=dest)
def handle_swl(args: InstrArgs) -> Optional[StoreStmt]:
# swl in practice only occurs together with swr, so we can treat it as a regular
# store, with the expression wrapped in UnalignedLoad if needed.
source = args.reg(0)
target = args.memory_ref(1)
if not isinstance(early_unwrap(source), UnalignedLoad):
source = UnalignedLoad(source)
dest = deref_unaligned(target, args.regs, args.stack_info, store=True)
return StoreStmt(source=source, dest=dest)
def handle_swr(args: InstrArgs) -> Optional[StoreStmt]:
expr = early_unwrap(args.reg(0))
target = args.memory_ref(1)
if not isinstance(expr, Load3Bytes):
# Elide swr's that don't come from 3-byte-loading lwr's; they probably
# come with a corresponding swl which has already been emitted.
return None
real_target = replace(target, offset=target.offset - 2)
dest = deref_unaligned(real_target, args.regs, args.stack_info, store=True)
return StoreStmt(source=expr, dest=dest)
def handle_sra(args: InstrArgs) -> Expression:
lhs = args.reg(1)
shift = args.imm(2)
if isinstance(shift, Literal) and shift.value in [16, 24]:
expr = early_unwrap(lhs)
pow2 = 1 << shift.value
if isinstance(expr, BinaryOp) and isinstance(expr.right, Literal):
tp = Type.s16() if shift.value == 16 else Type.s8()
rhs = expr.right.value
if expr.op == "<<" and rhs == shift.value:
return as_type(expr.left, tp, silent=False)
elif expr.op == "<<" and rhs > shift.value:
new_shift = fold_mul_chains(
BinaryOp.int(expr.left, "<<", Literal(rhs - shift.value))
)
return as_type(new_shift, tp, silent=False)
elif expr.op == "*" and rhs % pow2 == 0 and rhs != pow2:
mul = BinaryOp.int(expr.left, "*", Literal(value=rhs // pow2))
return as_type(mul, tp, silent=False)
return fold_divmod(
BinaryOp(as_sintish(lhs), ">>", as_intish(shift), type=Type.s32())
)
def handle_conditional_move(args: InstrArgs, nonzero: bool) -> Expression:
op = "!=" if nonzero else "=="
type = Type.any_reg()
return TernaryOp(
BinaryOp.scmp(args.reg(2), op, Literal(0)),
as_type(args.reg(1), type, silent=True),
as_type(args.reg(0), type, silent=True),
type,
)
def format_f32_imm(num: int) -> str:
packed = struct.pack(">I", num & (2 ** 32 - 1))
value = struct.unpack(">f", packed)[0]
if not value or value == 4294967296.0:
# Zero, negative zero, nan, or INT_MAX.
return str(value)
# Write values smaller than 1e-7 / greater than 1e7 using scientific notation,
# and values in between using fixed point.
if abs(math.log10(abs(value))) > 6.9:
fmt_char = "e"
elif abs(value) < 1:
fmt_char = "f"
else:
fmt_char = "g"
def fmt(prec: int) -> str:
"""Format 'value' with 'prec' significant digits/decimals, in either scientific
or regular notation depending on 'fmt_char'."""
ret = ("{:." + str(prec) + fmt_char + "}").format(value)
if fmt_char == "e":
return ret.replace("e+", "e").replace("e0", "e").replace("e-0", "e-")
if "e" in ret:
# The "g" format character can sometimes introduce scientific notation if
# formatting with too few decimals. If this happens, return an incorrect
# value to prevent the result from being used.
#
# Since the value we are formatting is within (1e-7, 1e7) in absolute
# value, it will at least be possible to format with 7 decimals, which is
# less than float precision. Thus, this annoying Python limitation won't
# lead to us outputting numbers with more precision than we really have.
return "0"
return ret
# 20 decimals is more than enough for a float. Start there, then try to shrink it.
prec = 20
while prec > 0:
prec -= 1
value2 = float(fmt(prec))
if struct.pack(">f", value2) != packed:
prec += 1
break
if prec == 20:
# Uh oh, even the original value didn't format correctly. Fall back to str(),
# which ought to work.
return str(value)
ret = fmt(prec)
if "." not in ret:
ret += ".0"
return ret
def format_f64_imm(num: int) -> str:
(value,) = struct.unpack(">d", struct.pack(">Q", num & (2 ** 64 - 1)))
return str(value)
def fold_divmod(original_expr: BinaryOp) -> BinaryOp:
"""
Return a new BinaryOp instance if this one can be simplified to a single / or % op.
This involves simplifying expressions using MULT_HI, MULTU_HI, +, -, <<, >>, and /.
In GCC 2.7.2, the code that generates these instructions is in expmed.c.
See also https://ridiculousfish.com/blog/posts/labor-of-division-episode-i.html
for a modern writeup of a similar algorithm.
This optimization is also used by MWCC and modern compilers (but not IDO).
"""
mult_high_ops = ("MULT_HI", "MULTU_HI")
possible_match_ops = mult_high_ops + ("-", "+", ">>")
# Only operate on integer expressions of certain operations
if original_expr.is_floating() or original_expr.op not in possible_match_ops:
return original_expr
# Use `early_unwrap_ints` instead of `early_unwrap` to ignore Casts to integer types
# Although this discards some extra type information, this function largely ignores
# sign/size information to stay simpler. The result will be made with BinaryOp.int()
# regardless of input types.
expr = original_expr
left_expr = early_unwrap_ints(expr.left)
right_expr = early_unwrap_ints(expr.right)
divisor_shift = 0
# Detect signed power-of-two division: (x >> N) + MIPS2C_CARRY --> x / (1 << N)
if (
isinstance(left_expr, BinaryOp)
and left_expr.op == ">>"
and isinstance(left_expr.right, Literal)
and expr.op == "+"
and isinstance(right_expr, CarryBit)
):
new_denom = 1 << left_expr.right.value
return BinaryOp.sint(
left=left_expr.left,
op="/",
right=Literal(new_denom),
silent=True,
)
# Fold `/` with `>>`: ((x / N) >> M) --> x / (N << M)
# NB: If x is signed, this is only correct if there is a sign-correcting subtraction term
if (
isinstance(left_expr, BinaryOp)
and left_expr.op == "/"
and isinstance(left_expr.right, Literal)
and expr.op == ">>"
and isinstance(right_expr, Literal)
):
new_denom = left_expr.right.value << right_expr.value
if new_denom < (1 << 32):
return BinaryOp.int(
left=left_expr.left,
op="/",
right=Literal(new_denom),
)
# Detect `%`: (x - ((x / y) * y)) --> x % y
if expr.op == "-" and isinstance(right_expr, BinaryOp) and right_expr.op == "*":
div_expr = early_unwrap_ints(right_expr.left)
mod_base = early_unwrap_ints(right_expr.right)
if (
isinstance(div_expr, BinaryOp)
and early_unwrap_ints(div_expr.left) == left_expr
):
# Accept either `(x / y) * y` or `(x >> N) * M` (where `1 << N == M`)
divisor = early_unwrap_ints(div_expr.right)
if (div_expr.op == "/" and divisor == mod_base) or (
div_expr.op == ">>"
and isinstance(divisor, Literal)
and isinstance(mod_base, Literal)
and (1 << divisor.value) == mod_base.value
):
return BinaryOp.int(left=left_expr, op="%", right=right_expr.right)
# Detect dividing by a negative: ((x >> 31) - (x / N)) --> x / -N
if (
expr.op == "-"
and isinstance(left_expr, BinaryOp)
and left_expr.op == ">>"
and early_unwrap_ints(left_expr.right) == Literal(31)
and isinstance(right_expr, BinaryOp)
and right_expr.op == "/"
and isinstance(right_expr.right, Literal)
):
# Swap left_expr & right_expr, but replace the N in right_expr with -N
left_expr, right_expr = (
replace(right_expr, right=Literal(-right_expr.right.value)),
left_expr,
)
# Remove outer error term: ((x / N) + ((x / N) >> 31)) --> x / N
# As N gets close to (1 << 30), this is no longer a negligible error term
if (
expr.op == "+"
and isinstance(left_expr, BinaryOp)
and left_expr.op == "/"
and isinstance(left_expr.right, Literal)
and left_expr.right.value <= (1 << 29)
and isinstance(right_expr, BinaryOp)
and early_unwrap_ints(right_expr.left) == left_expr
and right_expr.op == ">>"
and early_unwrap_ints(right_expr.right) == Literal(31)
):
return left_expr
# Remove outer error term: ((x / N) - (x >> 31)) --> x / N
if (
expr.op == "-"
and isinstance(left_expr, BinaryOp)
and left_expr.op == "/"
and isinstance(left_expr.right, Literal)
and isinstance(right_expr, BinaryOp)
and right_expr.op == ">>"
and early_unwrap_ints(right_expr.right) == Literal(31)
):
div_expr = left_expr
shift_var_expr = early_unwrap_ints(right_expr.left)
div_var_expr = early_unwrap_ints(div_expr.left)
# Check if the LHS of the shift is the same var that we're dividing by
if div_var_expr == shift_var_expr:
if isinstance(div_expr.right, Literal) and div_expr.right.value >= (
1 << 30
):
return BinaryOp.int(
left=div_expr.left,
op=div_expr.op,
right=div_expr.right,
)
return div_expr
# If the var is under 32 bits, the error term may look like `(x << K) >> 31` instead
if (
isinstance(shift_var_expr, BinaryOp)
and early_unwrap_ints(div_expr.left)
== early_unwrap_ints(shift_var_expr.left)
and shift_var_expr.op == "<<"
and isinstance(shift_var_expr.right, Literal)
):
return div_expr
# Shift on the result of the mul: MULT_HI(x, N) >> M, shift the divisor by M
if (
isinstance(left_expr, BinaryOp)
and expr.op == ">>"
and isinstance(right_expr, Literal)
):
divisor_shift += right_expr.value
expr = left_expr
left_expr = early_unwrap_ints(expr.left)
right_expr = early_unwrap_ints(expr.right)
# Normalize MULT_HI(N, x) to MULT_HI(x, N)
if isinstance(left_expr, Literal) and not isinstance(right_expr, Literal):
left_expr, right_expr = right_expr, left_expr
# Remove inner addition: (MULT_HI(x, N) + x) >> M --> MULT_HI(x, N) >> M
# MULT_HI performs signed multiplication, so the `+ x` acts as setting the 32nd bit
# while having a result with the same sign as x.
# We can ignore it because `round_div` can work with arbitrarily large constants
if (
isinstance(left_expr, BinaryOp)
and left_expr.op == "MULT_HI"
and expr.op == "+"
and early_unwrap_ints(left_expr.left) == right_expr
):
expr = left_expr
left_expr = early_unwrap_ints(expr.left)
right_expr = early_unwrap_ints(expr.right)
# Shift on the LHS of the mul: MULT_HI(x >> M, N) --> MULT_HI(x, N) >> M
if (
expr.op in mult_high_ops
and isinstance(left_expr, BinaryOp)
and left_expr.op == ">>"
and isinstance(left_expr.right, Literal)
):
divisor_shift += left_expr.right.value
left_expr = early_unwrap_ints(left_expr.left)
# Instead of checking for the error term precisely, just check that
# the quotient is "close enough" to the integer value
def round_div(x: int, y: int) -> Optional[int]:
if y <= 1:
return None
result = round(x / y)
if x / (y + 1) <= result <= x / (y - 1):
return result
return None
if expr.op in mult_high_ops and isinstance(right_expr, Literal):
denom = round_div(1 << (32 + divisor_shift), right_expr.value)
if denom is not None:
return BinaryOp.int(
left=left_expr,
op="/",
right=Literal(denom),
)
return original_expr
def replace_clz_shift(expr: BinaryOp) -> BinaryOp:
"""
Simplify an expression matching `CLZ(x) >> 5` into `x == 0`,
and further simplify `(a - b) == 0` into `a == b`.
"""
# Check that the outer expression is `>>`
if expr.is_floating() or expr.op != ">>":
return expr
# Match `CLZ(x) >> 5`, or return the original expr
left_expr = early_unwrap_ints(expr.left)
right_expr = early_unwrap_ints(expr.right)
if not (
isinstance(left_expr, UnaryOp)
and left_expr.op == "CLZ"
and isinstance(right_expr, Literal)
and right_expr.value == 5
):
return expr
# If the inner `x` is `(a - b)`, return `a == b`
sub_expr = early_unwrap(left_expr.expr)
if (
isinstance(sub_expr, BinaryOp)
and not sub_expr.is_floating()
and sub_expr.op == "-"
):
return BinaryOp.icmp(sub_expr.left, "==", sub_expr.right)
return BinaryOp.icmp(left_expr.expr, "==", Literal(0, type=left_expr.expr.type))
def replace_bitand(expr: BinaryOp) -> Expression:
"""Detect expressions using `&` for truncating integer casts"""
if not expr.is_floating() and expr.op == "&":
if expr.right == Literal(0xFF):
return as_type(expr.left, Type.int_of_size(8), silent=False)
if expr.right == Literal(0xFFFF):
return as_type(expr.left, Type.int_of_size(16), silent=False)
return expr
def fold_mul_chains(expr: Expression) -> Expression:
"""Simplify an expression involving +, -, * and << to a single multiplication,
e.g. 4*x - x -> 3*x, or x<<2 -> x*4. This includes some logic for preventing
folds of consecutive sll, and keeping multiplications by large powers of two
as bitshifts at the top layer."""
def fold(
expr: Expression, toplevel: bool, allow_sll: bool
) -> Tuple[Expression, int]:
if isinstance(expr, BinaryOp):
lbase, lnum = fold(expr.left, False, (expr.op != "<<"))
rbase, rnum = fold(expr.right, False, (expr.op != "<<"))
if expr.op == "<<" and isinstance(expr.right, Literal) and allow_sll:
# Left-shifts by small numbers are easier to understand if
# written as multiplications (they compile to the same thing).
if toplevel and lnum == 1 and not (1 <= expr.right.value <= 4):
return (expr, 1)
return (lbase, lnum << expr.right.value)
if (
expr.op == "*"
and isinstance(expr.right, Literal)
and (allow_sll or expr.right.value % 2 != 0)
):
return (lbase, lnum * expr.right.value)
if early_unwrap(lbase) == early_unwrap(rbase):
if expr.op == "+":
return (lbase, lnum + rnum)
if expr.op == "-":
return (lbase, lnum - rnum)
if isinstance(expr, UnaryOp) and expr.op == "-" and not toplevel:
base, num = fold(expr.expr, False, True)
return (base, -num)
if (
isinstance(expr, EvalOnceExpr)
and not expr.emit_exactly_once
and not expr.forced_emit
):
base, num = fold(early_unwrap(expr), False, allow_sll)
if num != 1 and is_trivial_expression(base):
return (base, num)
return (expr, 1)
base, num = fold(expr, True, True)
if num == 1:
return expr
return BinaryOp.int(left=base, op="*", right=Literal(num))
def array_access_from_add(
expr: Expression,
offset: int,
stack_info: StackInfo,
*,
target_size: Optional[int],
ptr: bool,
) -> Optional[Expression]:
expr = early_unwrap(expr)
if not isinstance(expr, BinaryOp) or expr.op != "+":
return None
base = expr.left
addend = expr.right
if addend.type.is_pointer_or_array() and not base.type.is_pointer_or_array():
base, addend = addend, base
index: Expression
scale: int
uw_addend = early_unwrap(addend)
if (
isinstance(uw_addend, BinaryOp)
and uw_addend.op == "*"
and isinstance(uw_addend.right, Literal)
):
index = uw_addend.left
scale = uw_addend.right.value
elif (
isinstance(uw_addend, BinaryOp)
and uw_addend.op == "<<"
and isinstance(uw_addend.right, Literal)
):
index = uw_addend.left
scale = 1 << uw_addend.right.value
else:
index = addend
scale = 1
if scale < 0:
scale = -scale
index = UnaryOp.sint("-", index)
target_type = base.type.get_pointer_target()
if target_type is None:
return None
uw_base = early_unwrap(base)
typepool = stack_info.global_info.typepool
# In `&x + index * scale`, if the type of `x` is not known, try to mark it as an array.
# Skip the `scale = 1` case because this often indicates a complex `index` expression,
# and is not actually a 1-byte array lookup.
if (
scale > 1
and offset == 0
and isinstance(uw_base, AddressOf)
and target_type.get_size_bytes() is None
):
inner_type: Optional[Type] = None
if (
isinstance(uw_base.expr, GlobalSymbol)
and uw_base.expr.potential_array_dim(scale)[1] != 0
):
# For GlobalSymbols, use the size of the asm data to check the feasibility of being
# an array with `scale`. This helps be more conservative around fake symbols.
pass
elif scale == 2:
# This *could* be a struct, but is much more likely to be an int
inner_type = Type.int_of_size(16)
elif scale == 4:
inner_type = Type.reg32(likely_float=False)
elif typepool.unk_inference and isinstance(uw_base.expr, GlobalSymbol):
# Make up a struct with a tag name based on the symbol & struct size.
# Although `scale = 8` could indicate an array of longs/doubles, it seems more
# common to be an array of structs.
struct_name = f"_struct_{uw_base.expr.symbol_name}_0x{scale:X}"
struct = typepool.get_struct_by_tag_name(
struct_name, stack_info.global_info.typemap
)
if struct is None:
struct = StructDeclaration.unknown(
typepool, size=scale, tag_name=struct_name
)
elif struct.size != scale:
# This should only happen if there was already a struct with this name in the context
raise DecompFailure(f"sizeof(struct {struct_name}) != {scale:#x}")
inner_type = Type.struct(struct)
if inner_type is not None:
# This might fail, if `uw_base.expr.type` can't be changed to an array
uw_base.expr.type.unify(Type.array(inner_type, dim=None))
# This acts as a backup, and will usually succeed
target_type.unify(inner_type)
if target_type.get_size_bytes() == scale:
# base[index]
pass
else:
# base->subarray[index]
sub_path, sub_type, remaining_offset = base.type.get_deref_field(
offset, target_size=scale, exact=False
)
# Check if the last item in the path is `0`, which indicates the start of an array
# If it is, remove it: it will be replaced by `[index]`
if sub_path is None or len(sub_path) < 2 or sub_path[-1] != 0:
return None
sub_path.pop()
base = StructAccess(
struct_var=base,
offset=offset - remaining_offset,
target_size=None,
field_path=sub_path,
stack_info=stack_info,
type=sub_type,
)
offset = remaining_offset
target_type = sub_type
ret: Expression = ArrayAccess(base, index, type=target_type)
# Add .field if necessary by wrapping ret in StructAccess(AddressOf(...))
ret_ref = AddressOf(ret, type=ret.type.reference())
field_path, field_type, _ = ret_ref.type.get_deref_field(
offset, target_size=target_size
)
if offset != 0 or (target_size is not None and target_size != scale):
ret = StructAccess(
struct_var=ret_ref,
offset=offset,
target_size=target_size,
field_path=field_path,
stack_info=stack_info,
type=field_type,
)
if ptr:
ret = AddressOf(ret, type=ret.type.reference())
return ret
def handle_add(args: InstrArgs) -> Expression:
lhs = args.reg(1)
rhs = args.reg(2)
stack_info = args.stack_info
type = Type.intptr()
# Because lhs & rhs are in registers, it shouldn't be possible for them to be arrays.
# If they are, treat them the same as pointers anyways.
if lhs.type.is_pointer_or_array():
type = Type.ptr()
elif rhs.type.is_pointer_or_array():
type = Type.ptr()
# addiu instructions can sometimes be emitted as addu instead, when the
# offset is too large.
if isinstance(rhs, Literal):
return handle_addi_real(args.reg_ref(0), args.reg_ref(1), lhs, rhs, stack_info)
if isinstance(lhs, Literal):
return handle_addi_real(args.reg_ref(0), args.reg_ref(2), rhs, lhs, stack_info)
expr = BinaryOp(left=as_intptr(lhs), op="+", right=as_intptr(rhs), type=type)
folded_expr = fold_mul_chains(expr)
if isinstance(folded_expr, BinaryOp):
folded_expr = fold_divmod(folded_expr)
if folded_expr is not expr:
return folded_expr
array_expr = array_access_from_add(expr, 0, stack_info, target_size=None, ptr=True)
if array_expr is not None:
return array_expr
return expr
def handle_add_float(args: InstrArgs) -> Expression:
if args.reg_ref(1) == args.reg_ref(2):
two = Literal(1 << 30, type=Type.f32())
return BinaryOp.f32(two, "*", args.reg(1))
return BinaryOp.f32(args.reg(1), "+", args.reg(2))
def handle_add_double(args: InstrArgs) -> Expression:
if args.reg_ref(1) == args.reg_ref(2):
two = Literal(1 << 62, type=Type.f64())
return BinaryOp.f64(two, "*", args.dreg(1))
return BinaryOp.f64(args.dreg(1), "+", args.dreg(2))
def handle_bgez(args: InstrArgs) -> Condition:
expr = args.reg(0)
uw_expr = early_unwrap(expr)
if (
isinstance(uw_expr, BinaryOp)
and uw_expr.op == "<<"
and isinstance(uw_expr.right, Literal)
):
shift = uw_expr.right.value
bitand = BinaryOp.int(uw_expr.left, "&", Literal(1 << (31 - shift)))
return UnaryOp("!", bitand, type=Type.bool())
return BinaryOp.scmp(expr, ">=", Literal(0))
def rlwi_mask(mask_begin: int, mask_end: int) -> int:
# Compute the mask constant used by the rlwi* family of PPC instructions,
# referred to as the `MASK(MB, ME)` function in the processor manual.
# Bit 0 is the MSB, Bit 31 is the LSB
bits_upto: Callable[[int], int] = lambda m: (1 << (32 - m)) - 1
all_ones = 0xFFFFFFFF
if mask_begin <= mask_end:
# Set bits inside the range, fully inclusive
mask = bits_upto(mask_begin) - bits_upto(mask_end + 1)
else:
# Set bits from [31, mask_end] and [mask_begin, 0]
mask = (bits_upto(mask_end + 1) - bits_upto(mask_begin)) ^ all_ones
return mask
def handle_rlwinm(
source: Expression,
shift: int,
mask_begin: int,
mask_end: int,
simplify: bool = True,
) -> Expression:
# TODO: Detect shift + truncate, like `(x << 2) & 0xFFF3` or `(x >> 2) & 0x3FFF`
# The output of the rlwinm instruction is `ROTL(source, shift) & mask`. We write this as
# ((source << shift) & mask) | ((source >> (32 - shift)) & mask)
# and compute both OR operands (upper_bits and lower_bits respectively).
all_ones = 0xFFFFFFFF
mask = rlwi_mask(mask_begin, mask_end)
left_shift = shift
right_shift = 32 - shift
left_mask = (all_ones << left_shift) & mask
right_mask = (all_ones >> right_shift) & mask
# We only simplify if the `simplify` argument is True, and there will be no `|` in the
# resulting expression. If there is an `|`, the expression is best left as bitwise math
simplify = simplify and not (left_mask and right_mask)
if isinstance(source, Literal):
upper_value = (source.value << left_shift) & mask
lower_value = (source.value >> right_shift) & mask
return Literal(upper_value | lower_value)
upper_bits: Optional[Expression]
if left_mask == 0:
upper_bits = None
else:
upper_bits = source
if left_shift != 0:
upper_bits = BinaryOp.int(
left=upper_bits, op="<<", right=Literal(left_shift)
)
if simplify:
upper_bits = fold_mul_chains(upper_bits)
if left_mask != (all_ones << left_shift) & all_ones:
upper_bits = BinaryOp.int(left=upper_bits, op="&", right=Literal(left_mask))
if simplify:
upper_bits = replace_bitand(upper_bits)
lower_bits: Optional[Expression]
if right_mask == 0:
lower_bits = None
else:
lower_bits = BinaryOp.uint(left=source, op=">>", right=Literal(right_shift))
if simplify:
lower_bits = replace_clz_shift(fold_divmod(lower_bits))
if right_mask != (all_ones >> right_shift) & all_ones:
lower_bits = BinaryOp.int(
left=lower_bits, op="&", right=Literal(right_mask)
)
if simplify:
lower_bits = replace_bitand(lower_bits)
if upper_bits is None and lower_bits is None:
return Literal(0)
elif upper_bits is None:
assert lower_bits is not None
return lower_bits
elif lower_bits is None:
return upper_bits
else:
return BinaryOp.int(left=upper_bits, op="|", right=lower_bits)
def handle_rlwimi(
base: Expression, source: Expression, shift: int, mask_begin: int, mask_end: int
) -> Expression:
# This instruction reads from `base`, replaces some bits with values from `source`, then
# writes the result back into the first register. This can be used to copy any contiguous
# bitfield from `source` into `base`, and is commonly used when manipulating flags, such
# as in `x |= 0x10` or `x &= ~0x10`.
# It's generally more readable to write the mask with `~` (instead of computing the inverse here)
mask_literal = Literal(rlwi_mask(mask_begin, mask_end))
mask = UnaryOp("~", mask_literal, type=Type.u32())
masked_base = BinaryOp.int(left=base, op="&", right=mask)
if source == Literal(0):
# If the source is 0, there are no bits inserted. (This may look like `x &= ~0x10`)
return masked_base
# Set `simplify=False` to keep the `inserted` expression as bitwise math instead of `*` or `/`
inserted = handle_rlwinm(source, shift, mask_begin, mask_end, simplify=False)
if inserted == mask_literal:
# If this instruction will set all the bits in the mask, we can OR the values
# together without masking the base. (`x |= 0xF0` instead of `x = (x & ~0xF0) | 0xF0`)
return BinaryOp.int(left=base, op="|", right=inserted)
return BinaryOp.int(left=masked_base, op="|", right=inserted)
def handle_loadx(args: InstrArgs, type: Type) -> Expression:
# "indexed loads" like `lwzx rD, rA, rB` read `(rA + rB)` into `rD`
size = type.get_size_bytes()
assert size is not None
ptr = BinaryOp.intptr(left=args.reg(1), op="+", right=args.reg(2))
expr = deref(ptr, args.regs, args.stack_info, size=size)
return as_type(expr, type, silent=True)
def strip_macros(arg: Argument) -> Argument:
"""Replace %lo(...) by 0, and assert that there are no %hi(...). We assume that
%hi's only ever occur in lui, where we expand them to an entire value, and not
just the upper part. This preserves semantics in most cases (though not when %hi's
are reused for different %lo's...)"""
if isinstance(arg, Macro):
if arg.macro_name in ["sda2", "sda21"]:
return arg.argument
if arg.macro_name == "hi":
raise DecompFailure("%hi macro outside of lui")
if arg.macro_name not in ["lo", "l"]:
raise DecompFailure(f"Unrecognized linker macro %{arg.macro_name}")
# This is sort of weird; for `symbol@l` we return 0 here and assume
# that this @l is always perfectly paired with one other @ha.
# However, with `literal@l`, we return the literal value, and assume it is
# paired with another `literal@ha`. This lets us reuse `literal@ha` values,
# but assumes that we never mix literals & symbols
if isinstance(arg.argument, AsmLiteral):
return AsmLiteral(arg.argument.value)
return AsmLiteral(0)
elif isinstance(arg, AsmAddressMode) and isinstance(arg.lhs, Macro):
if arg.lhs.macro_name in ["sda2", "sda21"]:
return arg.lhs.argument
if arg.lhs.macro_name not in ["lo", "l"]:
raise DecompFailure(
f"Bad linker macro in instruction argument {arg}, expected %lo"
)
return AsmAddressMode(lhs=AsmLiteral(0), rhs=arg.rhs)
else:
return arg
@dataclass
class AbiArgSlot:
offset: int
reg: Optional[Register]
type: Type
name: Optional[str] = None
comment: Optional[str] = None
@dataclass
class Abi:
arg_slots: List[AbiArgSlot]
possible_slots: List[AbiArgSlot]
def reg_always_set(node: Node, reg: Register, *, dom_set: bool) -> bool:
if node.immediate_dominator is None:
return False
seen = {node.immediate_dominator}
stack = node.parents[:]
while stack:
n = stack.pop()
if n == node.immediate_dominator and not dom_set:
return False
if n in seen:
continue
seen.add(n)
clobbered: Optional[bool] = None
for instr in n.block.instructions:
with current_instr(instr):
if reg in instr.outputs:
clobbered = False
elif reg in instr.clobbers:
clobbered = True
if clobbered == True:
return False
if clobbered is None:
stack.extend(n.parents)
return True
def pick_phi_assignment_nodes(
reg: Register, nodes: List[Node], expr: Expression
) -> List[Node]:
"""
As part of `assign_phis()`, we need to pick a set of nodes where we can emit a
`SetPhiStmt` that assigns the phi for `reg` to `expr`.
The final register state for `reg` for each node in `nodes` is `expr`,
so the best case would be finding a single dominating node for the assignment.
"""
# Find the set of nodes which dominate *all* of `nodes`, sorted by number
# of dominators. (This puts "earlier" nodes at the beginning of the list.)
dominators = sorted(
set.intersection(*(node.dominators for node in nodes)),
key=lambda n: len(n.dominators),
)
# Check the dominators for a node with the correct final state for `reg`
for node in dominators:
regs = get_block_info(node).final_register_states
raw = regs.get_raw(reg)
meta = regs.get_meta(reg)
if raw is None or meta is None or meta.force:
continue
if raw == expr:
return [node]
# We couldn't find anything, so fall back to the naive solution
# TODO: In some cases there may be a better solution (e.g. one that requires 2 nodes)
return nodes
def assign_phis(used_phis: List[PhiExpr], stack_info: StackInfo) -> None:
i = 0
# Iterate over used phis until there are no more remaining. New ones may
# appear during iteration, hence the while loop.
while i < len(used_phis):
phi = used_phis[i]
assert phi.num_usages > 0
assert len(phi.node.parents) >= 2
# Group parent nodes by the value of their phi register
equivalent_nodes: DefaultDict[Expression, List[Node]] = defaultdict(list)
for node in phi.node.parents:
expr = get_block_info(node).final_register_states[phi.reg]
expr.type.unify(phi.type)
equivalent_nodes[expr].append(node)
exprs = list(equivalent_nodes.keys())
first_uw = early_unwrap(exprs[0])
if all(early_unwrap(e) == first_uw for e in exprs[1:]):
# All the phis have the same value (e.g. because we recomputed an
# expression after a store, or restored a register after a function
# call). Just use that value instead of introducing a phi node.
# TODO: the unwrapping here is necessary, but also kinda sketchy:
# we may set as replacement_expr an expression that really shouldn't
# be repeated, e.g. a StructAccess. It would make sense to use less
# eager unwrapping, and/or to emit an EvalOnceExpr at this point
# (though it's too late for it to be able to participate in the
# prevent_later_uses machinery).
phi.replacement_expr = as_type(first_uw, phi.type, silent=True)
for _ in range(phi.num_usages):
first_uw.use()
else:
for expr, nodes in equivalent_nodes.items():
for node in pick_phi_assignment_nodes(phi.reg, nodes, expr):
block_info = get_block_info(node)
expr = block_info.final_register_states[phi.reg]
if isinstance(expr, PhiExpr):
# Explicitly mark how the expression is used if it's a phi,
# so we can propagate phi sets (to get rid of temporaries).
expr.use(from_phi=phi)
else:
expr.use()
typed_expr = as_type(expr, phi.type, silent=True)
block_info.to_write.append(SetPhiStmt(phi, typed_expr))
i += 1
name_counter: Dict[Register, int] = {}
for phi in used_phis:
if not phi.replacement_expr and phi.propagates_to() == phi:
counter = name_counter.get(phi.reg, 0) + 1
name_counter[phi.reg] = counter
output_reg_name = stack_info.function.reg_formatter.format(phi.reg)
prefix = f"phi_{output_reg_name}"
phi.name = f"{prefix}_{counter}" if counter > 1 else prefix
stack_info.phi_vars.append(phi)
def propagate_register_meta(nodes: List[Node], reg: Register) -> None:
"""Propagate RegMeta bits forwards/backwards."""
non_terminal: List[Node] = [n for n in nodes if not isinstance(n, TerminalNode)]
# Set `is_read` based on `read_inherited`.
for n in non_terminal:
if reg in get_block_info(n).final_register_states.read_inherited:
for p in n.parents:
par_meta = get_block_info(p).final_register_states.get_meta(reg)
if par_meta:
par_meta.is_read = True
# Propagate `is_read` backwards.
todo = non_terminal[:]
while todo:
n = todo.pop()
meta = get_block_info(n).final_register_states.get_meta(reg)
for p in n.parents:
par_meta = get_block_info(p).final_register_states.get_meta(reg)
if (par_meta and not par_meta.is_read) and (
meta and meta.inherited and meta.is_read
):
par_meta.is_read = True
todo.append(p)
# Set `uninteresting` and propagate it, `function_return`, and `in_pattern` forwards.
# Start by assuming inherited values are all set; they will get unset iteratively,
# but for cyclic dependency purposes we want to assume them set.
for n in non_terminal:
meta = get_block_info(n).final_register_states.get_meta(reg)
if meta:
if meta.inherited:
meta.uninteresting = True
meta.function_return = True
meta.in_pattern = True
else:
meta.uninteresting |= (
meta.is_read or meta.function_return or meta.in_pattern
)
todo = non_terminal[:]
while todo:
n = todo.pop()
if isinstance(n, TerminalNode):
continue
meta = get_block_info(n).final_register_states.get_meta(reg)
if not meta or not meta.inherited:
continue
all_uninteresting = True
all_function_return = True
all_in_pattern = True
for p in n.parents:
par_meta = get_block_info(p).final_register_states.get_meta(reg)
if par_meta:
all_uninteresting &= par_meta.uninteresting
all_function_return &= par_meta.function_return
all_in_pattern &= par_meta.in_pattern
if meta.uninteresting and not all_uninteresting and not meta.is_read:
meta.uninteresting = False
todo.extend(n.children())
if meta.function_return and not all_function_return:
meta.function_return = False
todo.extend(n.children())
if meta.in_pattern and not all_in_pattern:
meta.in_pattern = False
todo.extend(n.children())
def determine_return_register(
return_blocks: List[BlockInfo], fn_decl_provided: bool, arch: Arch
) -> Optional[Register]:
"""Determine which of the arch's base_return_regs (i.e. v0, f0) is the most
likely to contain the return value, or if the function is likely void."""
def priority(block_info: BlockInfo, reg: Register) -> int:
meta = block_info.final_register_states.get_meta(reg)
if not meta:
return 4
if meta.uninteresting:
return 2
if meta.in_pattern:
return 1
if meta.function_return:
return 0
return 3
if not return_blocks:
return None
best_reg: Optional[Register] = None
best_prio = -1
for reg in arch.base_return_regs:
prios = [priority(b, reg) for b in return_blocks]
max_prio = max(prios)
if max_prio == 4:
# Register is not always set, skip it
continue
if max_prio <= 2 and not fn_decl_provided:
# Register is always read after being written, or comes from a
# function call; seems unlikely to be an intentional return.
# Skip it, unless we have a known non-void return type.
continue
if max_prio > best_prio:
best_prio = max_prio
best_reg = reg
return best_reg
def translate_node_body(node: Node, regs: RegInfo, stack_info: StackInfo) -> BlockInfo:
"""
Given a node and current register contents, return a BlockInfo containing
the translated AST for that node.
"""
to_write: List[Union[Statement]] = []
local_var_writes: Dict[LocalVar, Tuple[Register, Expression]] = {}
subroutine_args: Dict[int, Expression] = {}
branch_condition: Optional[Condition] = None
switch_expr: Optional[Expression] = None
has_custom_return: bool = False
has_function_call: bool = False
in_pattern: bool = False
arch = stack_info.global_info.arch
def eval_once(
expr: Expression,
*,
emit_exactly_once: bool,
trivial: bool,
prefix: str = "",
reuse_var: Optional[Var] = None,
) -> EvalOnceExpr:
if emit_exactly_once:
# (otherwise this will be marked used once num_usages reaches 1)
expr.use()
elif "_fictive_" in prefix and isinstance(expr, EvalOnceExpr):
# Avoid creating additional EvalOnceExprs for fictive Registers
# so they're less likely to appear in the output
return expr
assert reuse_var or prefix
if prefix == "condition_bit":
prefix = "cond"
var = reuse_var or Var(stack_info, "temp_" + prefix)
expr = EvalOnceExpr(
wrapped_expr=expr,
var=var,
type=expr.type,
emit_exactly_once=emit_exactly_once,
trivial=trivial,
)
var.num_usages += 1
stmt = EvalOnceStmt(expr)
to_write.append(stmt)
stack_info.temp_vars.append(stmt)
return expr
def prevent_later_uses(expr_filter: Callable[[Expression], bool]) -> None:
"""Prevent later uses of registers whose contents match a callback filter."""
for r in regs.contents.keys():
data = regs.contents.get(r)
assert data is not None
expr = data.value
if not data.meta.force and expr_filter(expr):
# Mark the register as "if used, emit the expression's once
# var". We usually always have a once var at this point,
# but if we don't, create one.
if not isinstance(expr, EvalOnceExpr):
expr = eval_once(
expr,
emit_exactly_once=False,
trivial=False,
prefix=stack_info.function.reg_formatter.format(r),
)
# This write isn't changing the value of the register; it didn't need
# to be declared as part of the current instruction's inputs/outputs.
regs.unchecked_set_with_meta(r, expr, replace(data.meta, force=True))
def prevent_later_value_uses(sub_expr: Expression) -> None:
"""Prevent later uses of registers that recursively contain a given
subexpression."""
# Unused PassedInArg are fine; they can pass the uses_expr test simply based
# on having the same variable name. If we didn't filter them out here it could
# cause them to be incorrectly passed as function arguments -- the function
# call logic sees an opaque wrapper and doesn't realize that they are unused
# arguments that should not be passed on.
prevent_later_uses(
lambda e: uses_expr(e, lambda e2: e2 == sub_expr)
and not (isinstance(e, PassedInArg) and not e.copied)
)
def prevent_later_function_calls() -> None:
"""Prevent later uses of registers that recursively contain a function call."""
prevent_later_uses(lambda e: uses_expr(e, lambda e2: isinstance(e2, FuncCall)))
def prevent_later_reads() -> None:
"""Prevent later uses of registers that recursively contain a read."""
contains_read = lambda e: isinstance(e, (StructAccess, ArrayAccess))
prevent_later_uses(lambda e: uses_expr(e, contains_read))
def set_reg_maybe_return(reg: Register, expr: Expression) -> None:
regs.set_with_meta(reg, expr, RegMeta(in_pattern=in_pattern))
def set_reg(reg: Register, expr: Optional[Expression]) -> Optional[Expression]:
if expr is None:
if reg in regs:
del regs[reg]
return None
if isinstance(expr, LocalVar):
if (
isinstance(node, ReturnNode)
and stack_info.maybe_get_register_var(reg)
and stack_info.in_callee_save_reg_region(expr.value)
and reg in stack_info.callee_save_regs
):
# Elide saved register restores with --reg-vars (it doesn't
# matter in other cases).
return None
if expr in local_var_writes:
# Elide register restores (only for the same register for now,
# to be conversative).
orig_reg, orig_expr = local_var_writes[expr]
if orig_reg == reg:
expr = orig_expr
uw_expr = expr
if not isinstance(expr, Literal):
expr = eval_once(
expr,
emit_exactly_once=False,
trivial=is_trivial_expression(expr),
prefix=stack_info.function.reg_formatter.format(reg),
)
if reg == Register("zero"):
# Emit the expression as is. It's probably a volatile load.
expr.use()
to_write.append(ExprStmt(expr))
else:
dest = stack_info.maybe_get_register_var(reg)
if dest is not None:
stack_info.use_register_var(dest)
# Avoid emitting x = x, but still refresh EvalOnceExpr's etc.
if not (isinstance(uw_expr, RegisterVar) and uw_expr.reg == reg):
source = as_type(expr, dest.type, True)
source.use()
to_write.append(StoreStmt(source=source, dest=dest))
expr = dest
set_reg_maybe_return(reg, expr)
return expr
def clear_caller_save_regs() -> None:
for reg in arch.temp_regs:
if reg in regs:
del regs[reg]
def maybe_clear_local_var_writes(func_args: List[Expression]) -> None:
# Clear the `local_var_writes` dict if any of the `func_args` contain
# a reference to a stack var. (The called function may modify the stack,
# replacing the value we have in `local_var_writes`.)
for arg in func_args:
if uses_expr(
arg,
lambda expr: isinstance(expr, AddressOf)
and isinstance(expr.expr, LocalVar),
):
local_var_writes.clear()
return
def process_instr(instr: Instruction) -> None:
nonlocal branch_condition, switch_expr, has_function_call, in_pattern
in_pattern = instr.in_pattern
mnemonic = instr.mnemonic
arch_mnemonic = instr.arch_mnemonic(arch)
args = InstrArgs(instr.args, regs, stack_info)
expr: Expression
# Figure out what code to generate!
if mnemonic in arch.instrs_ignore:
pass
elif mnemonic in arch.instrs_store or mnemonic in arch.instrs_store_update:
# Store a value in a permanent place.
if mnemonic in arch.instrs_store:
to_store = arch.instrs_store[mnemonic](args)
else:
# PPC specific store-and-update instructions
# `stwu r3, 8(r4)` is equivalent to `$r3 = *($r4 + 8); $r4 += 8;`
to_store = arch.instrs_store_update[mnemonic](args)
# Update the register in the second argument
update = args.memory_ref(1)
if not isinstance(update, AddressMode):
raise DecompFailure(
f"Unhandled store-and-update arg in {instr}: {update!r}"
)
set_reg(
update.rhs,
add_imm(args.regs[update.rhs], Literal(update.offset), stack_info),
)
if to_store is None:
# Elided register preserval.
pass
elif isinstance(to_store.dest, SubroutineArg):
# About to call a subroutine with this argument. Skip arguments for the
# first four stack slots; they are also passed in registers.
if to_store.dest.value >= 0x10:
subroutine_args[to_store.dest.value] = to_store.source
else:
if isinstance(to_store.dest, LocalVar):
stack_info.add_local_var(to_store.dest)
raw_value = to_store.source
if isinstance(raw_value, Cast) and raw_value.reinterpret:
# When preserving values on the stack across function calls,
# ignore the type of the stack variable. The same stack slot
# might be used to preserve values of different types.
raw_value = raw_value.expr
local_var_writes[to_store.dest] = (args.reg_ref(0), raw_value)
# Emit a write. This includes four steps:
# - mark the expression as used (since writes are always emitted)
# - mark the dest used (if it's a struct access it needs to be
# evaluated, though ideally we would not mark the top-level expression
# used; it may cause early emissions that shouldn't happen)
# - mark other usages of the dest as "emit before this point if used".
# - emit the actual write.
#
# Note that the prevent_later_value_uses step happens after use(), since
# the stored expression is allowed to reference its destination var,
# but before the write is written, since prevent_later_value_uses might
# emit writes of its own that should go before this write. In practice
# that probably never occurs -- all relevant register contents should be
# EvalOnceExpr's that can be emitted at their point of creation, but
# I'm not 100% certain that that's always the case and will remain so.
to_store.source.use()
to_store.dest.use()
prevent_later_value_uses(to_store.dest)
prevent_later_function_calls()
to_write.append(to_store)
elif mnemonic in arch.instrs_source_first:
# Just 'mtc1'. It's reversed, so we have to specially handle it.
set_reg(args.reg_ref(1), arch.instrs_source_first[mnemonic](args))
elif mnemonic in arch.instrs_branches:
assert branch_condition is None
branch_condition = arch.instrs_branches[mnemonic](args)
elif mnemonic in arch.instrs_float_branches:
assert branch_condition is None
cond_bit = regs[Register("condition_bit")]
if not isinstance(cond_bit, BinaryOp):
cond_bit = ExprCondition(cond_bit, type=cond_bit.type)
if arch_mnemonic == "mips:bc1t":
branch_condition = cond_bit
elif arch_mnemonic == "mips:bc1f":
branch_condition = cond_bit.negated()
elif mnemonic in arch.instrs_jumps:
if arch_mnemonic == "ppc:bctr":
# Switch jump
assert isinstance(node, SwitchNode)
switch_expr = args.regs[Register("ctr")]
elif arch_mnemonic == "mips:jr":
# MIPS:
if args.reg_ref(0) == arch.return_address_reg:
# Return from the function.
assert isinstance(node, ReturnNode)
else:
# Switch jump.
assert isinstance(node, SwitchNode)
switch_expr = args.reg(0)
elif arch_mnemonic == "ppc:blr":
assert isinstance(node, ReturnNode)
else:
assert False, f"Unhandled jump mnemonic {arch_mnemonic}"
elif mnemonic in arch.instrs_fn_call:
if arch_mnemonic in ["mips:jal", "ppc:bl"]:
fn_target = args.imm(0)
if not (
(
isinstance(fn_target, AddressOf)
and isinstance(fn_target.expr, GlobalSymbol)
)
or isinstance(fn_target, Literal)
):
raise DecompFailure(
f"Target of function call must be a symbol, not {fn_target}"
)
elif arch_mnemonic == "ppc:blrl":
fn_target = args.regs[Register("lr")]
elif arch_mnemonic == "ppc:bctrl":
fn_target = args.regs[Register("ctr")]
elif arch_mnemonic == "mips:jalr":
fn_target = args.reg(1)
else:
assert False, f"Unhandled fn call mnemonic {arch_mnemonic}"
fn_target = as_function_ptr(fn_target)
fn_sig = fn_target.type.get_function_pointer_signature()
assert fn_sig is not None, "known function pointers must have a signature"
likely_regs: Dict[Register, bool] = {}
for reg, data in regs.contents.items():
# We use a much stricter filter for PPC than MIPS, because the same
# registers can be used arguments & return values.
# The ABI can also mix & match the rN & fN registers, which makes the
# "require" heuristic less powerful.
#
# - `meta.inherited` will only be False for registers set in *this* basic block
# - `meta.function_return` will only be accurate for registers set within this
# basic block because we have not called `propagate_register_meta` yet.
# Within this block, it will be True for registers that were return values.
if arch.arch == Target.ArchEnum.PPC and (
data.meta.inherited or data.meta.function_return
):
likely_regs[reg] = False
elif data.meta.in_pattern:
# Like `meta.function_return` mentioned above, `meta.in_pattern` will only be
# accurate for registers set within this basic block.
likely_regs[reg] = False
elif isinstance(data.value, PassedInArg) and not data.value.copied:
likely_regs[reg] = False
else:
likely_regs[reg] = True
abi = arch.function_abi(fn_sig, likely_regs, for_call=True)
func_args: List[Expression] = []
for slot in abi.arg_slots:
if slot.reg:
expr = regs[slot.reg]
elif slot.offset in subroutine_args:
expr = subroutine_args.pop(slot.offset)
else:
expr = ErrorExpr(
f"Unable to find stack arg {slot.offset:#x} in block"
)
func_args.append(
CommentExpr.wrap(
as_type(expr, slot.type, True), prefix=slot.comment
)
)
for slot in abi.possible_slots:
assert slot.reg is not None
func_args.append(regs[slot.reg])
# Add the arguments after a3.
# TODO: limit this based on abi.arg_slots. If the function type is known
# and not variadic, this list should be empty.
for _, arg in sorted(subroutine_args.items()):
if fn_sig.params_known and not fn_sig.is_variadic:
func_args.append(CommentExpr.wrap(arg, prefix="extra?"))
else:
func_args.append(arg)
if not fn_sig.params_known:
while len(func_args) > len(fn_sig.params):
fn_sig.params.append(FunctionParam())
# When the function signature isn't provided, the we only assume that each
# parameter is "simple" (<=4 bytes, no return struct, etc.). This may not
# match the actual function signature, but it's the best we can do.
# Without that assumption, the logic from `function_abi` would be needed here.
for i, (arg_expr, param) in enumerate(zip(func_args, fn_sig.params)):
func_args[i] = as_type(arg_expr, param.type.decay(), True)
# Reset subroutine_args, for the next potential function call.
subroutine_args.clear()
call: Expression = FuncCall(
fn_target, func_args, fn_sig.return_type.weaken_void_ptr()
)
call = eval_once(call, emit_exactly_once=True, trivial=False, prefix="ret")
# Clear out caller-save registers, for clarity and to ensure that
# argument regs don't get passed into the next function.
clear_caller_save_regs()
# Clear out local var write tracking if any argument contains a stack
# reference. That dict is used to track register saves/restores, which
# are unreliable if we call a function with a stack reference.
maybe_clear_local_var_writes(func_args)
# Prevent reads and function calls from moving across this call.
# This isn't really right, because this call might be moved later,
# and then this prevention should also be... but it's the best we
# can do with the current code architecture.
prevent_later_function_calls()
prevent_later_reads()
return_reg_vals = arch.function_return(call)
for out in instr.outputs:
if not isinstance(out, Register):
continue
val = return_reg_vals[out]
if not isinstance(val, SecondF64Half):
val = eval_once(
val,
emit_exactly_once=False,
trivial=False,
prefix=stack_info.function.reg_formatter.format(out),
)
regs.set_with_meta(out, val, RegMeta(function_return=True))
has_function_call = True
elif mnemonic in arch.instrs_float_comp:
expr = arch.instrs_float_comp[mnemonic](args)
regs[Register("condition_bit")] = expr
elif mnemonic in arch.instrs_hi_lo:
hi, lo = arch.instrs_hi_lo[mnemonic](args)
set_reg(Register("hi"), hi)
set_reg(Register("lo"), lo)
elif mnemonic in arch.instrs_implicit_destination:
reg, expr_fn = arch.instrs_implicit_destination[mnemonic]
set_reg(reg, expr_fn(args))
elif mnemonic in arch.instrs_ppc_compare:
if instr.args[0] != Register("cr0"):
raise DecompFailure(
f"Instruction {instr} not supported (first arg is not $cr0)"
)
set_reg(Register("cr0_eq"), arch.instrs_ppc_compare[mnemonic](args, "=="))
set_reg(Register("cr0_gt"), arch.instrs_ppc_compare[mnemonic](args, ">"))
set_reg(Register("cr0_lt"), arch.instrs_ppc_compare[mnemonic](args, "<"))
set_reg(Register("cr0_so"), Literal(0))
elif mnemonic in arch.instrs_no_dest:
stmt = arch.instrs_no_dest[mnemonic](args)
to_write.append(stmt)
elif mnemonic.rstrip(".") in arch.instrs_destination_first:
target = args.reg_ref(0)
val = arch.instrs_destination_first[mnemonic.rstrip(".")](args)
# TODO: IDO tends to keep variables within single registers. Thus,
# if source = target, maybe we could overwrite that variable instead
# of creating a new one?
target_val = set_reg(target, val)
mn_parts = arch_mnemonic.split(".")
if arch_mnemonic.startswith("ppc:") and arch_mnemonic.endswith("."):
# PPC instructions suffixed with . set condition bits (CR0) based on the result value
if target_val is None:
target_val = val
set_reg(
Register("cr0_eq"),
BinaryOp.icmp(target_val, "==", Literal(0, type=target_val.type)),
)
# Use manual casts for cr0_gt/cr0_lt so that the type of target_val is not modified
# until the resulting bit is .use()'d.
target_s32 = Cast(
target_val, reinterpret=True, silent=True, type=Type.s32()
)
set_reg(
Register("cr0_gt"),
BinaryOp(target_s32, ">", Literal(0), type=Type.s32()),
)
set_reg(
Register("cr0_lt"),
BinaryOp(target_s32, "<", Literal(0), type=Type.s32()),
)
set_reg(
Register("cr0_so"),
fn_op("MIPS2C_OVERFLOW", [target_val], type=Type.s32()),
)
elif (
len(mn_parts) >= 2
and mn_parts[0].startswith("mips:")
and mn_parts[1] == "d"
) or arch_mnemonic == "mips:ldc1":
set_reg(target.other_f64_reg(), SecondF64Half())
elif mnemonic in arch.instrs_load_update:
target = args.reg_ref(0)
val = arch.instrs_load_update[mnemonic](args)
set_reg(target, val)
if arch_mnemonic in ["ppc:lwzux", "ppc:lhzux", "ppc:lbzux"]:
# In `rD, rA, rB`, update `rA = rA + rB`
update_reg = args.reg_ref(1)
offset = args.reg(2)
else:
# In `rD, rA(N)`, update `rA = rA + N`
update = args.memory_ref(1)
if not isinstance(update, AddressMode):
raise DecompFailure(
f"Unhandled store-and-update arg in {instr}: {update!r}"
)
update_reg = update.rhs
offset = Literal(update.offset)
if update_reg == target:
raise DecompFailure(
f"Invalid instruction, rA and rD must be different in {instr}"
)
set_reg(update_reg, add_imm(args.regs[update_reg], offset, stack_info))
else:
expr = ErrorExpr(f"unknown instruction: {instr}")
if arch_mnemonic.startswith("ppc:") and arch_mnemonic.endswith("."):
# Unimplemented PPC instructions that modify CR0
set_reg(Register("cr0_eq"), expr)
set_reg(Register("cr0_gt"), expr)
set_reg(Register("cr0_lt"), expr)
set_reg(Register("cr0_so"), expr)
if args.count() >= 1 and isinstance(args.raw_arg(0), Register):
reg = args.reg_ref(0)
expr = eval_once(
expr,
emit_exactly_once=True,
trivial=False,
prefix=stack_info.function.reg_formatter.format(reg),
)
if reg != Register("zero"):
set_reg_maybe_return(reg, expr)
else:
to_write.append(ExprStmt(expr))
for instr in node.block.instructions:
with regs.current_instr(instr):
process_instr(instr)
if branch_condition is not None:
branch_condition.use()
switch_control: Optional[SwitchControl] = None
if switch_expr is not None:
switch_control = SwitchControl.from_expr(switch_expr)
switch_control.control_expr.use()
return BlockInfo(
to_write=to_write,
return_value=None,
switch_control=switch_control,
branch_condition=branch_condition,
final_register_states=regs,
has_function_call=has_function_call,
)
def translate_graph_from_block(
node: Node,
regs: RegInfo,
stack_info: StackInfo,
used_phis: List[PhiExpr],
return_blocks: List[BlockInfo],
options: Options,
) -> None:
"""
Given a FlowGraph node and a dictionary of register contents, give that node
its appropriate BlockInfo (which contains the AST of its code).
"""
if options.debug:
print(f"\nNode in question: {node}")
# Translate the given node and discover final register states.
try:
block_info = translate_node_body(node, regs, stack_info)
if options.debug:
print(block_info)
except Exception as e: # TODO: handle issues better
if options.stop_on_error:
raise
instr: Optional[Instruction] = None
if isinstance(e, InstrProcessingFailure) and isinstance(e.__cause__, Exception):
instr = e.instr
e = e.__cause__
if isinstance(e, DecompFailure):
emsg = str(e)
print(emsg)
else:
tb = e.__traceback__
traceback.print_exception(None, e, tb)
emsg = str(e) or traceback.format_tb(tb)[-1]
emsg = emsg.strip().split("\n")[-1].strip()
error_stmts: List[Statement] = [CommentStmt(f"Error: {emsg}")]
if instr is not None:
print(
f"Error occurred while processing instruction: {instr}", file=sys.stderr
)
error_stmts.append(CommentStmt(f"At instruction: {instr}"))
print(file=sys.stderr)
block_info = BlockInfo(
to_write=error_stmts,
return_value=None,
switch_control=None,
branch_condition=ErrorExpr(),
final_register_states=regs,
has_function_call=False,
)
node.block.add_block_info(block_info)
if isinstance(node, ReturnNode):
return_blocks.append(block_info)
# Translate everything dominated by this node, now that we know our own
# final register state. This will eventually reach every node.
for child in node.immediately_dominates:
if isinstance(child, TerminalNode):
continue
new_regs = RegInfo(stack_info=stack_info)
for reg, data in regs.contents.items():
new_regs.set_with_meta(
reg, data.value, RegMeta(inherited=True, force=data.meta.force)
)
phi_regs = (
r for r in locs_clobbered_until_dominator(child) if isinstance(r, Register)
)
for reg in phi_regs:
if reg_always_set(child, reg, dom_set=(reg in regs)):
expr: Optional[Expression] = stack_info.maybe_get_register_var(reg)
if expr is None:
expr = PhiExpr(
reg=reg, node=child, used_phis=used_phis, type=Type.any_reg()
)
new_regs.set_with_meta(reg, expr, RegMeta(inherited=True))
elif reg in new_regs:
del new_regs[reg]
translate_graph_from_block(
child, new_regs, stack_info, used_phis, return_blocks, options
)
def resolve_types_late(stack_info: StackInfo) -> None:
"""
After translating a function, perform a final type-resolution pass.
"""
# Final check over stack var types. Because of delayed type unification, some
# locations should now be marked as "weak".
for location in stack_info.weak_stack_var_types.keys():
stack_info.get_stack_var(location, store=False)
# Use dereferences to determine pointer types
struct_type_map = stack_info.get_struct_type_map()
for var, offset_type_map in struct_type_map.items():
if len(offset_type_map) == 1 and 0 in offset_type_map:
# var was probably a plain pointer, not a struct
# Try to unify it with the appropriate pointer type,
# to fill in the type if it does not already have one
type = offset_type_map[0]
var.type.unify(Type.ptr(type))
@dataclass
class FunctionInfo:
stack_info: StackInfo
flow_graph: FlowGraph
return_type: Type
symbol: GlobalSymbol
@dataclass
class GlobalInfo:
asm_data: AsmData
arch: Arch
target: Target
local_functions: Set[str]
typemap: TypeMap
typepool: TypePool
global_symbol_map: Dict[str, GlobalSymbol] = field(default_factory=dict)
def asm_data_value(self, sym_name: str) -> Optional[AsmDataEntry]:
return self.asm_data.values.get(sym_name)
def address_of_gsym(self, sym_name: str) -> AddressOf:
if sym_name in self.global_symbol_map:
sym = self.global_symbol_map[sym_name]
else:
demangled_symbol: Optional[CxxSymbol] = None
demangled_str: Optional[str] = None
if self.target.language == Target.LanguageEnum.CXX:
try:
demangled_symbol = demangle_codewarrior_parse(sym_name)
except ValueError:
pass
else:
demangled_str = str(demangled_symbol)
sym = self.global_symbol_map[sym_name] = GlobalSymbol(
symbol_name=sym_name,
type=Type.any(),
asm_data_entry=self.asm_data_value(sym_name),
demangled_str=demangled_str,
)
# If the symbol is a C++ vtable, try to build a custom type for it by parsing it
if (
self.target.language == Target.LanguageEnum.CXX
and sym_name.startswith("__vt__")
and sym.asm_data_entry is not None
):
sym.type.unify(self.vtable_type(sym_name, sym.asm_data_entry))
fn = self.typemap.functions.get(sym_name)
ctype: Optional[CType]
if fn is not None:
ctype = fn.type
else:
ctype = self.typemap.var_types.get(sym_name)
if ctype is not None:
sym.symbol_in_context = True
sym.initializer_in_typemap = (
sym_name in self.typemap.vars_with_initializers
)
sym.type.unify(Type.ctype(ctype, self.typemap, self.typepool))
if sym_name not in self.typepool.unknown_decls:
sym.type_provided = True
elif sym_name in self.local_functions:
sym.type.unify(Type.function())
# Do this after unifying the type in the typemap, so that it has lower precedence
if demangled_symbol is not None:
sym.type.unify(
Type.demangled_symbol(self.typemap, self.typepool, demangled_symbol)
)
return AddressOf(sym, type=sym.type.reference())
def vtable_type(self, sym_name: str, asm_data_entry: AsmDataEntry) -> Type:
"""
Parse MWCC vtable data to create a custom struct to represent it.
This format is not well documented, but is briefly explored in this series of posts:
https://web.archive.org/web/20220413174849/http://hacksoflife.blogspot.com/2007/02/c-objects-part-2-single-inheritance.html
"""
size = asm_data_entry.size_range_bytes()[1]
struct = StructDeclaration.unknown(
self.typepool, size=size, align=4, tag_name=sym_name
)
offset = 0
for entry in asm_data_entry.data:
if isinstance(entry, bytes):
# MWCC vtables start with a pointer to a typeid struct (or NULL) and an offset
if len(entry) % 4 != 0:
raise DecompFailure(
f"Unable to parse misaligned vtable data in {sym_name}"
)
for i in range(len(entry) // 4):
field_name = f"{struct.new_field_prefix}{offset:X}"
struct.try_add_field(
Type.reg32(likely_float=False), offset, field_name, size=4
)
offset += 4
else:
entry_name = entry
try:
demangled_field_sym = demangle_codewarrior_parse(entry)
if demangled_field_sym.name.qualified_name is not None:
entry_name = str(demangled_field_sym.name.qualified_name[-1])
except ValueError:
pass
field = struct.try_add_field(
self.address_of_gsym(entry).type,
offset,
name=entry_name,
size=4,
)
assert field is not None
field.known = True
offset += 4
return Type.struct(struct)
def is_function_known_void(self, sym_name: str) -> bool:
"""Return True if the function exists in the context, and has no return value"""
fn = self.typemap.functions.get(sym_name)
if fn is None:
return False
return fn.ret_type is None
def initializer_for_symbol(
self, sym: GlobalSymbol, fmt: Formatter
) -> Optional[str]:
assert sym.asm_data_entry is not None
data = sym.asm_data_entry.data[:]
def read_uint(n: int) -> Optional[int]:
"""Read the next `n` bytes from `data` as an (long) integer"""
assert 0 < n <= 8
if not data or not isinstance(data[0], bytes):
return None
if len(data[0]) < n:
return None
bs = data[0][:n]
data[0] = data[0][n:]
if not data[0]:
del data[0]
value = 0
for b in bs:
value = (value << 8) | b
return value
def read_pointer() -> Optional[Expression]:
"""Read the next label from `data`"""
if not data or not isinstance(data[0], str):
return None
label = data[0]
data.pop(0)
return self.address_of_gsym(label)
def for_type(type: Type) -> Optional[str]:
"""Return the initializer for a single element of type `type`"""
if type.is_struct() or type.is_array():
struct_fields = type.get_initializer_fields()
if not struct_fields:
return None
members = []
for field in struct_fields:
if isinstance(field, int):
# Check that all padding bytes are 0
for i in range(field):
padding = read_uint(1)
if padding != 0:
return None
else:
m = for_type(field)
if m is None:
return None
members.append(m)
return fmt.format_array(members)
if type.is_reg():
size = type.get_size_bytes()
if not size:
return None
if size == 4:
ptr = read_pointer()
if ptr is not None:
return as_type(ptr, type, silent=True).format(fmt)
value = read_uint(size)
if value is not None:
enum_name = type.get_enum_name(value)
if enum_name is not None:
return enum_name
expr = as_type(Literal(value), type, True)
return elide_casts_for_store(expr).format(fmt)
# Type kinds K_FN and K_VOID do not have initializers
return None
return for_type(sym.type)
def find_forward_declares_needed(self, functions: List[FunctionInfo]) -> Set[str]:
funcs_seen = set()
forward_declares_needed = self.asm_data.mentioned_labels
for func in functions:
funcs_seen.add(func.stack_info.function.name)
for instr in func.stack_info.function.body:
if not isinstance(instr, Instruction):
continue
for arg in instr.args:
if isinstance(arg, AsmGlobalSymbol):
func_name = arg.symbol_name
elif isinstance(arg, Macro) and isinstance(
arg.argument, AsmGlobalSymbol
):
func_name = arg.argument.symbol_name
else:
continue
if func_name in self.local_functions:
if func_name not in funcs_seen:
forward_declares_needed.add(func_name)
return forward_declares_needed
def global_decls(
self,
fmt: Formatter,
decls: Options.GlobalDeclsEnum,
functions: List[FunctionInfo],
) -> str:
# Format labels from symbol_type_map into global declarations.
# As the initializers are formatted, this may cause more symbols
# to be added to the global_symbol_map.
forward_declares_needed = self.find_forward_declares_needed(functions)
lines = []
processed_names: Set[str] = set()
while True:
names: AbstractSet[str] = self.global_symbol_map.keys()
if decls == Options.GlobalDeclsEnum.ALL:
names |= self.asm_data.values.keys()
names -= processed_names
if not names:
break
for name in sorted(names):
processed_names.add(name)
sym = self.address_of_gsym(name).expr
assert isinstance(sym, GlobalSymbol)
data_entry = sym.asm_data_entry
# Is the label defined in this unit (in the active AsmData file(s))
is_in_file = data_entry is not None or name in self.local_functions
# Is the label externally visible (mentioned in the context file)
is_global = sym.symbol_in_context
# Is the label a symbol in .rodata?
is_const = data_entry is not None and data_entry.is_readonly
if data_entry and data_entry.is_jtbl:
# Skip jump tables
continue
if is_in_file and is_global and sym.type.is_function():
# Skip externally-declared functions that are defined here
continue
if self.local_functions == {name}:
# Skip the function being decompiled if just a single one
continue
if not is_in_file and sym.type_provided:
# Skip externally-declared symbols that are defined in other files
continue
# TODO: Use original MIPSFile ordering for variables
sort_order = (
not sym.type.is_function(),
is_global,
is_in_file,
is_const,
name,
)
qualifier = ""
value: Optional[str] = None
comments = []
# Determine type qualifier: static, extern, or neither
if is_in_file and is_global:
qualifier = ""
elif is_in_file:
qualifier = "static"
else:
qualifier = "extern"
if sym.type.is_function():
comments.append(qualifier)
qualifier = ""
# Try to guess if the symbol is an array (and if it is, its dimension) if
# we have a data entry for it, and the symbol is either not in the typemap
# or was a variable-length array there ("VLA", e.g. `int []`)
# (Otherwise, if the dim is provided by the typemap, we trust it.)
element_type, array_dim = sym.type.get_array()
is_vla = element_type is not None and (
array_dim is None or array_dim <= 0
)
if data_entry and (not sym.type_provided or is_vla):
# The size of the data entry is uncertain, because of padding
# between sections. Generally `(max_data_size - data_size) < 16`.
min_data_size, max_data_size = data_entry.size_range_bytes()
# The size of the element type (not the size of the array type)
if element_type is None:
element_type = sym.type
# If we don't know the type, we can't guess the array_dim
type_size = element_type.get_size_bytes()
if type_size:
potential_dim, extra_bytes = sym.potential_array_dim(type_size)
if potential_dim == 0 and extra_bytes > 0:
# The type is too big for our data. (not an array)
comments.append(
f"type too large by {fmt.format_int(type_size - extra_bytes)}"
)
elif potential_dim > 1 or is_vla:
# NB: In general, replacing the types of Expressions can be sketchy.
# However, the GlobalSymbol here came from address_of_gsym(), which
# always returns a reference to the element_type.
array_dim = potential_dim
sym.type = Type.array(element_type, array_dim)
if potential_dim != 0 and extra_bytes > 0:
comments.append(
f"extra bytes: {fmt.format_int(extra_bytes)}"
)
# Try to convert the data from .data/.rodata into an initializer
if data_entry and not data_entry.is_bss:
value = self.initializer_for_symbol(sym, fmt)
if value is None:
# This warning helps distinguish .bss symbols from .data/.rodata,
# IDO only puts symbols in .bss if they don't have any initializer
comments.append("unable to generate initializer")
if is_const:
comments.append("const")
# Float & string constants are almost always inlined and can be omitted
if sym.is_string_constant():
continue
if array_dim is None and sym.type.is_likely_float():
continue
# In "none" mode, do not emit any decls
if decls == Options.GlobalDeclsEnum.NONE:
continue
# In modes except "all", skip the decl if the context file already had an initializer
if decls != Options.GlobalDeclsEnum.ALL and sym.initializer_in_typemap:
continue
# In modes except "all", skip vtable decls when compiling C++
if (
decls != Options.GlobalDeclsEnum.ALL
and self.target.language == Target.LanguageEnum.CXX
and name.startswith("__vt__")
):
continue
if (
sym.type.is_function()
and decls != Options.GlobalDeclsEnum.ALL
and name in self.local_functions
and name not in forward_declares_needed
):
continue
qualifier = f"{qualifier} " if qualifier else ""
value = f" = {value}" if value else ""
lines.append(
(
sort_order,
fmt.with_comments(
f"{qualifier}{sym.type.to_decl(name, fmt)}{value};",
comments,
)
+ "\n",
)
)
lines.sort()
return "".join(line for _, line in lines)
def narrow_func_call_outputs(
function: Function,
global_info: GlobalInfo,
) -> None:
"""
Modify the `outputs` list of function call Instructions using the context file.
For now, this only handles known-void functions, but in the future it could
be extended to select a specific register subset based on type.
"""
for instr in function.body:
if (
isinstance(instr, Instruction)
and isinstance(instr.function_target, AsmGlobalSymbol)
and global_info.is_function_known_void(instr.function_target.symbol_name)
):
instr.outputs.clear()
def translate_to_ast(
function: Function,
flow_graph: FlowGraph,
options: Options,
global_info: GlobalInfo,
) -> FunctionInfo:
"""
Given a function, produce a FlowGraph that both contains control-flow
information and has AST transformations for each block of code and
branch condition.
"""
# Initialize info about the function.
stack_info = get_stack_info(function, global_info, flow_graph)
start_regs: RegInfo = RegInfo(stack_info=stack_info)
arch = global_info.arch
start_regs[arch.stack_pointer_reg] = GlobalSymbol("sp", type=Type.ptr())
for reg in arch.saved_regs:
start_regs[reg] = stack_info.saved_reg_symbol(reg.register_name)
fn_sym = global_info.address_of_gsym(function.name).expr
assert isinstance(fn_sym, GlobalSymbol)
fn_type = fn_sym.type
fn_type.unify(Type.function())
fn_sig = Type.ptr(fn_type).get_function_pointer_signature()
assert fn_sig is not None, "fn_type is known to be a function"
return_type = fn_sig.return_type
stack_info.is_variadic = fn_sig.is_variadic
def make_arg(offset: int, type: Type) -> PassedInArg:
assert offset % 4 == 0
return PassedInArg(offset, copied=False, stack_info=stack_info, type=type)
abi = arch.function_abi(
fn_sig,
likely_regs={reg: True for reg in arch.argument_regs},
for_call=False,
)
for slot in abi.arg_slots:
stack_info.add_known_param(slot.offset, slot.name, slot.type)
if slot.reg is not None:
start_regs.set_with_meta(
slot.reg, make_arg(slot.offset, slot.type), RegMeta(uninteresting=True)
)
for slot in abi.possible_slots:
if slot.reg is not None:
start_regs.set_with_meta(
slot.reg, make_arg(slot.offset, slot.type), RegMeta(uninteresting=True)
)
if options.reg_vars == ["saved"]:
reg_vars = arch.saved_regs
elif options.reg_vars == ["most"]:
reg_vars = arch.saved_regs + arch.simple_temp_regs
elif options.reg_vars == ["all"]:
reg_vars = arch.saved_regs + arch.simple_temp_regs + arch.argument_regs
else:
reg_vars = [
stack_info.function.reg_formatter.parse(x, arch) for x in options.reg_vars
]
for reg in reg_vars:
reg_name = stack_info.function.reg_formatter.format(reg)
stack_info.add_register_var(reg, reg_name)
if options.debug:
print(stack_info)
print("\nNow, we attempt to translate:")
used_phis: List[PhiExpr] = []
return_blocks: List[BlockInfo] = []
translate_graph_from_block(
flow_graph.entry_node(),
start_regs,
stack_info,
used_phis,
return_blocks,
options,
)
for reg in arch.base_return_regs:
propagate_register_meta(flow_graph.nodes, reg)
return_reg: Optional[Register] = None
if not options.void and not return_type.is_void():
return_reg = determine_return_register(
return_blocks, fn_sym.type_provided, arch
)
if return_reg is not None:
for b in return_blocks:
if return_reg in b.final_register_states:
ret_val = b.final_register_states[return_reg]
ret_val = as_type(ret_val, return_type, True)
ret_val.use()
b.return_value = ret_val
else:
return_type.unify(Type.void())
if not fn_sig.params_known:
while len(fn_sig.params) < len(stack_info.arguments):
fn_sig.params.append(FunctionParam())
for param, arg in zip(fn_sig.params, stack_info.arguments):
param.type.unify(arg.type)
if not param.name:
param.name = arg.format(Formatter())
assign_phis(used_phis, stack_info)
resolve_types_late(stack_info)
if options.pdb_translate:
import pdb
v: Dict[str, object] = {}
fmt = Formatter()
for local in stack_info.local_vars:
var_name = local.format(fmt)
v[var_name] = local
for temp in stack_info.temp_vars:
if temp.need_decl():
var_name = temp.expr.var.format(fmt)
v[var_name] = temp.expr
for phi in stack_info.phi_vars:
assert phi.name is not None
v[phi.name] = phi
pdb.set_trace()
return FunctionInfo(stack_info, flow_graph, return_type, fn_sym)
| 38.14032 | 131 | 0.598064 | import abc
from collections import defaultdict
from contextlib import contextmanager
from dataclasses import dataclass, field, replace
import math
import struct
import sys
import traceback
import typing
from typing import (
AbstractSet,
Callable,
Collection,
DefaultDict,
Dict,
Iterator,
List,
Mapping,
Optional,
Set,
Tuple,
Union,
)
from .c_types import CType, TypeMap
from .demangle_codewarrior import parse as demangle_codewarrior_parse, CxxSymbol
from .error import DecompFailure, static_assert_unreachable
from .flow_graph import (
ArchFlowGraph,
FlowGraph,
Function,
Node,
ReturnNode,
SwitchNode,
TerminalNode,
locs_clobbered_until_dominator,
)
from .ir_pattern import IrPattern, simplify_ir_patterns
from .options import CodingStyle, Formatter, Options, Target
from .parse_file import AsmData, AsmDataEntry
from .parse_instruction import (
ArchAsm,
Argument,
AsmAddressMode,
AsmGlobalSymbol,
AsmLiteral,
BinOp,
Instruction,
InstrProcessingFailure,
Macro,
Register,
StackLocation,
current_instr,
)
from .types import (
AccessPath,
FunctionParam,
FunctionSignature,
StructDeclaration,
Type,
TypePool,
)
InstrSet = Collection[str]
InstrMap = Mapping[str, Callable[["InstrArgs"], "Expression"]]
StmtInstrMap = Mapping[str, Callable[["InstrArgs"], "Statement"]]
CmpInstrMap = Mapping[str, Callable[["InstrArgs"], "Condition"]]
StoreInstrMap = Mapping[str, Callable[["InstrArgs"], Optional["StoreStmt"]]]
MaybeInstrMap = Mapping[str, Callable[["InstrArgs"], Optional["Expression"]]]
PairInstrMap = Mapping[str, Callable[["InstrArgs"], Tuple["Expression", "Expression"]]]
ImplicitInstrMap = Mapping[str, Tuple[Register, Callable[["InstrArgs"], "Expression"]]]
PpcCmpInstrMap = Mapping[str, Callable[["InstrArgs", str], "Expression"]]
class Arch(ArchFlowGraph):
instrs_ignore: InstrSet = set()
instrs_store: StoreInstrMap = {}
instrs_store_update: StoreInstrMap = {}
instrs_load_update: InstrMap = {}
instrs_branches: CmpInstrMap = {}
instrs_float_branches: InstrSet = set()
instrs_float_comp: CmpInstrMap = {}
instrs_ppc_compare: PpcCmpInstrMap = {}
instrs_jumps: InstrSet = set()
instrs_fn_call: InstrSet = set()
instrs_no_dest: StmtInstrMap = {}
instrs_hi_lo: PairInstrMap = {}
instrs_source_first: InstrMap = {}
instrs_destination_first: InstrMap = {}
instrs_implicit_destination: ImplicitInstrMap = {}
@abc.abstractmethod
def function_abi(
self,
fn_sig: FunctionSignature,
likely_regs: Dict[Register, bool],
*,
for_call: bool,
) -> "Abi":
...
@abc.abstractmethod
def function_return(self, expr: "Expression") -> Dict[Register, "Expression"]:
...
ir_patterns: List[IrPattern] = []
def simplify_ir(self, flow_graph: FlowGraph) -> None:
simplify_ir_patterns(self, flow_graph, self.ir_patterns)
ASSOCIATIVE_OPS: Set[str] = {"+", "&&", "||", "&", "|", "^", "*"}
COMPOUND_ASSIGNMENT_OPS: Set[str] = {"+", "-", "*", "/", "%", "&", "|", "^", "<<", ">>"}
PSEUDO_FUNCTION_OPS: Set[str] = {"MULT_HI", "MULTU_HI", "DMULT_HI", "DMULTU_HI", "CLZ"}
def as_type(expr: "Expression", type: Type, silent: bool) -> "Expression":
type = type.weaken_void_ptr()
ptr_target_type = type.get_pointer_target()
if expr.type.unify(type):
if silent or isinstance(expr, Literal):
return expr
elif ptr_target_type is not None:
ptr_target_type_size = ptr_target_type.get_size_bytes()
field_path, field_type, _ = expr.type.get_deref_field(
0, target_size=ptr_target_type_size
)
if field_path is not None and field_type.unify(ptr_target_type):
expr = AddressOf(
StructAccess(
struct_var=expr,
offset=0,
target_size=ptr_target_type_size,
field_path=field_path,
stack_info=None,
type=field_type,
),
type=type,
)
if silent:
return expr
return Cast(expr=expr, reinterpret=True, silent=False, type=type)
def as_f32(expr: "Expression") -> "Expression":
return as_type(expr, Type.f32(), True)
def as_f64(expr: "Expression") -> "Expression":
return as_type(expr, Type.f64(), True)
def as_sintish(expr: "Expression", *, silent: bool = False) -> "Expression":
return as_type(expr, Type.sintish(), silent)
def as_uintish(expr: "Expression") -> "Expression":
return as_type(expr, Type.uintish(), False)
def as_u32(expr: "Expression") -> "Expression":
return as_type(expr, Type.u32(), False)
def as_s64(expr: "Expression", *, silent: bool = False) -> "Expression":
return as_type(expr, Type.s64(), silent)
def as_u64(expr: "Expression", *, silent: bool = False) -> "Expression":
return as_type(expr, Type.u64(), silent)
def as_intish(expr: "Expression") -> "Expression":
return as_type(expr, Type.intish(), True)
def as_int64(expr: "Expression") -> "Expression":
return as_type(expr, Type.int64(), True)
def as_intptr(expr: "Expression") -> "Expression":
return as_type(expr, Type.intptr(), True)
def as_ptr(expr: "Expression") -> "Expression":
return as_type(expr, Type.ptr(), True)
def as_function_ptr(expr: "Expression") -> "Expression":
return as_type(expr, Type.ptr(Type.function()), True)
@dataclass
class StackInfo:
function: Function
global_info: "GlobalInfo"
flow_graph: FlowGraph
allocated_stack_size: int = 0
is_leaf: bool = True
is_variadic: bool = False
uses_framepointer: bool = False
subroutine_arg_top: int = 0
callee_save_regs: Set[Register] = field(default_factory=set)
callee_save_reg_region: Tuple[int, int] = (0, 0)
unique_type_map: Dict[Tuple[str, object], "Type"] = field(default_factory=dict)
local_vars: List["LocalVar"] = field(default_factory=list)
temp_vars: List["EvalOnceStmt"] = field(default_factory=list)
phi_vars: List["PhiExpr"] = field(default_factory=list)
reg_vars: Dict[Register, "RegisterVar"] = field(default_factory=dict)
used_reg_vars: Set[Register] = field(default_factory=set)
arguments: List["PassedInArg"] = field(default_factory=list)
temp_name_counter: Dict[str, int] = field(default_factory=dict)
nonzero_accesses: Set["Expression"] = field(default_factory=set)
param_names: Dict[int, str] = field(default_factory=dict)
stack_pointer_type: Optional[Type] = None
replace_first_arg: Optional[Tuple[str, Type]] = None
weak_stack_var_types: Dict[int, Type] = field(default_factory=dict)
weak_stack_var_locations: Set[int] = field(default_factory=set)
def temp_var(self, prefix: str) -> str:
counter = self.temp_name_counter.get(prefix, 0) + 1
self.temp_name_counter[prefix] = counter
return prefix + (f"_{counter}" if counter > 1 else "")
def in_subroutine_arg_region(self, location: int) -> bool:
if self.global_info.arch.arch == Target.ArchEnum.PPC:
return False
if self.is_leaf:
return False
assert self.subroutine_arg_top is not None
return location < self.subroutine_arg_top
def in_callee_save_reg_region(self, location: int) -> bool:
lower_bound, upper_bound = self.callee_save_reg_region
if lower_bound <= location < upper_bound:
return True
if (
self.global_info.arch.arch == Target.ArchEnum.PPC
and location == self.allocated_stack_size + 4
):
return True
return False
def location_above_stack(self, location: int) -> bool:
return location >= self.allocated_stack_size
def add_known_param(self, offset: int, name: Optional[str], type: Type) -> None:
if offset == 0 and type.is_pointer() and self.replace_first_arg is None:
namespace = self.function.name.partition("_")[0]
base_struct_type = type.get_pointer_target()
self_struct = self.global_info.typepool.get_struct_by_tag_name(
namespace, self.global_info.typemap
)
if (
self_struct is not None
and base_struct_type is not None
and base_struct_type.is_struct()
):
self_struct_type = Type.struct(self_struct)
field_path, field_type, _ = self_struct_type.get_field(
offset=0, target_size=base_struct_type.get_size_bytes()
)
if (
field_path is not None
and field_type.unify(base_struct_type)
and not self_struct_type.unify(base_struct_type)
):
self.replace_first_arg = (name or "_self", type)
name = "this" if name == "thisx" else "self"
type = Type.ptr(Type.struct(self_struct))
if name:
self.param_names[offset] = name
_, arg = self.get_argument(offset)
self.add_argument(arg)
arg.type.unify(type)
def get_param_name(self, offset: int) -> Optional[str]:
return self.param_names.get(offset)
def add_local_var(self, var: "LocalVar") -> None:
if any(v.value == var.value for v in self.local_vars):
return
self.local_vars.append(var)
self.local_vars.sort(key=lambda v: v.value)
def add_argument(self, arg: "PassedInArg") -> None:
if any(a.value == arg.value for a in self.arguments):
return
self.arguments.append(arg)
self.arguments.sort(key=lambda a: a.value)
def get_argument(self, location: int) -> Tuple["Expression", "PassedInArg"]:
real_location = location & -4
arg = PassedInArg(
real_location,
copied=True,
stack_info=self,
type=self.unique_type_for("arg", real_location, Type.any_reg()),
)
if real_location == location - 3:
return as_type(arg, Type.int_of_size(8), True), arg
if real_location == location - 2:
return as_type(arg, Type.int_of_size(16), True), arg
return arg, arg
def record_struct_access(self, ptr: "Expression", location: int) -> None:
if location:
self.nonzero_accesses.add(unwrap_deep(ptr))
def has_nonzero_access(self, ptr: "Expression") -> bool:
return unwrap_deep(ptr) in self.nonzero_accesses
def unique_type_for(self, category: str, key: object, default: Type) -> "Type":
key = (category, key)
if key not in self.unique_type_map:
self.unique_type_map[key] = default
return self.unique_type_map[key]
def saved_reg_symbol(self, reg_name: str) -> "GlobalSymbol":
sym_name = "saved_reg_" + reg_name
type = self.unique_type_for("saved_reg", sym_name, Type.any_reg())
return GlobalSymbol(symbol_name=sym_name, type=type)
def should_save(self, expr: "Expression", offset: Optional[int]) -> bool:
expr = early_unwrap(expr)
if isinstance(expr, GlobalSymbol) and (
expr.symbol_name.startswith("saved_reg_") or expr.symbol_name == "sp"
):
return True
if (
isinstance(expr, PassedInArg)
and not expr.copied
and (offset is None or offset == self.allocated_stack_size + expr.value)
):
return True
return False
def get_stack_var(self, location: int, *, store: bool) -> "Expression":
if self.in_callee_save_reg_region(location):
return LocalVar(location, type=Type.any_reg(), path=None)
elif self.location_above_stack(location):
ret, arg = self.get_argument(location - self.allocated_stack_size)
if not store:
self.add_argument(arg)
return ret
elif self.in_subroutine_arg_region(location):
return SubroutineArg(location, type=Type.any_reg())
else:
# Local variable
assert self.stack_pointer_type is not None
field_path, field_type, _ = self.stack_pointer_type.get_deref_field(
location, target_size=None
)
# Some variables on the stack are compiler-managed, and aren't declared
previous_stored_type = self.weak_stack_var_types.get(location)
if previous_stored_type is not None:
if not previous_stored_type.unify(field_type):
# This marker is only used to annotate the output
self.weak_stack_var_locations.add(location)
if store:
# If there's already been a store to `location`, then return a fresh type
field_type = Type.any_field()
else:
field_type = previous_stored_type
if store:
self.weak_stack_var_types[location] = field_type
return LocalVar(location, type=field_type, path=field_path)
def maybe_get_register_var(self, reg: Register) -> Optional["RegisterVar"]:
return self.reg_vars.get(reg)
def add_register_var(self, reg: Register, name: str) -> None:
type = Type.floatish() if reg.is_float() else Type.intptr()
self.reg_vars[reg] = RegisterVar(reg=reg, type=type, name=name)
def use_register_var(self, var: "RegisterVar") -> None:
self.used_reg_vars.add(var.reg)
def is_stack_reg(self, reg: Register) -> bool:
if reg == self.global_info.arch.stack_pointer_reg:
return True
if reg == self.global_info.arch.frame_pointer_reg:
return self.uses_framepointer
return False
def get_struct_type_map(self) -> Dict["Expression", Dict[int, Type]]:
struct_type_map: Dict[Expression, Dict[int, Type]] = {}
for (category, key), type in self.unique_type_map.items():
if category != "struct":
continue
var, offset = typing.cast(Tuple[Expression, int], key)
if var not in struct_type_map:
struct_type_map[var] = {}
struct_type_map[var][offset] = type
return struct_type_map
def __str__(self) -> str:
return "\n".join(
[
f"Stack info for function {self.function.name}:",
f"Allocated stack size: {self.allocated_stack_size}",
f"Leaf? {self.is_leaf}",
f"Bounds of callee-saved vars region: {self.callee_save_reg_region}",
f"Callee save registers: {self.callee_save_regs}",
]
)
def get_stack_info(
function: Function,
global_info: "GlobalInfo",
flow_graph: FlowGraph,
) -> StackInfo:
arch = global_info.arch
info = StackInfo(function, global_info, flow_graph)
#
# IDO puts local variables *above* the saved registers on the stack, but
# GCC puts local variables *below* the saved registers.
# To support both, we explicitly determine both the upper & lower bounds of the
# saved registers. Then, we estimate the boundary of the subroutine arguments
# by finding the lowest stack offset that is loaded from or computed. (This
# assumes that the compiler will never reuse a section of stack for *both*
# a local variable *and* a subroutine argument.) Anything within the stack frame,
# but outside of these two regions, is considered a local variable.
callee_saved_offsets: List[int] = []
# Track simple literal values stored into registers: MIPS compilers need a temp
# reg to move the stack pointer more than 0x7FFF bytes.
temp_reg_values: Dict[Register, int] = {}
for inst in flow_graph.entry_node().block.instructions:
arch_mnemonic = inst.arch_mnemonic(arch)
if inst.mnemonic in arch.instrs_fn_call:
break
elif arch_mnemonic == "mips:addiu" and inst.args[0] == arch.stack_pointer_reg:
# Moving the stack pointer on MIPS
assert isinstance(inst.args[2], AsmLiteral)
info.allocated_stack_size = abs(inst.args[2].signed_value())
elif (
arch_mnemonic == "mips:subu"
and inst.args[0] == arch.stack_pointer_reg
and inst.args[1] == arch.stack_pointer_reg
and inst.args[2] in temp_reg_values
):
# Moving the stack pointer more than 0x7FFF on MIPS
# TODO: This instruction needs to be ignored later in translation, in the
# same way that `addiu $sp, $sp, N` is ignored in handle_addi_real
assert isinstance(inst.args[2], Register)
info.allocated_stack_size = temp_reg_values[inst.args[2]]
elif arch_mnemonic == "ppc:stwu" and inst.args[0] == arch.stack_pointer_reg:
# Moving the stack pointer on PPC
assert isinstance(inst.args[1], AsmAddressMode)
assert isinstance(inst.args[1].lhs, AsmLiteral)
info.allocated_stack_size = abs(inst.args[1].lhs.signed_value())
elif (
arch_mnemonic == "mips:move"
and inst.args[0] == arch.frame_pointer_reg
and inst.args[1] == arch.stack_pointer_reg
):
# "move fp, sp" very likely means the code is compiled with frame
# pointers enabled; thus fp should be treated the same as sp.
info.uses_framepointer = True
elif (
arch_mnemonic
in [
"mips:sw",
"mips:swc1",
"mips:sdc1",
"ppc:stw",
"ppc:stmw",
"ppc:stfd",
"ppc:psq_st",
]
and isinstance(inst.args[0], Register)
and inst.args[0] in arch.saved_regs
and isinstance(inst.args[1], AsmAddressMode)
and inst.args[1].rhs == arch.stack_pointer_reg
and (
inst.args[0] not in info.callee_save_regs
or arch_mnemonic == "ppc:psq_st"
)
):
# Initial saving of callee-save register onto the stack.
if inst.args[0] in (arch.return_address_reg, Register("r0")):
# Saving the return address on the stack.
info.is_leaf = False
# The registers & their stack accesses must be matched up in ArchAsm.parse
for reg, mem in zip(inst.inputs, inst.outputs):
if isinstance(reg, Register) and isinstance(mem, StackLocation):
assert mem.symbolic_offset is None
stack_offset = mem.offset
if arch_mnemonic != "ppc:psq_st":
# psq_st instructions store the same register as stfd, just
# as packed singles instead. Prioritize the stfd.
info.callee_save_regs.add(reg)
callee_saved_offsets.append(stack_offset)
elif arch_mnemonic == "ppc:mflr" and inst.args[0] == Register("r0"):
info.is_leaf = False
elif arch_mnemonic == "mips:li" and inst.args[0] in arch.temp_regs:
assert isinstance(inst.args[0], Register)
assert isinstance(inst.args[1], AsmLiteral)
temp_reg_values[inst.args[0]] = inst.args[1].value
elif (
arch_mnemonic == "mips:ori"
and inst.args[0] == inst.args[1]
and inst.args[0] in temp_reg_values
):
assert isinstance(inst.args[0], Register)
assert isinstance(inst.args[2], AsmLiteral)
temp_reg_values[inst.args[0]] |= inst.args[2].value
if not info.is_leaf:
# Iterate over the whole function, not just the first basic block,
# to estimate the boundary for the subroutine argument region
info.subroutine_arg_top = info.allocated_stack_size
for node in flow_graph.nodes:
for inst in node.block.instructions:
arch_mnemonic = inst.arch_mnemonic(arch)
if (
arch_mnemonic in ["mips:lw", "mips:lwc1", "mips:ldc1", "ppc:lwz"]
and isinstance(inst.args[1], AsmAddressMode)
and inst.args[1].rhs == arch.stack_pointer_reg
and inst.args[1].lhs_as_literal() >= 16
):
info.subroutine_arg_top = min(
info.subroutine_arg_top, inst.args[1].lhs_as_literal()
)
elif (
arch_mnemonic == "mips:addiu"
and inst.args[0] != arch.stack_pointer_reg
and inst.args[1] == arch.stack_pointer_reg
and isinstance(inst.args[2], AsmLiteral)
and inst.args[2].value < info.allocated_stack_size
):
info.subroutine_arg_top = min(
info.subroutine_arg_top, inst.args[2].value
)
# Compute the bounds of the callee-saved register region, including padding
if callee_saved_offsets:
callee_saved_offsets.sort()
bottom = callee_saved_offsets[0]
# Both IDO & GCC save registers in two subregions:
# (a) One for double-sized registers
# (b) One for word-sized registers, padded to a multiple of 8 bytes
# IDO has (a) lower than (b); GCC has (b) lower than (a)
# Check that there are no gaps in this region, other than a single
# 4-byte word between subregions.
top = bottom
internal_padding_added = False
for offset in callee_saved_offsets:
if offset != top:
if not internal_padding_added and offset == top + 4:
internal_padding_added = True
else:
raise DecompFailure(
f"Gap in callee-saved word stack region. "
f"Saved: {callee_saved_offsets}, "
f"gap at: {offset} != {top}."
)
top = offset + 4
info.callee_save_reg_region = (bottom, top)
# Subroutine arguments must be at the very bottom of the stack, so they
# must come after the callee-saved region
info.subroutine_arg_top = min(info.subroutine_arg_top, bottom)
# Use a struct to represent the stack layout. If the struct is provided in the context,
# its fields will be used for variable types & names.
stack_struct_name = f"_mips2c_stack_{function.name}"
stack_struct = global_info.typepool.get_struct_by_tag_name(
stack_struct_name, global_info.typemap
)
if stack_struct is not None:
if stack_struct.size != info.allocated_stack_size:
raise DecompFailure(
f"Function {function.name} has a provided stack type {stack_struct_name} "
f"with size {stack_struct.size}, but the detected stack size was "
f"{info.allocated_stack_size}."
)
else:
stack_struct = StructDeclaration.unknown(
global_info.typepool,
size=info.allocated_stack_size,
tag_name=stack_struct_name,
)
# Mark the struct as a stack struct so we never try to use a reference to the struct itself
stack_struct.is_stack = True
stack_struct.new_field_prefix = "sp"
# This acts as the type of the $sp register
info.stack_pointer_type = Type.ptr(Type.struct(stack_struct))
return info
def format_hex(val: int) -> str:
return format(val, "x").upper()
def escape_byte(b: int) -> bytes:
table = {
b"\0": b"\\0",
b"\b": b"\\b",
b"\f": b"\\f",
b"\n": b"\\n",
b"\r": b"\\r",
b"\t": b"\\t",
b"\v": b"\\v",
b"\\": b"\\\\",
b'"': b'\\"',
}
bs = bytes([b])
if bs in table:
return table[bs]
if b < 0x20 or b in (0xFF, 0x7F):
return f"\\x{b:02x}".encode("ascii")
return bs
@dataclass(eq=False)
class Var:
stack_info: StackInfo = field(repr=False)
prefix: str
num_usages: int = 0
name: Optional[str] = None
def format(self, fmt: Formatter) -> str:
if self.name is None:
self.name = self.stack_info.temp_var(self.prefix)
return self.name
def __str__(self) -> str:
return "<temp>"
class Expression(abc.ABC):
type: Type
@abc.abstractmethod
def dependencies(self) -> List["Expression"]:
...
def use(self) -> None:
for expr in self.dependencies():
expr.use()
@abc.abstractmethod
def format(self, fmt: Formatter) -> str:
...
def __str__(self) -> str:
fmt = Formatter(debug=True)
return '"' + self.format(fmt) + '"'
class Condition(Expression):
@abc.abstractmethod
def negated(self) -> "Condition":
...
class Statement(abc.ABC):
@abc.abstractmethod
def should_write(self) -> bool:
...
@abc.abstractmethod
def format(self, fmt: Formatter) -> str:
...
def __str__(self) -> str:
fmt = Formatter(debug=True)
return '"' + self.format(fmt) + '"'
@dataclass(frozen=True, eq=False)
class ErrorExpr(Condition):
desc: Optional[str] = None
type: Type = field(default_factory=Type.any_reg)
def dependencies(self) -> List[Expression]:
return []
def negated(self) -> "Condition":
return self
def format(self, fmt: Formatter) -> str:
if self.desc is not None:
return f"MIPS2C_ERROR({self.desc})"
return "MIPS2C_ERROR()"
@dataclass(frozen=True)
class CommentExpr(Expression):
expr: Expression
type: Type = field(compare=False)
prefix: Optional[str] = None
suffix: Optional[str] = None
def dependencies(self) -> List[Expression]:
return [self.expr]
def format(self, fmt: Formatter) -> str:
expr_str = self.expr.format(fmt)
if fmt.coding_style.comment_style == CodingStyle.CommentStyle.NONE:
return expr_str
prefix_str = f"/* {self.prefix} */ " if self.prefix is not None else ""
suffix_str = f" /* {self.suffix} */" if self.suffix is not None else ""
return f"{prefix_str}{expr_str}{suffix_str}"
@staticmethod
def wrap(
expr: Expression, prefix: Optional[str] = None, suffix: Optional[str] = None
) -> Expression:
if prefix is None and suffix is None:
return expr
return CommentExpr(expr=expr, type=expr.type, prefix=prefix, suffix=suffix)
@dataclass(frozen=True, eq=False)
class SecondF64Half(Expression):
type: Type = field(default_factory=Type.any_reg)
def dependencies(self) -> List[Expression]:
return []
def format(self, fmt: Formatter) -> str:
return "(second half of f64)"
@dataclass(frozen=True, eq=False)
class CarryBit(Expression):
type: Type = field(default_factory=Type.intish)
def dependencies(self) -> List[Expression]:
return []
def format(self, fmt: Formatter) -> str:
return "MIPS2C_CARRY"
@staticmethod
def add_to(expr: Expression) -> "BinaryOp":
return fold_divmod(BinaryOp.intptr(expr, "+", CarryBit()))
@staticmethod
def sub_from(expr: Expression) -> "BinaryOp":
return BinaryOp.intptr(expr, "-", UnaryOp("!", CarryBit(), type=Type.intish()))
@dataclass(frozen=True, eq=False)
class BinaryOp(Condition):
left: Expression
op: str
right: Expression
type: Type
@staticmethod
def int(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_intish(left), op=op, right=as_intish(right), type=Type.intish()
)
@staticmethod
def int64(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_int64(left), op=op, right=as_int64(right), type=Type.int64()
)
@staticmethod
def intptr(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_intptr(left), op=op, right=as_intptr(right), type=Type.intptr()
)
@staticmethod
def icmp(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_intptr(left), op=op, right=as_intptr(right), type=Type.bool()
)
@staticmethod
def scmp(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_sintish(left, silent=True),
op=op,
right=as_sintish(right, silent=True),
type=Type.bool(),
)
@staticmethod
def sintptr_cmp(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_type(left, Type.sintptr(), False),
op=op,
right=as_type(right, Type.sintptr(), False),
type=Type.bool(),
)
@staticmethod
def ucmp(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_uintish(left), op=op, right=as_uintish(right), type=Type.bool()
)
@staticmethod
def uintptr_cmp(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_type(left, Type.uintptr(), False),
op=op,
right=as_type(right, Type.uintptr(), False),
type=Type.bool(),
)
@staticmethod
def fcmp(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_f32(left),
op=op,
right=as_f32(right),
type=Type.bool(),
)
@staticmethod
def dcmp(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_f64(left),
op=op,
right=as_f64(right),
type=Type.bool(),
)
@staticmethod
def sint(
left: Expression, op: str, right: Expression, *, silent: bool = False
) -> "BinaryOp":
return BinaryOp(
left=as_sintish(left, silent=silent),
op=op,
right=as_sintish(right, silent=silent),
type=Type.s32(),
)
@staticmethod
def uint(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_uintish(left), op=op, right=as_uintish(right), type=Type.u32()
)
@staticmethod
def s64(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(left=as_s64(left), op=op, right=as_s64(right), type=Type.s64())
@staticmethod
def u64(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(left=as_u64(left), op=op, right=as_u64(right), type=Type.u64())
@staticmethod
def f32(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_f32(left),
op=op,
right=as_f32(right),
type=Type.f32(),
)
@staticmethod
def f64(left: Expression, op: str, right: Expression) -> "BinaryOp":
return BinaryOp(
left=as_f64(left),
op=op,
right=as_f64(right),
type=Type.f64(),
)
def is_comparison(self) -> bool:
return self.op in ["==", "!=", ">", "<", ">=", "<="]
def is_floating(self) -> bool:
return self.left.type.is_float() and self.right.type.is_float()
def negated(self) -> "Condition":
if (
self.op in ["&&", "||"]
and isinstance(self.left, Condition)
and isinstance(self.right, Condition)
):
# DeMorgan's Laws
return BinaryOp(
left=self.left.negated(),
op={"&&": "||", "||": "&&"}[self.op],
right=self.right.negated(),
type=Type.bool(),
)
if not self.is_comparison() or (
self.is_floating() and self.op in ["<", ">", "<=", ">="]
):
return UnaryOp("!", self, type=Type.bool())
return BinaryOp(
left=self.left,
op={"==": "!=", "!=": "==", ">": "<=", "<": ">=", ">=": "<", "<=": ">"}[
self.op
],
right=self.right,
type=Type.bool(),
)
def dependencies(self) -> List[Expression]:
return [self.left, self.right]
def format(self, fmt: Formatter) -> str:
left_expr = late_unwrap(self.left)
right_expr = late_unwrap(self.right)
if (
self.is_comparison()
and isinstance(left_expr, Literal)
and not isinstance(right_expr, Literal)
):
return BinaryOp(
left=right_expr,
op=self.op.translate(str.maketrans("<>", "><")),
right=left_expr,
type=self.type,
).format(fmt)
if (
not self.is_floating()
and isinstance(right_expr, Literal)
and right_expr.value < 0
):
if self.op == "+":
neg = Literal(value=-right_expr.value, type=right_expr.type)
sub = BinaryOp(op="-", left=left_expr, right=neg, type=self.type)
return sub.format(fmt)
if self.op in ("&", "|"):
neg = Literal(value=~right_expr.value, type=right_expr.type)
right = UnaryOp("~", neg, type=Type.any_reg())
expr = BinaryOp(op=self.op, left=left_expr, right=right, type=self.type)
return expr.format(fmt)
lhs = left_expr.format(fmt)
if (
isinstance(left_expr, BinaryOp)
and left_expr.op == self.op
and self.op in ASSOCIATIVE_OPS
):
lhs = lhs[1:-1]
if self.op in ("/", "%") and isinstance(right_expr, Literal):
rhs = right_expr.format(fmt, force_dec=True)
else:
rhs = right_expr.format(fmt)
if self.op in PSEUDO_FUNCTION_OPS:
return f"{self.op}({lhs}, {rhs})"
return f"({lhs} {self.op} {rhs})"
@dataclass(frozen=True, eq=False)
class TernaryOp(Expression):
cond: Condition
left: Expression
right: Expression
type: Type
def dependencies(self) -> List[Expression]:
return [self.cond, self.left, self.right]
def format(self, fmt: Formatter) -> str:
cond_str = simplify_condition(self.cond).format(fmt)
left_str = self.left.format(fmt)
right_str = self.right.format(fmt)
return f"({cond_str} ? {left_str} : {right_str})"
@dataclass(frozen=True, eq=False)
class UnaryOp(Condition):
op: str
expr: Expression
type: Type
def dependencies(self) -> List[Expression]:
return [self.expr]
@staticmethod
def sint(op: str, expr: Expression) -> "UnaryOp":
expr = as_sintish(expr, silent=True)
return UnaryOp(
op=op,
expr=expr,
type=expr.type,
)
def negated(self) -> "Condition":
if self.op == "!" and isinstance(self.expr, (UnaryOp, BinaryOp)):
return self.expr
return UnaryOp("!", self, type=Type.bool())
def format(self, fmt: Formatter) -> str:
# These aren't real operators (or functions); format them as a fn call
if self.op in PSEUDO_FUNCTION_OPS:
return f"{self.op}({self.expr.format(fmt)})"
return f"{self.op}{self.expr.format(fmt)}"
@dataclass(frozen=True, eq=False)
class ExprCondition(Condition):
expr: Expression
type: Type
is_negated: bool = False
def dependencies(self) -> List[Expression]:
return [self.expr]
def negated(self) -> "Condition":
return ExprCondition(self.expr, self.type, not self.is_negated)
def format(self, fmt: Formatter) -> str:
neg = "!" if self.is_negated else ""
return f"{neg}{self.expr.format(fmt)}"
@dataclass(frozen=True, eq=False)
class CommaConditionExpr(Condition):
statements: List["Statement"]
condition: "Condition"
type: Type = Type.bool()
def dependencies(self) -> List[Expression]:
assert False, "CommaConditionExpr should not be used within translate.py"
return []
def negated(self) -> "Condition":
return CommaConditionExpr(self.statements, self.condition.negated())
def format(self, fmt: Formatter) -> str:
comma_joined = ", ".join(
stmt.format(fmt).rstrip(";") for stmt in self.statements
)
return f"({comma_joined}, {self.condition.format(fmt)})"
@dataclass(frozen=True, eq=False)
class Cast(Expression):
expr: Expression
type: Type
reinterpret: bool = False
silent: bool = True
def dependencies(self) -> List[Expression]:
return [self.expr]
def use(self) -> None:
self.expr.type.unify(self.type)
super().use()
def needed_for_store(self) -> bool:
if not self.reinterpret:
return True
if not self.expr.type.unify(self.type):
return True
return False
def is_trivial(self) -> bool:
return (
self.reinterpret
and self.expr.type.is_float() == self.type.is_float()
and is_trivial_expression(self.expr)
)
def format(self, fmt: Formatter) -> str:
if self.reinterpret and self.expr.type.is_float() != self.type.is_float():
if fmt.valid_syntax:
return (
f"MIPS2C_BITWISE({self.type.format(fmt)}, {self.expr.format(fmt)})"
)
return f"(bitwise {self.type.format(fmt)}) {self.expr.format(fmt)}"
if self.reinterpret and (
self.silent
or (is_type_obvious(self.expr) and self.expr.type.unify(self.type))
):
return self.expr.format(fmt)
if fmt.skip_casts:
return self.expr.format(fmt)
# Function casts require special logic because function calls have
# higher precedence than casts
fn_sig = self.type.get_function_pointer_signature()
if fn_sig:
prototype_sig = self.expr.type.get_function_pointer_signature()
if not prototype_sig or not prototype_sig.unify_with_args(fn_sig):
# A function pointer cast is required if the inner expr is not
# a function pointer, or has incompatible argument types
return f"(({self.type.format(fmt)}) {self.expr.format(fmt)})"
if not prototype_sig.return_type.unify(fn_sig.return_type):
# Only cast the return value of the call
return f"({fn_sig.return_type.format(fmt)}) {self.expr.format(fmt)}"
# No cast needed
return self.expr.format(fmt)
return f"({self.type.format(fmt)}) {self.expr.format(fmt)}"
@dataclass(frozen=True, eq=False)
class FuncCall(Expression):
function: Expression
args: List[Expression]
type: Type
def dependencies(self) -> List[Expression]:
return self.args + [self.function]
def format(self, fmt: Formatter) -> str:
# TODO: The function type may have a different number of params than it had
# when the FuncCall was created. Should we warn that there may be the wrong
# number of arguments at this callsite?
args = ", ".join(format_expr(arg, fmt) for arg in self.args)
return f"{self.function.format(fmt)}({args})"
@dataclass(frozen=True, eq=True)
class LocalVar(Expression):
value: int
type: Type = field(compare=False)
path: Optional[AccessPath] = field(compare=False)
def dependencies(self) -> List[Expression]:
return []
def format(self, fmt: Formatter) -> str:
fallback_name = f"unksp{format_hex(self.value)}"
if self.path is None:
return fallback_name
name = StructAccess.access_path_to_field_name(self.path, fmt)
if name.startswith("->"):
return name[2:]
return fallback_name
def toplevel_decl(self, fmt: Formatter) -> Optional[str]:
# If len(self.path) > 2, then this local is an inner field of another
# local, so it doesn't need to be declared.
if (
self.path is None
or len(self.path) != 2
or not isinstance(self.path[1], str)
):
return None
return self.type.to_decl(self.path[1], fmt)
@dataclass(frozen=True, eq=False)
class RegisterVar(Expression):
reg: Register
name: str
type: Type
def dependencies(self) -> List[Expression]:
return []
def format(self, fmt: Formatter) -> str:
return self.name
@dataclass(frozen=True, eq=True)
class PassedInArg(Expression):
value: int
copied: bool = field(compare=False)
stack_info: StackInfo = field(compare=False, repr=False)
type: Type = field(compare=False)
def dependencies(self) -> List[Expression]:
return []
def format(self, fmt: Formatter) -> str:
assert self.value % 4 == 0
name = self.stack_info.get_param_name(self.value)
return name or f"arg{format_hex(self.value // 4)}"
@dataclass(frozen=True, eq=True)
class SubroutineArg(Expression):
value: int
type: Type = field(compare=False)
def dependencies(self) -> List[Expression]:
return []
def format(self, fmt: Formatter) -> str:
return f"subroutine_arg{format_hex(self.value // 4)}"
@dataclass(eq=True, unsafe_hash=True)
class StructAccess(Expression):
# that may invalidate it.
struct_var: Expression
offset: int
target_size: Optional[int]
field_path: Optional[AccessPath] = field(compare=False)
stack_info: Optional[StackInfo] = field(compare=False, repr=False)
type: Type = field(compare=False)
checked_late_field_path: bool = field(default=False, compare=False)
def __post_init__(self) -> None:
# stack_info is used to resolve field_path late
assert (
self.stack_info is not None or self.field_path is not None
), "Must provide at least one of (stack_info, field_path)"
self.assert_valid_field_path(self.field_path)
@staticmethod
def assert_valid_field_path(path: Optional[AccessPath]) -> None:
assert path is None or (
path and isinstance(path[0], int)
), "The first element of the field path, if present, must be an int"
@classmethod
def access_path_to_field_name(cls, path: AccessPath, fmt: Formatter) -> str:
cls.assert_valid_field_path(path)
output = ""
# Replace an initial "[0]." with "->"
if len(path) >= 2 and path[0] == 0 and isinstance(path[1], str):
output += f"->{path[1]}"
path = path[2:]
for p in path:
if isinstance(p, str):
output += f".{p}"
elif isinstance(p, int):
output += f"[{fmt.format_int(p)}]"
else:
static_assert_unreachable(p)
return output
def dependencies(self) -> List[Expression]:
return [self.struct_var]
def make_reference(self) -> Optional["StructAccess"]:
field_path = self.late_field_path()
if field_path and len(field_path) >= 2 and field_path[-1] == 0:
return replace(self, field_path=field_path[:-1])
return None
def late_field_path(self) -> Optional[AccessPath]:
# If we didn't have a type at the time when the struct access was
if self.field_path is None and not self.checked_late_field_path:
var = late_unwrap(self.struct_var)
var.format(Formatter())
field_path, field_type, _ = var.type.get_deref_field(
self.offset, target_size=self.target_size
)
if field_path is not None:
self.assert_valid_field_path(field_path)
self.field_path = field_path
self.type.unify(field_type)
self.checked_late_field_path = True
return self.field_path
def late_has_known_type(self) -> bool:
if self.late_field_path() is not None:
return True
assert (
self.stack_info is not None
), "StructAccess must have stack_info if field_path isn't set"
if self.offset == 0:
var = late_unwrap(self.struct_var)
if (
not self.stack_info.has_nonzero_access(var)
and isinstance(var, AddressOf)
and isinstance(var.expr, GlobalSymbol)
and var.expr.type_provided
):
return True
return False
def format(self, fmt: Formatter) -> str:
var = late_unwrap(self.struct_var)
has_nonzero_access = False
if self.stack_info is not None:
has_nonzero_access = self.stack_info.has_nonzero_access(var)
field_path = self.late_field_path()
if field_path is not None and field_path != [0]:
has_nonzero_access = True
elif fmt.valid_syntax and (self.offset != 0 or has_nonzero_access):
offset_str = fmt.format_int(self.offset)
return f"MIPS2C_FIELD({var.format(fmt)}, {Type.ptr(self.type).format(fmt)}, {offset_str})"
else:
prefix = "unk" + ("_" if fmt.coding_style.unknown_underscore else "")
field_path = [0, prefix + format_hex(self.offset)]
field_name = self.access_path_to_field_name(field_path, fmt)
# Rewrite `(&x)->y` to `x.y` by stripping `AddressOf` & setting deref=False
deref = True
if (
isinstance(var, AddressOf)
and not var.expr.type.is_array()
and field_name.startswith("->")
):
var = var.expr
field_name = field_name.replace("->", ".", 1)
deref = False
# Rewrite `x->unk0` to `*x` and `x.unk0` to `x`, unless has_nonzero_access
if self.offset == 0 and not has_nonzero_access:
return f"{'*' if deref else ''}{var.format(fmt)}"
return f"{parenthesize_for_struct_access(var, fmt)}{field_name}"
@dataclass(frozen=True, eq=True)
class ArrayAccess(Expression):
# Represents ptr[index]. eq=True for symmetry with StructAccess.
ptr: Expression
index: Expression
type: Type = field(compare=False)
def dependencies(self) -> List[Expression]:
return [self.ptr, self.index]
def format(self, fmt: Formatter) -> str:
base = parenthesize_for_struct_access(self.ptr, fmt)
index = format_expr(self.index, fmt)
return f"{base}[{index}]"
@dataclass(eq=False)
class GlobalSymbol(Expression):
symbol_name: str
type: Type
asm_data_entry: Optional[AsmDataEntry] = None
symbol_in_context: bool = False
type_provided: bool = False
initializer_in_typemap: bool = False
demangled_str: Optional[str] = None
def dependencies(self) -> List[Expression]:
return []
def is_string_constant(self) -> bool:
ent = self.asm_data_entry
if not ent or not ent.is_string:
return False
return len(ent.data) == 1 and isinstance(ent.data[0], bytes)
def format_string_constant(self, fmt: Formatter) -> str:
assert self.is_string_constant(), "checked by caller"
assert self.asm_data_entry and isinstance(self.asm_data_entry.data[0], bytes)
has_trailing_null = False
data = self.asm_data_entry.data[0]
while data and data[-1] == 0:
data = data[:-1]
has_trailing_null = True
data = b"".join(map(escape_byte, data))
strdata = data.decode("utf-8", "backslashreplace")
ret = f'"{strdata}"'
if not has_trailing_null:
ret += " /* not null-terminated */"
return ret
def format(self, fmt: Formatter) -> str:
return self.symbol_name
def potential_array_dim(self, element_size: int) -> Tuple[int, int]:
# If we don't have the .data/.rodata entry for this symbol, we can't guess
# its array dimension. Jump tables are ignored and not treated as arrays.
if self.asm_data_entry is None or self.asm_data_entry.is_jtbl:
return 0, element_size
min_data_size, max_data_size = self.asm_data_entry.size_range_bytes()
if element_size > max_data_size:
# The type is too big for the data (not an array)
return 0, max_data_size
# Check if it's possible that this symbol is not an array, and is just 1 element
if min_data_size <= element_size <= max_data_size and not self.type.is_array():
return 1, 0
array_dim, extra_bytes = divmod(min_data_size, element_size)
if extra_bytes != 0:
# bytes from the padding, then indicate that in the return value.
padding_bytes = element_size - extra_bytes
if min_data_size + padding_bytes > max_data_size:
return array_dim, extra_bytes
# Include potential padding in the array. Although this is unlikely to match the original C,
# it's much easier to manually remove all or some of these elements than to add them back in.
return max_data_size // element_size, 0
@dataclass(frozen=True, eq=True)
class Literal(Expression):
value: int
type: Type = field(compare=False, default_factory=Type.any)
elide_cast: bool = field(compare=False, default=False)
def dependencies(self) -> List[Expression]:
return []
def format(self, fmt: Formatter, force_dec: bool = False) -> str:
enum_name = self.type.get_enum_name(self.value)
if enum_name is not None:
return enum_name
if self.type.is_likely_float():
if self.type.get_size_bits() == 64:
return format_f64_imm(self.value)
else:
return format_f32_imm(self.value) + "f"
if self.type.is_pointer() and self.value == 0:
return "NULL"
prefix = ""
suffix = ""
if not fmt.skip_casts and not self.elide_cast:
if self.type.is_pointer():
prefix = f"({self.type.format(fmt)})"
if self.type.is_unsigned():
suffix = "U"
if force_dec:
value = str(self.value)
else:
size_bits = self.type.get_size_bits()
v = self.value
if (
self.type.is_signed()
and size_bits
and v & (1 << (size_bits - 1))
and v > (3 << (size_bits - 2))
and v < 2 ** size_bits
):
v -= 1 << size_bits
value = fmt.format_int(v, size_bits=size_bits)
return prefix + value + suffix
def likely_partial_offset(self) -> bool:
return self.value % 2 ** 15 in (0, 2 ** 15 - 1) and self.value < 0x1000000
@dataclass(frozen=True, eq=True)
class AddressOf(Expression):
expr: Expression
type: Type = field(compare=False, default_factory=Type.ptr)
def dependencies(self) -> List[Expression]:
return [self.expr]
def format(self, fmt: Formatter) -> str:
if isinstance(self.expr, GlobalSymbol):
if self.expr.is_string_constant():
return self.expr.format_string_constant(fmt)
if self.expr.type.is_array():
return f"{self.expr.format(fmt)}"
if self.expr.type.is_function():
return f"{self.expr.format(fmt)}"
if isinstance(self.expr, StructAccess):
ref = self.expr.make_reference()
if ref:
return f"{ref.format(fmt)}"
return f"&{self.expr.format(fmt)}"
@dataclass(frozen=True)
class Lwl(Expression):
load_expr: Expression
key: Tuple[int, object]
type: Type = field(compare=False, default_factory=Type.any_reg)
def dependencies(self) -> List[Expression]:
return [self.load_expr]
def format(self, fmt: Formatter) -> str:
return f"MIPS2C_LWL({self.load_expr.format(fmt)})"
@dataclass(frozen=True)
class Load3Bytes(Expression):
load_expr: Expression
type: Type = field(compare=False, default_factory=Type.any_reg)
def dependencies(self) -> List[Expression]:
return [self.load_expr]
def format(self, fmt: Formatter) -> str:
if fmt.valid_syntax:
return f"MIPS2C_FIRST3BYTES({self.load_expr.format(fmt)})"
return f"(first 3 bytes) {self.load_expr.format(fmt)}"
@dataclass(frozen=True)
class UnalignedLoad(Expression):
load_expr: Expression
type: Type = field(compare=False, default_factory=Type.any_reg)
def dependencies(self) -> List[Expression]:
return [self.load_expr]
def format(self, fmt: Formatter) -> str:
if fmt.valid_syntax:
return f"MIPS2C_UNALIGNED32({self.load_expr.format(fmt)})"
return f"(unaligned s32) {self.load_expr.format(fmt)}"
@dataclass(frozen=False, eq=False)
class EvalOnceExpr(Expression):
wrapped_expr: Expression
var: Var
type: Type
emit_exactly_once: bool
trivial: bool
forced_emit: bool = False
num_usages: int = 0
def dependencies(self) -> List[Expression]:
if self.need_decl():
return []
return [self.wrapped_expr]
def use(self) -> None:
self.num_usages += 1
if self.trivial or (self.num_usages == 1 and not self.emit_exactly_once):
self.wrapped_expr.use()
def force(self) -> None:
# getting this wrong are pretty mild -- it just causes extraneous var
# emission in rare cases.
self.trivial = False
self.forced_emit = True
self.use()
self.use()
def need_decl(self) -> bool:
return self.num_usages > 1 and not self.trivial
def format(self, fmt: Formatter) -> str:
if not self.need_decl():
return self.wrapped_expr.format(fmt)
else:
return self.var.format(fmt)
@dataclass(frozen=False, eq=False)
class PhiExpr(Expression):
reg: Register
node: Node
type: Type
used_phis: List["PhiExpr"]
name: Optional[str] = None
num_usages: int = 0
replacement_expr: Optional[Expression] = None
used_by: Optional["PhiExpr"] = None
def dependencies(self) -> List[Expression]:
return []
def get_var_name(self) -> str:
return self.name or f"unnamed-phi({self.reg.register_name})"
def use(self, from_phi: Optional["PhiExpr"] = None) -> None:
if self.num_usages == 0:
self.used_phis.append(self)
self.used_by = from_phi
self.num_usages += 1
if self.used_by != from_phi:
self.used_by = None
if self.replacement_expr is not None:
self.replacement_expr.use()
def propagates_to(self) -> "PhiExpr":
if self.used_by is None or self.replacement_expr is not None:
return self
return self.used_by.propagates_to()
def format(self, fmt: Formatter) -> str:
if self.replacement_expr:
return self.replacement_expr.format(fmt)
return self.get_var_name()
@dataclass
class SwitchControl:
control_expr: Expression
jump_table: Optional[GlobalSymbol] = None
offset: int = 0
is_irregular: bool = False
def matches_guard_condition(self, cond: Condition) -> bool:
cmp_expr = simplify_condition(cond)
if not isinstance(cmp_expr, BinaryOp) or cmp_expr.op not in (">=", ">"):
return False
cmp_exclusive = cmp_expr.op == ">"
# The LHS may have been wrapped in a u32 cast
left_expr = late_unwrap(cmp_expr.left)
if isinstance(left_expr, Cast):
left_expr = late_unwrap(left_expr.expr)
if self.offset != 0:
if (
not isinstance(left_expr, BinaryOp)
or late_unwrap(left_expr.left) != late_unwrap(self.control_expr)
or left_expr.op != "+"
or late_unwrap(left_expr.right) != Literal(-self.offset)
):
return False
elif left_expr != late_unwrap(self.control_expr):
return False
right_expr = late_unwrap(cmp_expr.right)
if (
self.jump_table is None
or self.jump_table.asm_data_entry is None
or not self.jump_table.asm_data_entry.is_jtbl
or not isinstance(right_expr, Literal)
):
return False
# Count the number of labels (exclude padding bytes)
jump_table_len = sum(
isinstance(e, str) for e in self.jump_table.asm_data_entry.data
)
return right_expr.value + int(cmp_exclusive) == jump_table_len
@staticmethod
def irregular_from_expr(control_expr: Expression) -> "SwitchControl":
return SwitchControl(
control_expr=control_expr,
jump_table=None,
offset=0,
is_irregular=True,
)
@staticmethod
def from_expr(expr: Expression) -> "SwitchControl":
# The "error" expression we use if we aren't able to parse `expr`
error_expr = SwitchControl(expr)
struct_expr = early_unwrap(expr)
if not isinstance(struct_expr, StructAccess) or struct_expr.offset != 0:
return error_expr
add_expr = early_unwrap(struct_expr.struct_var)
if not isinstance(add_expr, BinaryOp) or add_expr.op != "+":
return error_expr
left_expr, right_expr = early_unwrap(add_expr.left), early_unwrap(
add_expr.right
)
if isinstance(left_expr, AddressOf) and isinstance(
left_expr.expr, GlobalSymbol
):
jtbl_addr_expr, mul_expr = left_expr, right_expr
elif isinstance(right_expr, AddressOf) and isinstance(
right_expr.expr, GlobalSymbol
):
mul_expr, jtbl_addr_expr = left_expr, right_expr
else:
return error_expr
jump_table = jtbl_addr_expr.expr
assert isinstance(jump_table, GlobalSymbol)
if (
not isinstance(mul_expr, BinaryOp)
or mul_expr.op != "*"
or early_unwrap(mul_expr.right) != Literal(4)
):
return error_expr
control_expr = mul_expr.left
offset = 0
uw_control_expr = early_unwrap(control_expr)
if isinstance(uw_control_expr, BinaryOp) and uw_control_expr.op == "+":
offset_lit = early_unwrap(uw_control_expr.right)
if isinstance(offset_lit, Literal):
control_expr = uw_control_expr.left
offset = -offset_lit.value
if jump_table.asm_data_entry is None or not jump_table.asm_data_entry.is_jtbl:
return error_expr
return SwitchControl(control_expr, jump_table, offset)
@dataclass
class EvalOnceStmt(Statement):
expr: EvalOnceExpr
def need_decl(self) -> bool:
return self.expr.need_decl()
def should_write(self) -> bool:
if self.expr.emit_exactly_once:
return self.expr.num_usages != 1
else:
return self.need_decl()
def format(self, fmt: Formatter) -> str:
val_str = format_expr(elide_casts_for_store(self.expr.wrapped_expr), fmt)
if self.expr.emit_exactly_once and self.expr.num_usages == 0:
return f"{val_str};"
return f"{self.expr.var.format(fmt)} = {val_str};"
@dataclass
class SetPhiStmt(Statement):
phi: PhiExpr
expr: Expression
def should_write(self) -> bool:
expr = self.expr
if isinstance(expr, PhiExpr) and expr.propagates_to() != expr:
assert expr.propagates_to() == self.phi.propagates_to()
return False
if late_unwrap(expr) == self.phi.propagates_to():
return False
return True
def format(self, fmt: Formatter) -> str:
return format_assignment(self.phi.propagates_to(), self.expr, fmt)
@dataclass
class ExprStmt(Statement):
expr: Expression
def should_write(self) -> bool:
return True
def format(self, fmt: Formatter) -> str:
return f"{format_expr(self.expr, fmt)};"
@dataclass
class StoreStmt(Statement):
source: Expression
dest: Expression
def should_write(self) -> bool:
return True
def format(self, fmt: Formatter) -> str:
dest = self.dest
source = self.source
if (
isinstance(dest, StructAccess) and dest.late_has_known_type()
) or isinstance(dest, (ArrayAccess, LocalVar, RegisterVar, SubroutineArg)):
source = elide_casts_for_store(source)
return format_assignment(dest, source, fmt)
@dataclass
class CommentStmt(Statement):
contents: str
def should_write(self) -> bool:
return True
def format(self, fmt: Formatter) -> str:
return f"// {self.contents}"
def error_stmt(msg: str) -> ExprStmt:
return ExprStmt(ErrorExpr(msg))
@dataclass(frozen=True)
class AddressMode:
offset: int
rhs: Register
def __str__(self) -> str:
if self.offset:
return f"{self.offset}({self.rhs})"
else:
return f"({self.rhs})"
@dataclass(frozen=True)
class RawSymbolRef:
offset: int
sym: AsmGlobalSymbol
def __str__(self) -> str:
if self.offset:
return f"{self.sym.symbol_name} + {self.offset}"
else:
return self.sym.symbol_name
@dataclass
class RegMeta:
inherited: bool = False
is_read: bool = False
function_return: bool = False
# function_return = True, or is a passed in argument
uninteresting: bool = False
# True if the regdata must be replaced by variable if it is ever read
force: bool = False
# True if the regdata was assigned by an Instruction marked as in_pattern;
# it was part of a matched IR pattern but couldn't be elided at the time
in_pattern: bool = False
@dataclass
class RegData:
value: Expression
meta: RegMeta
@dataclass
class RegInfo:
stack_info: StackInfo = field(repr=False)
contents: Dict[Register, RegData] = field(default_factory=dict)
read_inherited: Set[Register] = field(default_factory=set)
_active_instr: Optional[Instruction] = None
def __getitem__(self, key: Register) -> Expression:
if self._active_instr is not None and key not in self._active_instr.inputs:
lineno = self._active_instr.meta.lineno
return ErrorExpr(f"Read from unset register {key} on line {lineno}")
if key == Register("zero"):
return Literal(0)
data = self.contents.get(key)
if data is None:
return ErrorExpr(f"Read from unset register {key}")
ret = data.value
data.meta.is_read = True
if data.meta.inherited:
self.read_inherited.add(key)
if isinstance(ret, PassedInArg) and not ret.copied:
val, arg = self.stack_info.get_argument(ret.value)
self.stack_info.add_argument(arg)
val.type.unify(ret.type)
return val
if data.meta.force:
assert isinstance(ret, EvalOnceExpr)
ret.force()
return ret
def __contains__(self, key: Register) -> bool:
return key in self.contents
def __setitem__(self, key: Register, value: Expression) -> None:
self.set_with_meta(key, value, RegMeta())
def set_with_meta(self, key: Register, value: Expression, meta: RegMeta) -> None:
if self._active_instr is not None and key not in self._active_instr.outputs:
raise DecompFailure(f"Undeclared write to {key} in {self._active_instr}")
self.unchecked_set_with_meta(key, value, meta)
def unchecked_set_with_meta(
self, key: Register, value: Expression, meta: RegMeta
) -> None:
assert key != Register("zero")
self.contents[key] = RegData(value, meta)
def __delitem__(self, key: Register) -> None:
assert key != Register("zero")
del self.contents[key]
def get_raw(self, key: Register) -> Optional[Expression]:
data = self.contents.get(key)
return data.value if data is not None else None
def get_meta(self, key: Register) -> Optional[RegMeta]:
data = self.contents.get(key)
return data.meta if data is not None else None
@contextmanager
def current_instr(self, instr: Instruction) -> Iterator[None]:
self._active_instr = instr
try:
with current_instr(instr):
yield
finally:
self._active_instr = None
def __str__(self) -> str:
return ", ".join(
f"{k}: {v.value}"
for k, v in sorted(self.contents.items(), key=lambda x: x[0].register_name)
if not self.stack_info.should_save(v.value, None)
)
@dataclass
class BlockInfo:
to_write: List[Statement]
return_value: Optional[Expression]
switch_control: Optional[SwitchControl]
branch_condition: Optional[Condition]
final_register_states: RegInfo
has_function_call: bool
def __str__(self) -> str:
newline = "\n\t"
return "\n".join(
[
f"Statements: {newline.join(str(w) for w in self.statements_to_write())}",
f"Branch condition: {self.branch_condition}",
f"Final register states: {self.final_register_states}",
]
)
def statements_to_write(self) -> List[Statement]:
return [st for st in self.to_write if st.should_write()]
def get_block_info(node: Node) -> BlockInfo:
ret = node.block.block_info
assert isinstance(ret, BlockInfo)
return ret
@dataclass
class InstrArgs:
raw_args: List[Argument]
regs: RegInfo = field(repr=False)
stack_info: StackInfo = field(repr=False)
def raw_arg(self, index: int) -> Argument:
assert index >= 0
if index >= len(self.raw_args):
raise DecompFailure(
f"Too few arguments for instruction, expected at least {index + 1}"
)
return self.raw_args[index]
def reg_ref(self, index: int) -> Register:
ret = self.raw_arg(index)
if not isinstance(ret, Register):
raise DecompFailure(
f"Expected instruction argument to be a register, but found {ret}"
)
return ret
def imm_value(self, index: int) -> int:
arg = self.full_imm(index)
assert isinstance(arg, Literal)
return arg.value
def reg(self, index: int) -> Expression:
return self.regs[self.reg_ref(index)]
def dreg(self, index: int) -> Expression:
reg = self.reg_ref(index)
if not reg.is_float():
raise DecompFailure(
f"Expected instruction argument {reg} to be a float register"
)
ret = self.regs[reg]
# PPC: FPR's hold doubles (64 bits), so we don't need to do anything special
if self.stack_info.global_info.arch.arch == Target.ArchEnum.PPC:
return ret
# MIPS: Look at the paired FPR to get the full 64-bit value
if not isinstance(ret, Literal) or ret.type.get_size_bits() == 64:
return ret
reg_num = int(reg.register_name[1:])
if reg_num % 2 != 0:
raise DecompFailure(
"Tried to use a double-precision instruction with odd-numbered float "
f"register {reg}"
)
other = self.regs[Register(f"f{reg_num+1}")]
if not isinstance(other, Literal) or other.type.get_size_bits() == 64:
raise DecompFailure(
f"Unable to determine a value for double-precision register {reg} "
"whose second half is non-static. This is a mips_to_c restriction "
"which may be lifted in the future."
)
value = ret.value | (other.value << 32)
return Literal(value, type=Type.f64())
def cmp_reg(self, key: str) -> Condition:
cond = self.regs[Register(key)]
if not isinstance(cond, Condition):
cond = BinaryOp.icmp(cond, "!=", Literal(0))
return cond
def full_imm(self, index: int) -> Expression:
arg = strip_macros(self.raw_arg(index))
ret = literal_expr(arg, self.stack_info)
return ret
def imm(self, index: int) -> Expression:
ret = self.full_imm(index)
if isinstance(ret, Literal):
return Literal(((ret.value + 0x8000) & 0xFFFF) - 0x8000)
return ret
def unsigned_imm(self, index: int) -> Expression:
ret = self.full_imm(index)
if isinstance(ret, Literal):
return Literal(ret.value & 0xFFFF)
return ret
def hi_imm(self, index: int) -> Argument:
arg = self.raw_arg(index)
if not isinstance(arg, Macro) or arg.macro_name not in ("hi", "ha", "h"):
raise DecompFailure(
f"Got lui/lis instruction with macro other than %hi/@ha/@h: {arg}"
)
return arg.argument
def shifted_imm(self, index: int) -> Expression:
# TODO: Should this be part of hi_imm? Do we need to handle @ha?
raw_imm = self.unsigned_imm(index)
assert isinstance(raw_imm, Literal)
return Literal(raw_imm.value << 16)
def memory_ref(self, index: int) -> Union[AddressMode, RawSymbolRef]:
ret = strip_macros(self.raw_arg(index))
# In MIPS, we want to allow "lw $v0, symbol + 4", which is outputted by
# some disassemblers (like IDA) even though it isn't valid assembly.
if isinstance(ret, AsmGlobalSymbol):
return RawSymbolRef(offset=0, sym=ret)
if (
isinstance(ret, BinOp)
and ret.op in "+-"
and isinstance(ret.lhs, AsmGlobalSymbol)
and isinstance(ret.rhs, AsmLiteral)
):
sign = 1 if ret.op == "+" else -1
return RawSymbolRef(offset=(ret.rhs.value * sign), sym=ret.lhs)
if not isinstance(ret, AsmAddressMode):
raise DecompFailure(
"Expected instruction argument to be of the form offset($register), "
f"but found {ret}"
)
if not isinstance(ret.lhs, AsmLiteral):
raise DecompFailure(
f"Unable to parse offset for instruction argument {ret}. "
"Expected a constant or a %lo macro."
)
return AddressMode(offset=ret.lhs.signed_value(), rhs=ret.rhs)
def count(self) -> int:
return len(self.raw_args)
def deref(
arg: Union[AddressMode, RawSymbolRef, Expression],
regs: RegInfo,
stack_info: StackInfo,
*,
size: int,
store: bool = False,
) -> Expression:
if isinstance(arg, Expression):
offset = 0
var = arg
elif isinstance(arg, AddressMode):
offset = arg.offset
if stack_info.is_stack_reg(arg.rhs):
return stack_info.get_stack_var(offset, store=store)
var = regs[arg.rhs]
else:
offset = arg.offset
var = stack_info.global_info.address_of_gsym(arg.sym.symbol_name)
if isinstance(var, Literal) and var.value % (2 ** 16) == 0:
var = Literal(var.value + offset, type=var.type)
offset = 0
uw_var = early_unwrap(var)
if isinstance(uw_var, BinaryOp) and uw_var.op == "+":
for base, addend in [(uw_var.left, uw_var.right), (uw_var.right, uw_var.left)]:
if isinstance(addend, Literal) and addend.likely_partial_offset():
offset += addend.value
var = base
uw_var = early_unwrap(var)
break
var.type.unify(Type.ptr())
stack_info.record_struct_access(var, offset)
field_name: Optional[str] = None
type: Type = stack_info.unique_type_for("struct", (uw_var, offset), Type.any())
array_expr = array_access_from_add(
var, offset, stack_info, target_size=size, ptr=False
)
if array_expr is not None:
return array_expr
field_path, field_type, _ = var.type.get_deref_field(offset, target_size=size)
if field_path is not None:
field_type.unify(type)
type = field_type
else:
field_path = None
return StructAccess(
struct_var=var,
offset=offset,
target_size=size,
field_path=field_path,
stack_info=stack_info,
type=type,
)
def is_trivial_expression(expr: Expression) -> bool:
if isinstance(
expr,
(
EvalOnceExpr,
Literal,
GlobalSymbol,
LocalVar,
PassedInArg,
PhiExpr,
RegisterVar,
SubroutineArg,
),
):
return True
if isinstance(expr, AddressOf):
return all(is_trivial_expression(e) for e in expr.dependencies())
if isinstance(expr, Cast):
return expr.is_trivial()
return False
def is_type_obvious(expr: Expression) -> bool:
if isinstance(
expr,
(
Cast,
Literal,
AddressOf,
LocalVar,
PhiExpr,
PassedInArg,
RegisterVar,
FuncCall,
),
):
return True
if isinstance(expr, EvalOnceExpr):
if expr.need_decl():
return True
return is_type_obvious(expr.wrapped_expr)
return False
def simplify_condition(expr: Expression) -> Expression:
if isinstance(expr, EvalOnceExpr) and not expr.need_decl():
return simplify_condition(expr.wrapped_expr)
if isinstance(expr, UnaryOp):
inner = simplify_condition(expr.expr)
if expr.op == "!" and isinstance(inner, Condition):
return inner.negated()
return UnaryOp(expr=inner, op=expr.op, type=expr.type)
if isinstance(expr, BinaryOp):
left = simplify_condition(expr.left)
right = simplify_condition(expr.right)
if isinstance(left, BinaryOp) and left.is_comparison() and right == Literal(0):
if expr.op == "==":
return simplify_condition(left.negated())
if expr.op == "!=":
return left
if (
expr.is_comparison()
and isinstance(left, Literal)
and not isinstance(right, Literal)
):
return BinaryOp(
left=right,
op=expr.op.translate(str.maketrans("<>", "><")),
right=left,
type=expr.type,
)
return BinaryOp(left=left, op=expr.op, right=right, type=expr.type)
return expr
def balanced_parentheses(string: str) -> bool:
bal = 0
for c in string:
if c == "(":
bal += 1
elif c == ")":
if bal == 0:
return False
bal -= 1
return bal == 0
def format_expr(expr: Expression, fmt: Formatter) -> str:
ret = expr.format(fmt)
if ret.startswith("(") and balanced_parentheses(ret[1:-1]):
return ret[1:-1]
return ret
def format_assignment(dest: Expression, source: Expression, fmt: Formatter) -> str:
dest = late_unwrap(dest)
source = late_unwrap(source)
if isinstance(source, BinaryOp) and source.op in COMPOUND_ASSIGNMENT_OPS:
rhs = None
if late_unwrap(source.left) == dest:
rhs = source.right
elif late_unwrap(source.right) == dest and source.op in ASSOCIATIVE_OPS:
rhs = source.left
if rhs is not None:
return f"{dest.format(fmt)} {source.op}= {format_expr(rhs, fmt)};"
return f"{dest.format(fmt)} = {format_expr(source, fmt)};"
def parenthesize_for_struct_access(expr: Expression, fmt: Formatter) -> str:
s = expr.format(fmt)
if (
s.startswith("*")
or s.startswith("&")
or (isinstance(expr, Cast) and expr.needed_for_store())
):
return f"({s})"
return s
def elide_casts_for_store(expr: Expression) -> Expression:
uw_expr = late_unwrap(expr)
if isinstance(uw_expr, Cast) and not uw_expr.needed_for_store():
return elide_casts_for_store(uw_expr.expr)
if isinstance(uw_expr, Literal) and uw_expr.type.is_int():
return replace(uw_expr, elide_cast=True)
return uw_expr
def uses_expr(expr: Expression, expr_filter: Callable[[Expression], bool]) -> bool:
if expr_filter(expr):
return True
for e in expr.dependencies():
if uses_expr(e, expr_filter):
return True
return False
def late_unwrap(expr: Expression) -> Expression:
if isinstance(expr, EvalOnceExpr) and not expr.need_decl():
return late_unwrap(expr.wrapped_expr)
if isinstance(expr, PhiExpr) and expr.replacement_expr is not None:
return late_unwrap(expr.replacement_expr)
return expr
def early_unwrap(expr: Expression) -> Expression:
if (
isinstance(expr, EvalOnceExpr)
and not expr.forced_emit
and not expr.emit_exactly_once
):
return early_unwrap(expr.wrapped_expr)
return expr
def early_unwrap_ints(expr: Expression) -> Expression:
uw_expr = early_unwrap(expr)
if isinstance(uw_expr, Cast) and uw_expr.reinterpret and uw_expr.type.is_int():
return early_unwrap_ints(uw_expr.expr)
return uw_expr
def unwrap_deep(expr: Expression) -> Expression:
if isinstance(expr, EvalOnceExpr):
return unwrap_deep(expr.wrapped_expr)
return expr
def literal_expr(arg: Argument, stack_info: StackInfo) -> Expression:
if isinstance(arg, AsmGlobalSymbol):
return stack_info.global_info.address_of_gsym(arg.symbol_name)
if isinstance(arg, AsmLiteral):
return Literal(arg.value)
if isinstance(arg, BinOp):
lhs = literal_expr(arg.lhs, stack_info)
rhs = literal_expr(arg.rhs, stack_info)
return BinaryOp.int(left=lhs, op=arg.op, right=rhs)
raise DecompFailure(f"Instruction argument {arg} must be a literal")
def imm_add_32(expr: Expression) -> Expression:
if isinstance(expr, Literal):
return as_intish(Literal(expr.value + 32))
else:
return BinaryOp.int(expr, "+", Literal(32))
def fn_op(fn_name: str, args: List[Expression], type: Type) -> FuncCall:
fn_sig = FunctionSignature(
return_type=type,
params=[FunctionParam(type=arg.type) for arg in args],
params_known=True,
is_variadic=False,
)
return FuncCall(
function=GlobalSymbol(symbol_name=fn_name, type=Type.function(fn_sig)),
args=args,
type=type,
)
def void_fn_op(fn_name: str, args: List[Expression]) -> ExprStmt:
fn_call = fn_op(fn_name, args, Type.any_reg())
fn_call.use()
return ExprStmt(fn_call)
def load_upper(args: InstrArgs) -> Expression:
arg = args.raw_arg(1)
if not isinstance(arg, Macro):
assert not isinstance(
arg, Literal
), "normalize_instruction should convert lui/lis <literal> to li"
raise DecompFailure(
f"lui/lis argument must be a literal or %hi/@ha macro, found {arg}"
)
hi_arg = args.hi_imm(1)
if (
isinstance(hi_arg, BinOp)
and hi_arg.op in "+-"
and isinstance(hi_arg.lhs, AsmGlobalSymbol)
and isinstance(hi_arg.rhs, AsmLiteral)
):
sym = hi_arg.lhs
offset = hi_arg.rhs.value * (-1 if hi_arg.op == "-" else 1)
elif isinstance(hi_arg, AsmGlobalSymbol):
sym = hi_arg
offset = 0
else:
raise DecompFailure(f"Invalid %hi/@ha argument {hi_arg}")
stack_info = args.stack_info
source = stack_info.global_info.address_of_gsym(sym.symbol_name)
imm = Literal(offset)
return handle_addi_real(args.reg_ref(0), None, source, imm, stack_info)
def handle_convert(expr: Expression, dest_type: Type, source_type: Type) -> Cast:
silent = dest_type.data().kind != source_type.data().kind
expr.type.unify(source_type)
return Cast(expr=expr, type=dest_type, silent=silent, reinterpret=False)
def handle_la(args: InstrArgs) -> Expression:
target = args.memory_ref(1)
stack_info = args.stack_info
if isinstance(target, AddressMode):
return handle_addi(
InstrArgs(
raw_args=[args.reg_ref(0), target.rhs, AsmLiteral(target.offset)],
regs=args.regs,
stack_info=args.stack_info,
)
)
var = stack_info.global_info.address_of_gsym(target.sym.symbol_name)
return add_imm(var, Literal(target.offset), stack_info)
def handle_or(left: Expression, right: Expression) -> Expression:
if left == right:
return left
if isinstance(left, Literal) and isinstance(right, Literal):
if (((left.value & 0xFFFF) == 0 and (right.value & 0xFFFF0000) == 0)) or (
(right.value & 0xFFFF) == 0 and (left.value & 0xFFFF0000) == 0
):
return Literal(value=(left.value | right.value))
return BinaryOp.int(left=left, op="|", right=right)
def handle_sltu(args: InstrArgs) -> Expression:
right = args.reg(2)
if args.reg_ref(1) == Register("zero"):
uw_right = early_unwrap(right)
if isinstance(uw_right, BinaryOp) and uw_right.op == "^":
return BinaryOp.icmp(uw_right.left, "!=", uw_right.right)
return BinaryOp.icmp(right, "!=", Literal(0))
else:
left = args.reg(1)
return BinaryOp.ucmp(left, "<", right)
def handle_sltiu(args: InstrArgs) -> Expression:
left = args.reg(1)
right = args.imm(2)
if isinstance(right, Literal):
value = right.value & 0xFFFFFFFF
if value == 1:
uw_left = early_unwrap(left)
if isinstance(uw_left, BinaryOp) and uw_left.op == "^":
return BinaryOp.icmp(uw_left.left, "==", uw_left.right)
return BinaryOp.icmp(left, "==", Literal(0))
else:
right = Literal(value)
return BinaryOp.ucmp(left, "<", right)
def handle_addi(args: InstrArgs) -> Expression:
stack_info = args.stack_info
source_reg = args.reg_ref(1)
source = args.reg(1)
imm = args.imm(2)
uw_source = early_unwrap(source)
if (
isinstance(uw_source, BinaryOp)
and uw_source.op == "+"
and isinstance(uw_source.right, Literal)
and uw_source.right.value % 0x10000 == 0
and isinstance(imm, Literal)
):
return add_imm(
uw_source.left, Literal(imm.value + uw_source.right.value), stack_info
)
return handle_addi_real(args.reg_ref(0), source_reg, source, imm, stack_info)
def handle_addis(args: InstrArgs) -> Expression:
stack_info = args.stack_info
source_reg = args.reg_ref(1)
source = args.reg(1)
imm = args.shifted_imm(2)
return handle_addi_real(args.reg_ref(0), source_reg, source, imm, stack_info)
def handle_addi_real(
output_reg: Register,
source_reg: Optional[Register],
source: Expression,
imm: Expression,
stack_info: StackInfo,
) -> Expression:
if source_reg is not None and stack_info.is_stack_reg(source_reg):
assert isinstance(imm, Literal)
if stack_info.is_stack_reg(output_reg):
return source
var = stack_info.get_stack_var(imm.value, store=False)
if isinstance(var, LocalVar):
stack_info.add_local_var(var)
return AddressOf(var, type=var.type.reference())
else:
return add_imm(source, imm, stack_info)
def add_imm(source: Expression, imm: Expression, stack_info: StackInfo) -> Expression:
if imm == Literal(0):
return source
elif source.type.is_pointer_or_array():
if isinstance(imm, Literal) and not imm.likely_partial_offset():
array_access = array_access_from_add(
source, imm.value, stack_info, target_size=None, ptr=True
)
if array_access is not None:
return array_access
field_path, field_type, _ = source.type.get_deref_field(
imm.value, target_size=None
)
if field_path is not None:
return AddressOf(
StructAccess(
struct_var=source,
offset=imm.value,
target_size=None,
field_path=field_path,
stack_info=stack_info,
type=field_type,
),
type=field_type.reference(),
)
if isinstance(imm, Literal):
target = source.type.get_pointer_target()
if target:
target_size = target.get_size_bytes()
if target_size and imm.value % target_size == 0:
return BinaryOp(
left=source, op="+", right=as_intish(imm), type=source.type
)
return BinaryOp(left=source, op="+", right=as_intish(imm), type=Type.ptr())
elif isinstance(source, Literal) and isinstance(imm, Literal):
return Literal(source.value + imm.value)
else:
return BinaryOp.intptr(left=source, op="+", right=imm)
def handle_load(args: InstrArgs, type: Type) -> Expression:
# Though really, it would be great to expose the load types somehow...
size = type.get_size_bytes()
assert size is not None
expr = deref(args.memory_ref(1), args.regs, args.stack_info, size=size)
# Detect rodata constants
if isinstance(expr, StructAccess) and expr.offset == 0:
target = early_unwrap(expr.struct_var)
if (
isinstance(target, AddressOf)
and isinstance(target.expr, GlobalSymbol)
and type.is_likely_float()
):
sym_name = target.expr.symbol_name
ent = args.stack_info.global_info.asm_data_value(sym_name)
if (
ent
and ent.data
and isinstance(ent.data[0], bytes)
and len(ent.data[0]) >= size
and ent.is_readonly
and type.unify(target.expr.type)
):
data = ent.data[0][:size]
val: int
if size == 4:
(val,) = struct.unpack(">I", data)
else:
(val,) = struct.unpack(">Q", data)
return Literal(value=val, type=type)
return as_type(expr, type, silent=True)
def deref_unaligned(
arg: Union[AddressMode, RawSymbolRef],
regs: RegInfo,
stack_info: StackInfo,
*,
store: bool = False,
) -> Expression:
# We don't know the correct size pass to deref. Passing None would signal that we
return deref(arg, regs, stack_info, size=1, store=store)
def handle_lwl(args: InstrArgs) -> Expression:
# destination-first operation)
ref = args.memory_ref(1)
expr = deref_unaligned(ref, args.regs, args.stack_info)
key: Tuple[int, object]
if isinstance(ref, AddressMode):
key = (ref.offset, args.regs[ref.rhs])
else:
key = (ref.offset, ref.sym)
return Lwl(expr, key)
def handle_lwr(args: InstrArgs) -> Expression:
# Unaligned load for the right part of a register. This lwr may merge with an
# existing lwl, if it loads from the same target but with an offset that's +3.
uw_old_value = early_unwrap(args.reg(0))
ref = args.memory_ref(1)
lwl_key: Tuple[int, object]
if isinstance(ref, AddressMode):
lwl_key = (ref.offset - 3, args.regs[ref.rhs])
else:
lwl_key = (ref.offset - 3, ref.sym)
if isinstance(uw_old_value, Lwl) and uw_old_value.key[0] == lwl_key[0]:
return UnalignedLoad(uw_old_value.load_expr)
if ref.offset % 4 == 2:
left_mem_ref = replace(ref, offset=ref.offset - 2)
load_expr = deref_unaligned(left_mem_ref, args.regs, args.stack_info)
return Load3Bytes(load_expr)
return ErrorExpr("Unable to handle lwr; missing a corresponding lwl")
def make_store(args: InstrArgs, type: Type) -> Optional[StoreStmt]:
size = type.get_size_bytes()
assert size is not None
stack_info = args.stack_info
source_reg = args.reg_ref(0)
source_raw = args.regs.get_raw(source_reg)
if type.is_likely_float() and size == 8:
source_val = args.dreg(0)
else:
source_val = args.reg(0)
target = args.memory_ref(1)
is_stack = isinstance(target, AddressMode) and stack_info.is_stack_reg(target.rhs)
if (
is_stack
and source_raw is not None
and stack_info.should_save(source_raw, target.offset)
):
return None
dest = deref(target, args.regs, stack_info, size=size, store=True)
dest.type.unify(type)
return StoreStmt(source=as_type(source_val, type, silent=is_stack), dest=dest)
def make_storex(args: InstrArgs, type: Type) -> Optional[StoreStmt]:
size = type.get_size_bytes()
assert size is not None
source = args.reg(0)
ptr = BinaryOp.intptr(left=args.reg(1), op="+", right=args.reg(2))
dest = deref(ptr, args.regs, args.stack_info, size=size, store=True)
dest.type.unify(type)
return StoreStmt(source=as_type(source, type, silent=False), dest=dest)
def handle_swl(args: InstrArgs) -> Optional[StoreStmt]:
# swl in practice only occurs together with swr, so we can treat it as a regular
# store, with the expression wrapped in UnalignedLoad if needed.
source = args.reg(0)
target = args.memory_ref(1)
if not isinstance(early_unwrap(source), UnalignedLoad):
source = UnalignedLoad(source)
dest = deref_unaligned(target, args.regs, args.stack_info, store=True)
return StoreStmt(source=source, dest=dest)
def handle_swr(args: InstrArgs) -> Optional[StoreStmt]:
expr = early_unwrap(args.reg(0))
target = args.memory_ref(1)
if not isinstance(expr, Load3Bytes):
# Elide swr's that don't come from 3-byte-loading lwr's; they probably
return None
real_target = replace(target, offset=target.offset - 2)
dest = deref_unaligned(real_target, args.regs, args.stack_info, store=True)
return StoreStmt(source=expr, dest=dest)
def handle_sra(args: InstrArgs) -> Expression:
lhs = args.reg(1)
shift = args.imm(2)
if isinstance(shift, Literal) and shift.value in [16, 24]:
expr = early_unwrap(lhs)
pow2 = 1 << shift.value
if isinstance(expr, BinaryOp) and isinstance(expr.right, Literal):
tp = Type.s16() if shift.value == 16 else Type.s8()
rhs = expr.right.value
if expr.op == "<<" and rhs == shift.value:
return as_type(expr.left, tp, silent=False)
elif expr.op == "<<" and rhs > shift.value:
new_shift = fold_mul_chains(
BinaryOp.int(expr.left, "<<", Literal(rhs - shift.value))
)
return as_type(new_shift, tp, silent=False)
elif expr.op == "*" and rhs % pow2 == 0 and rhs != pow2:
mul = BinaryOp.int(expr.left, "*", Literal(value=rhs // pow2))
return as_type(mul, tp, silent=False)
return fold_divmod(
BinaryOp(as_sintish(lhs), ">>", as_intish(shift), type=Type.s32())
)
def handle_conditional_move(args: InstrArgs, nonzero: bool) -> Expression:
op = "!=" if nonzero else "=="
type = Type.any_reg()
return TernaryOp(
BinaryOp.scmp(args.reg(2), op, Literal(0)),
as_type(args.reg(1), type, silent=True),
as_type(args.reg(0), type, silent=True),
type,
)
def format_f32_imm(num: int) -> str:
packed = struct.pack(">I", num & (2 ** 32 - 1))
value = struct.unpack(">f", packed)[0]
if not value or value == 4294967296.0:
return str(value)
if abs(math.log10(abs(value))) > 6.9:
fmt_char = "e"
elif abs(value) < 1:
fmt_char = "f"
else:
fmt_char = "g"
def fmt(prec: int) -> str:
ret = ("{:." + str(prec) + fmt_char + "}").format(value)
if fmt_char == "e":
return ret.replace("e+", "e").replace("e0", "e").replace("e-0", "e-")
if "e" in ret:
# lead to us outputting numbers with more precision than we really have.
return "0"
return ret
# 20 decimals is more than enough for a float. Start there, then try to shrink it.
prec = 20
while prec > 0:
prec -= 1
value2 = float(fmt(prec))
if struct.pack(">f", value2) != packed:
prec += 1
break
if prec == 20:
# Uh oh, even the original value didn't format correctly. Fall back to str(),
return str(value)
ret = fmt(prec)
if "." not in ret:
ret += ".0"
return ret
def format_f64_imm(num: int) -> str:
(value,) = struct.unpack(">d", struct.pack(">Q", num & (2 ** 64 - 1)))
return str(value)
def fold_divmod(original_expr: BinaryOp) -> BinaryOp:
mult_high_ops = ("MULT_HI", "MULTU_HI")
possible_match_ops = mult_high_ops + ("-", "+", ">>")
if original_expr.is_floating() or original_expr.op not in possible_match_ops:
return original_expr
expr = original_expr
left_expr = early_unwrap_ints(expr.left)
right_expr = early_unwrap_ints(expr.right)
divisor_shift = 0
if (
isinstance(left_expr, BinaryOp)
and left_expr.op == ">>"
and isinstance(left_expr.right, Literal)
and expr.op == "+"
and isinstance(right_expr, CarryBit)
):
new_denom = 1 << left_expr.right.value
return BinaryOp.sint(
left=left_expr.left,
op="/",
right=Literal(new_denom),
silent=True,
)
if (
isinstance(left_expr, BinaryOp)
and left_expr.op == "/"
and isinstance(left_expr.right, Literal)
and expr.op == ">>"
and isinstance(right_expr, Literal)
):
new_denom = left_expr.right.value << right_expr.value
if new_denom < (1 << 32):
return BinaryOp.int(
left=left_expr.left,
op="/",
right=Literal(new_denom),
)
if expr.op == "-" and isinstance(right_expr, BinaryOp) and right_expr.op == "*":
div_expr = early_unwrap_ints(right_expr.left)
mod_base = early_unwrap_ints(right_expr.right)
if (
isinstance(div_expr, BinaryOp)
and early_unwrap_ints(div_expr.left) == left_expr
):
divisor = early_unwrap_ints(div_expr.right)
if (div_expr.op == "/" and divisor == mod_base) or (
div_expr.op == ">>"
and isinstance(divisor, Literal)
and isinstance(mod_base, Literal)
and (1 << divisor.value) == mod_base.value
):
return BinaryOp.int(left=left_expr, op="%", right=right_expr.right)
if (
expr.op == "-"
and isinstance(left_expr, BinaryOp)
and left_expr.op == ">>"
and early_unwrap_ints(left_expr.right) == Literal(31)
and isinstance(right_expr, BinaryOp)
and right_expr.op == "/"
and isinstance(right_expr.right, Literal)
):
left_expr, right_expr = (
replace(right_expr, right=Literal(-right_expr.right.value)),
left_expr,
)
if (
expr.op == "+"
and isinstance(left_expr, BinaryOp)
and left_expr.op == "/"
and isinstance(left_expr.right, Literal)
and left_expr.right.value <= (1 << 29)
and isinstance(right_expr, BinaryOp)
and early_unwrap_ints(right_expr.left) == left_expr
and right_expr.op == ">>"
and early_unwrap_ints(right_expr.right) == Literal(31)
):
return left_expr
if (
expr.op == "-"
and isinstance(left_expr, BinaryOp)
and left_expr.op == "/"
and isinstance(left_expr.right, Literal)
and isinstance(right_expr, BinaryOp)
and right_expr.op == ">>"
and early_unwrap_ints(right_expr.right) == Literal(31)
):
div_expr = left_expr
shift_var_expr = early_unwrap_ints(right_expr.left)
div_var_expr = early_unwrap_ints(div_expr.left)
if div_var_expr == shift_var_expr:
if isinstance(div_expr.right, Literal) and div_expr.right.value >= (
1 << 30
):
return BinaryOp.int(
left=div_expr.left,
op=div_expr.op,
right=div_expr.right,
)
return div_expr
# If the var is under 32 bits, the error term may look like `(x << K) >> 31` instead
if (
isinstance(shift_var_expr, BinaryOp)
and early_unwrap_ints(div_expr.left)
== early_unwrap_ints(shift_var_expr.left)
and shift_var_expr.op == "<<"
and isinstance(shift_var_expr.right, Literal)
):
return div_expr
# Shift on the result of the mul: MULT_HI(x, N) >> M, shift the divisor by M
if (
isinstance(left_expr, BinaryOp)
and expr.op == ">>"
and isinstance(right_expr, Literal)
):
divisor_shift += right_expr.value
expr = left_expr
left_expr = early_unwrap_ints(expr.left)
right_expr = early_unwrap_ints(expr.right)
# Normalize MULT_HI(N, x) to MULT_HI(x, N)
if isinstance(left_expr, Literal) and not isinstance(right_expr, Literal):
left_expr, right_expr = right_expr, left_expr
# Remove inner addition: (MULT_HI(x, N) + x) >> M --> MULT_HI(x, N) >> M
# MULT_HI performs signed multiplication, so the `+ x` acts as setting the 32nd bit
# while having a result with the same sign as x.
# We can ignore it because `round_div` can work with arbitrarily large constants
if (
isinstance(left_expr, BinaryOp)
and left_expr.op == "MULT_HI"
and expr.op == "+"
and early_unwrap_ints(left_expr.left) == right_expr
):
expr = left_expr
left_expr = early_unwrap_ints(expr.left)
right_expr = early_unwrap_ints(expr.right)
# Shift on the LHS of the mul: MULT_HI(x >> M, N) --> MULT_HI(x, N) >> M
if (
expr.op in mult_high_ops
and isinstance(left_expr, BinaryOp)
and left_expr.op == ">>"
and isinstance(left_expr.right, Literal)
):
divisor_shift += left_expr.right.value
left_expr = early_unwrap_ints(left_expr.left)
# Instead of checking for the error term precisely, just check that
# the quotient is "close enough" to the integer value
def round_div(x: int, y: int) -> Optional[int]:
if y <= 1:
return None
result = round(x / y)
if x / (y + 1) <= result <= x / (y - 1):
return result
return None
if expr.op in mult_high_ops and isinstance(right_expr, Literal):
denom = round_div(1 << (32 + divisor_shift), right_expr.value)
if denom is not None:
return BinaryOp.int(
left=left_expr,
op="/",
right=Literal(denom),
)
return original_expr
def replace_clz_shift(expr: BinaryOp) -> BinaryOp:
# Check that the outer expression is `>>`
if expr.is_floating() or expr.op != ">>":
return expr
# Match `CLZ(x) >> 5`, or return the original expr
left_expr = early_unwrap_ints(expr.left)
right_expr = early_unwrap_ints(expr.right)
if not (
isinstance(left_expr, UnaryOp)
and left_expr.op == "CLZ"
and isinstance(right_expr, Literal)
and right_expr.value == 5
):
return expr
# If the inner `x` is `(a - b)`, return `a == b`
sub_expr = early_unwrap(left_expr.expr)
if (
isinstance(sub_expr, BinaryOp)
and not sub_expr.is_floating()
and sub_expr.op == "-"
):
return BinaryOp.icmp(sub_expr.left, "==", sub_expr.right)
return BinaryOp.icmp(left_expr.expr, "==", Literal(0, type=left_expr.expr.type))
def replace_bitand(expr: BinaryOp) -> Expression:
if not expr.is_floating() and expr.op == "&":
if expr.right == Literal(0xFF):
return as_type(expr.left, Type.int_of_size(8), silent=False)
if expr.right == Literal(0xFFFF):
return as_type(expr.left, Type.int_of_size(16), silent=False)
return expr
def fold_mul_chains(expr: Expression) -> Expression:
def fold(
expr: Expression, toplevel: bool, allow_sll: bool
) -> Tuple[Expression, int]:
if isinstance(expr, BinaryOp):
lbase, lnum = fold(expr.left, False, (expr.op != "<<"))
rbase, rnum = fold(expr.right, False, (expr.op != "<<"))
if expr.op == "<<" and isinstance(expr.right, Literal) and allow_sll:
# Left-shifts by small numbers are easier to understand if
# written as multiplications (they compile to the same thing).
if toplevel and lnum == 1 and not (1 <= expr.right.value <= 4):
return (expr, 1)
return (lbase, lnum << expr.right.value)
if (
expr.op == "*"
and isinstance(expr.right, Literal)
and (allow_sll or expr.right.value % 2 != 0)
):
return (lbase, lnum * expr.right.value)
if early_unwrap(lbase) == early_unwrap(rbase):
if expr.op == "+":
return (lbase, lnum + rnum)
if expr.op == "-":
return (lbase, lnum - rnum)
if isinstance(expr, UnaryOp) and expr.op == "-" and not toplevel:
base, num = fold(expr.expr, False, True)
return (base, -num)
if (
isinstance(expr, EvalOnceExpr)
and not expr.emit_exactly_once
and not expr.forced_emit
):
base, num = fold(early_unwrap(expr), False, allow_sll)
if num != 1 and is_trivial_expression(base):
return (base, num)
return (expr, 1)
base, num = fold(expr, True, True)
if num == 1:
return expr
return BinaryOp.int(left=base, op="*", right=Literal(num))
def array_access_from_add(
expr: Expression,
offset: int,
stack_info: StackInfo,
*,
target_size: Optional[int],
ptr: bool,
) -> Optional[Expression]:
expr = early_unwrap(expr)
if not isinstance(expr, BinaryOp) or expr.op != "+":
return None
base = expr.left
addend = expr.right
if addend.type.is_pointer_or_array() and not base.type.is_pointer_or_array():
base, addend = addend, base
index: Expression
scale: int
uw_addend = early_unwrap(addend)
if (
isinstance(uw_addend, BinaryOp)
and uw_addend.op == "*"
and isinstance(uw_addend.right, Literal)
):
index = uw_addend.left
scale = uw_addend.right.value
elif (
isinstance(uw_addend, BinaryOp)
and uw_addend.op == "<<"
and isinstance(uw_addend.right, Literal)
):
index = uw_addend.left
scale = 1 << uw_addend.right.value
else:
index = addend
scale = 1
if scale < 0:
scale = -scale
index = UnaryOp.sint("-", index)
target_type = base.type.get_pointer_target()
if target_type is None:
return None
uw_base = early_unwrap(base)
typepool = stack_info.global_info.typepool
# In `&x + index * scale`, if the type of `x` is not known, try to mark it as an array.
# Skip the `scale = 1` case because this often indicates a complex `index` expression,
# and is not actually a 1-byte array lookup.
if (
scale > 1
and offset == 0
and isinstance(uw_base, AddressOf)
and target_type.get_size_bytes() is None
):
inner_type: Optional[Type] = None
if (
isinstance(uw_base.expr, GlobalSymbol)
and uw_base.expr.potential_array_dim(scale)[1] != 0
):
# For GlobalSymbols, use the size of the asm data to check the feasibility of being
# an array with `scale`. This helps be more conservative around fake symbols.
pass
elif scale == 2:
# This *could* be a struct, but is much more likely to be an int
inner_type = Type.int_of_size(16)
elif scale == 4:
inner_type = Type.reg32(likely_float=False)
elif typepool.unk_inference and isinstance(uw_base.expr, GlobalSymbol):
# Make up a struct with a tag name based on the symbol & struct size.
# Although `scale = 8` could indicate an array of longs/doubles, it seems more
# common to be an array of structs.
struct_name = f"_struct_{uw_base.expr.symbol_name}_0x{scale:X}"
struct = typepool.get_struct_by_tag_name(
struct_name, stack_info.global_info.typemap
)
if struct is None:
struct = StructDeclaration.unknown(
typepool, size=scale, tag_name=struct_name
)
elif struct.size != scale:
# This should only happen if there was already a struct with this name in the context
raise DecompFailure(f"sizeof(struct {struct_name}) != {scale:#x}")
inner_type = Type.struct(struct)
if inner_type is not None:
# This might fail, if `uw_base.expr.type` can't be changed to an array
uw_base.expr.type.unify(Type.array(inner_type, dim=None))
target_type.unify(inner_type)
if target_type.get_size_bytes() == scale:
pass
else:
sub_path, sub_type, remaining_offset = base.type.get_deref_field(
offset, target_size=scale, exact=False
)
if sub_path is None or len(sub_path) < 2 or sub_path[-1] != 0:
return None
sub_path.pop()
base = StructAccess(
struct_var=base,
offset=offset - remaining_offset,
target_size=None,
field_path=sub_path,
stack_info=stack_info,
type=sub_type,
)
offset = remaining_offset
target_type = sub_type
ret: Expression = ArrayAccess(base, index, type=target_type)
ret_ref = AddressOf(ret, type=ret.type.reference())
field_path, field_type, _ = ret_ref.type.get_deref_field(
offset, target_size=target_size
)
if offset != 0 or (target_size is not None and target_size != scale):
ret = StructAccess(
struct_var=ret_ref,
offset=offset,
target_size=target_size,
field_path=field_path,
stack_info=stack_info,
type=field_type,
)
if ptr:
ret = AddressOf(ret, type=ret.type.reference())
return ret
def handle_add(args: InstrArgs) -> Expression:
lhs = args.reg(1)
rhs = args.reg(2)
stack_info = args.stack_info
type = Type.intptr()
# If they are, treat them the same as pointers anyways.
if lhs.type.is_pointer_or_array():
type = Type.ptr()
elif rhs.type.is_pointer_or_array():
type = Type.ptr()
# addiu instructions can sometimes be emitted as addu instead, when the
# offset is too large.
if isinstance(rhs, Literal):
return handle_addi_real(args.reg_ref(0), args.reg_ref(1), lhs, rhs, stack_info)
if isinstance(lhs, Literal):
return handle_addi_real(args.reg_ref(0), args.reg_ref(2), rhs, lhs, stack_info)
expr = BinaryOp(left=as_intptr(lhs), op="+", right=as_intptr(rhs), type=type)
folded_expr = fold_mul_chains(expr)
if isinstance(folded_expr, BinaryOp):
folded_expr = fold_divmod(folded_expr)
if folded_expr is not expr:
return folded_expr
array_expr = array_access_from_add(expr, 0, stack_info, target_size=None, ptr=True)
if array_expr is not None:
return array_expr
return expr
def handle_add_float(args: InstrArgs) -> Expression:
if args.reg_ref(1) == args.reg_ref(2):
two = Literal(1 << 30, type=Type.f32())
return BinaryOp.f32(two, "*", args.reg(1))
return BinaryOp.f32(args.reg(1), "+", args.reg(2))
def handle_add_double(args: InstrArgs) -> Expression:
if args.reg_ref(1) == args.reg_ref(2):
two = Literal(1 << 62, type=Type.f64())
return BinaryOp.f64(two, "*", args.dreg(1))
return BinaryOp.f64(args.dreg(1), "+", args.dreg(2))
def handle_bgez(args: InstrArgs) -> Condition:
expr = args.reg(0)
uw_expr = early_unwrap(expr)
if (
isinstance(uw_expr, BinaryOp)
and uw_expr.op == "<<"
and isinstance(uw_expr.right, Literal)
):
shift = uw_expr.right.value
bitand = BinaryOp.int(uw_expr.left, "&", Literal(1 << (31 - shift)))
return UnaryOp("!", bitand, type=Type.bool())
return BinaryOp.scmp(expr, ">=", Literal(0))
def rlwi_mask(mask_begin: int, mask_end: int) -> int:
# Compute the mask constant used by the rlwi* family of PPC instructions,
# referred to as the `MASK(MB, ME)` function in the processor manual.
# Bit 0 is the MSB, Bit 31 is the LSB
bits_upto: Callable[[int], int] = lambda m: (1 << (32 - m)) - 1
all_ones = 0xFFFFFFFF
if mask_begin <= mask_end:
# Set bits inside the range, fully inclusive
mask = bits_upto(mask_begin) - bits_upto(mask_end + 1)
else:
# Set bits from [31, mask_end] and [mask_begin, 0]
mask = (bits_upto(mask_end + 1) - bits_upto(mask_begin)) ^ all_ones
return mask
def handle_rlwinm(
source: Expression,
shift: int,
mask_begin: int,
mask_end: int,
simplify: bool = True,
) -> Expression:
# TODO: Detect shift + truncate, like `(x << 2) & 0xFFF3` or `(x >> 2) & 0x3FFF`
# The output of the rlwinm instruction is `ROTL(source, shift) & mask`. We write this as
# ((source << shift) & mask) | ((source >> (32 - shift)) & mask)
# and compute both OR operands (upper_bits and lower_bits respectively).
all_ones = 0xFFFFFFFF
mask = rlwi_mask(mask_begin, mask_end)
left_shift = shift
right_shift = 32 - shift
left_mask = (all_ones << left_shift) & mask
right_mask = (all_ones >> right_shift) & mask
# We only simplify if the `simplify` argument is True, and there will be no `|` in the
# resulting expression. If there is an `|`, the expression is best left as bitwise math
simplify = simplify and not (left_mask and right_mask)
if isinstance(source, Literal):
upper_value = (source.value << left_shift) & mask
lower_value = (source.value >> right_shift) & mask
return Literal(upper_value | lower_value)
upper_bits: Optional[Expression]
if left_mask == 0:
upper_bits = None
else:
upper_bits = source
if left_shift != 0:
upper_bits = BinaryOp.int(
left=upper_bits, op="<<", right=Literal(left_shift)
)
if simplify:
upper_bits = fold_mul_chains(upper_bits)
if left_mask != (all_ones << left_shift) & all_ones:
upper_bits = BinaryOp.int(left=upper_bits, op="&", right=Literal(left_mask))
if simplify:
upper_bits = replace_bitand(upper_bits)
lower_bits: Optional[Expression]
if right_mask == 0:
lower_bits = None
else:
lower_bits = BinaryOp.uint(left=source, op=">>", right=Literal(right_shift))
if simplify:
lower_bits = replace_clz_shift(fold_divmod(lower_bits))
if right_mask != (all_ones >> right_shift) & all_ones:
lower_bits = BinaryOp.int(
left=lower_bits, op="&", right=Literal(right_mask)
)
if simplify:
lower_bits = replace_bitand(lower_bits)
if upper_bits is None and lower_bits is None:
return Literal(0)
elif upper_bits is None:
assert lower_bits is not None
return lower_bits
elif lower_bits is None:
return upper_bits
else:
return BinaryOp.int(left=upper_bits, op="|", right=lower_bits)
def handle_rlwimi(
base: Expression, source: Expression, shift: int, mask_begin: int, mask_end: int
) -> Expression:
# This instruction reads from `base`, replaces some bits with values from `source`, then
# writes the result back into the first register. This can be used to copy any contiguous
# bitfield from `source` into `base`, and is commonly used when manipulating flags, such
# as in `x |= 0x10` or `x &= ~0x10`.
# It's generally more readable to write the mask with `~` (instead of computing the inverse here)
mask_literal = Literal(rlwi_mask(mask_begin, mask_end))
mask = UnaryOp("~", mask_literal, type=Type.u32())
masked_base = BinaryOp.int(left=base, op="&", right=mask)
if source == Literal(0):
return masked_base
inserted = handle_rlwinm(source, shift, mask_begin, mask_end, simplify=False)
if inserted == mask_literal:
return BinaryOp.int(left=base, op="|", right=inserted)
return BinaryOp.int(left=masked_base, op="|", right=inserted)
def handle_loadx(args: InstrArgs, type: Type) -> Expression:
size = type.get_size_bytes()
assert size is not None
ptr = BinaryOp.intptr(left=args.reg(1), op="+", right=args.reg(2))
expr = deref(ptr, args.regs, args.stack_info, size=size)
return as_type(expr, type, silent=True)
def strip_macros(arg: Argument) -> Argument:
if isinstance(arg, Macro):
if arg.macro_name in ["sda2", "sda21"]:
return arg.argument
if arg.macro_name == "hi":
raise DecompFailure("%hi macro outside of lui")
if arg.macro_name not in ["lo", "l"]:
raise DecompFailure(f"Unrecognized linker macro %{arg.macro_name}")
if isinstance(arg.argument, AsmLiteral):
return AsmLiteral(arg.argument.value)
return AsmLiteral(0)
elif isinstance(arg, AsmAddressMode) and isinstance(arg.lhs, Macro):
if arg.lhs.macro_name in ["sda2", "sda21"]:
return arg.lhs.argument
if arg.lhs.macro_name not in ["lo", "l"]:
raise DecompFailure(
f"Bad linker macro in instruction argument {arg}, expected %lo"
)
return AsmAddressMode(lhs=AsmLiteral(0), rhs=arg.rhs)
else:
return arg
@dataclass
class AbiArgSlot:
offset: int
reg: Optional[Register]
type: Type
name: Optional[str] = None
comment: Optional[str] = None
@dataclass
class Abi:
arg_slots: List[AbiArgSlot]
possible_slots: List[AbiArgSlot]
def reg_always_set(node: Node, reg: Register, *, dom_set: bool) -> bool:
if node.immediate_dominator is None:
return False
seen = {node.immediate_dominator}
stack = node.parents[:]
while stack:
n = stack.pop()
if n == node.immediate_dominator and not dom_set:
return False
if n in seen:
continue
seen.add(n)
clobbered: Optional[bool] = None
for instr in n.block.instructions:
with current_instr(instr):
if reg in instr.outputs:
clobbered = False
elif reg in instr.clobbers:
clobbered = True
if clobbered == True:
return False
if clobbered is None:
stack.extend(n.parents)
return True
def pick_phi_assignment_nodes(
reg: Register, nodes: List[Node], expr: Expression
) -> List[Node]:
dominators = sorted(
set.intersection(*(node.dominators for node in nodes)),
key=lambda n: len(n.dominators),
)
for node in dominators:
regs = get_block_info(node).final_register_states
raw = regs.get_raw(reg)
meta = regs.get_meta(reg)
if raw is None or meta is None or meta.force:
continue
if raw == expr:
return [node]
# TODO: In some cases there may be a better solution (e.g. one that requires 2 nodes)
return nodes
def assign_phis(used_phis: List[PhiExpr], stack_info: StackInfo) -> None:
i = 0
# Iterate over used phis until there are no more remaining. New ones may
# appear during iteration, hence the while loop.
while i < len(used_phis):
phi = used_phis[i]
assert phi.num_usages > 0
assert len(phi.node.parents) >= 2
# Group parent nodes by the value of their phi register
equivalent_nodes: DefaultDict[Expression, List[Node]] = defaultdict(list)
for node in phi.node.parents:
expr = get_block_info(node).final_register_states[phi.reg]
expr.type.unify(phi.type)
equivalent_nodes[expr].append(node)
exprs = list(equivalent_nodes.keys())
first_uw = early_unwrap(exprs[0])
if all(early_unwrap(e) == first_uw for e in exprs[1:]):
# All the phis have the same value (e.g. because we recomputed an
# expression after a store, or restored a register after a function
# call). Just use that value instead of introducing a phi node.
# TODO: the unwrapping here is necessary, but also kinda sketchy:
# we may set as replacement_expr an expression that really shouldn't
# prevent_later_uses machinery).
phi.replacement_expr = as_type(first_uw, phi.type, silent=True)
for _ in range(phi.num_usages):
first_uw.use()
else:
for expr, nodes in equivalent_nodes.items():
for node in pick_phi_assignment_nodes(phi.reg, nodes, expr):
block_info = get_block_info(node)
expr = block_info.final_register_states[phi.reg]
if isinstance(expr, PhiExpr):
# Explicitly mark how the expression is used if it's a phi,
expr.use(from_phi=phi)
else:
expr.use()
typed_expr = as_type(expr, phi.type, silent=True)
block_info.to_write.append(SetPhiStmt(phi, typed_expr))
i += 1
name_counter: Dict[Register, int] = {}
for phi in used_phis:
if not phi.replacement_expr and phi.propagates_to() == phi:
counter = name_counter.get(phi.reg, 0) + 1
name_counter[phi.reg] = counter
output_reg_name = stack_info.function.reg_formatter.format(phi.reg)
prefix = f"phi_{output_reg_name}"
phi.name = f"{prefix}_{counter}" if counter > 1 else prefix
stack_info.phi_vars.append(phi)
def propagate_register_meta(nodes: List[Node], reg: Register) -> None:
non_terminal: List[Node] = [n for n in nodes if not isinstance(n, TerminalNode)]
for n in non_terminal:
if reg in get_block_info(n).final_register_states.read_inherited:
for p in n.parents:
par_meta = get_block_info(p).final_register_states.get_meta(reg)
if par_meta:
par_meta.is_read = True
todo = non_terminal[:]
while todo:
n = todo.pop()
meta = get_block_info(n).final_register_states.get_meta(reg)
for p in n.parents:
par_meta = get_block_info(p).final_register_states.get_meta(reg)
if (par_meta and not par_meta.is_read) and (
meta and meta.inherited and meta.is_read
):
par_meta.is_read = True
todo.append(p)
for n in non_terminal:
meta = get_block_info(n).final_register_states.get_meta(reg)
if meta:
if meta.inherited:
meta.uninteresting = True
meta.function_return = True
meta.in_pattern = True
else:
meta.uninteresting |= (
meta.is_read or meta.function_return or meta.in_pattern
)
todo = non_terminal[:]
while todo:
n = todo.pop()
if isinstance(n, TerminalNode):
continue
meta = get_block_info(n).final_register_states.get_meta(reg)
if not meta or not meta.inherited:
continue
all_uninteresting = True
all_function_return = True
all_in_pattern = True
for p in n.parents:
par_meta = get_block_info(p).final_register_states.get_meta(reg)
if par_meta:
all_uninteresting &= par_meta.uninteresting
all_function_return &= par_meta.function_return
all_in_pattern &= par_meta.in_pattern
if meta.uninteresting and not all_uninteresting and not meta.is_read:
meta.uninteresting = False
todo.extend(n.children())
if meta.function_return and not all_function_return:
meta.function_return = False
todo.extend(n.children())
if meta.in_pattern and not all_in_pattern:
meta.in_pattern = False
todo.extend(n.children())
def determine_return_register(
return_blocks: List[BlockInfo], fn_decl_provided: bool, arch: Arch
) -> Optional[Register]:
def priority(block_info: BlockInfo, reg: Register) -> int:
meta = block_info.final_register_states.get_meta(reg)
if not meta:
return 4
if meta.uninteresting:
return 2
if meta.in_pattern:
return 1
if meta.function_return:
return 0
return 3
if not return_blocks:
return None
best_reg: Optional[Register] = None
best_prio = -1
for reg in arch.base_return_regs:
prios = [priority(b, reg) for b in return_blocks]
max_prio = max(prios)
if max_prio == 4:
continue
if max_prio <= 2 and not fn_decl_provided:
continue
if max_prio > best_prio:
best_prio = max_prio
best_reg = reg
return best_reg
def translate_node_body(node: Node, regs: RegInfo, stack_info: StackInfo) -> BlockInfo:
to_write: List[Union[Statement]] = []
local_var_writes: Dict[LocalVar, Tuple[Register, Expression]] = {}
subroutine_args: Dict[int, Expression] = {}
branch_condition: Optional[Condition] = None
switch_expr: Optional[Expression] = None
has_custom_return: bool = False
has_function_call: bool = False
in_pattern: bool = False
arch = stack_info.global_info.arch
def eval_once(
expr: Expression,
*,
emit_exactly_once: bool,
trivial: bool,
prefix: str = "",
reuse_var: Optional[Var] = None,
) -> EvalOnceExpr:
if emit_exactly_once:
expr.use()
elif "_fictive_" in prefix and isinstance(expr, EvalOnceExpr):
return expr
assert reuse_var or prefix
if prefix == "condition_bit":
prefix = "cond"
var = reuse_var or Var(stack_info, "temp_" + prefix)
expr = EvalOnceExpr(
wrapped_expr=expr,
var=var,
type=expr.type,
emit_exactly_once=emit_exactly_once,
trivial=trivial,
)
var.num_usages += 1
stmt = EvalOnceStmt(expr)
to_write.append(stmt)
stack_info.temp_vars.append(stmt)
return expr
def prevent_later_uses(expr_filter: Callable[[Expression], bool]) -> None:
for r in regs.contents.keys():
data = regs.contents.get(r)
assert data is not None
expr = data.value
if not data.meta.force and expr_filter(expr):
# Mark the register as "if used, emit the expression's once
# var". We usually always have a once var at this point,
if not isinstance(expr, EvalOnceExpr):
expr = eval_once(
expr,
emit_exactly_once=False,
trivial=False,
prefix=stack_info.function.reg_formatter.format(r),
)
# This write isn't changing the value of the register; it didn't need
# to be declared as part of the current instruction's inputs/outputs.
regs.unchecked_set_with_meta(r, expr, replace(data.meta, force=True))
def prevent_later_value_uses(sub_expr: Expression) -> None:
# cause them to be incorrectly passed as function arguments -- the function
# call logic sees an opaque wrapper and doesn't realize that they are unused
prevent_later_uses(
lambda e: uses_expr(e, lambda e2: e2 == sub_expr)
and not (isinstance(e, PassedInArg) and not e.copied)
)
def prevent_later_function_calls() -> None:
prevent_later_uses(lambda e: uses_expr(e, lambda e2: isinstance(e2, FuncCall)))
def prevent_later_reads() -> None:
contains_read = lambda e: isinstance(e, (StructAccess, ArrayAccess))
prevent_later_uses(lambda e: uses_expr(e, contains_read))
def set_reg_maybe_return(reg: Register, expr: Expression) -> None:
regs.set_with_meta(reg, expr, RegMeta(in_pattern=in_pattern))
def set_reg(reg: Register, expr: Optional[Expression]) -> Optional[Expression]:
if expr is None:
if reg in regs:
del regs[reg]
return None
if isinstance(expr, LocalVar):
if (
isinstance(node, ReturnNode)
and stack_info.maybe_get_register_var(reg)
and stack_info.in_callee_save_reg_region(expr.value)
and reg in stack_info.callee_save_regs
):
# matter in other cases).
return None
if expr in local_var_writes:
# Elide register restores (only for the same register for now,
# to be conversative).
orig_reg, orig_expr = local_var_writes[expr]
if orig_reg == reg:
expr = orig_expr
uw_expr = expr
if not isinstance(expr, Literal):
expr = eval_once(
expr,
emit_exactly_once=False,
trivial=is_trivial_expression(expr),
prefix=stack_info.function.reg_formatter.format(reg),
)
if reg == Register("zero"):
# Emit the expression as is. It's probably a volatile load.
expr.use()
to_write.append(ExprStmt(expr))
else:
dest = stack_info.maybe_get_register_var(reg)
if dest is not None:
stack_info.use_register_var(dest)
if not (isinstance(uw_expr, RegisterVar) and uw_expr.reg == reg):
source = as_type(expr, dest.type, True)
source.use()
to_write.append(StoreStmt(source=source, dest=dest))
expr = dest
set_reg_maybe_return(reg, expr)
return expr
def clear_caller_save_regs() -> None:
for reg in arch.temp_regs:
if reg in regs:
del regs[reg]
def maybe_clear_local_var_writes(func_args: List[Expression]) -> None:
# Clear the `local_var_writes` dict if any of the `func_args` contain
# a reference to a stack var. (The called function may modify the stack,
# replacing the value we have in `local_var_writes`.)
for arg in func_args:
if uses_expr(
arg,
lambda expr: isinstance(expr, AddressOf)
and isinstance(expr.expr, LocalVar),
):
local_var_writes.clear()
return
def process_instr(instr: Instruction) -> None:
nonlocal branch_condition, switch_expr, has_function_call, in_pattern
in_pattern = instr.in_pattern
mnemonic = instr.mnemonic
arch_mnemonic = instr.arch_mnemonic(arch)
args = InstrArgs(instr.args, regs, stack_info)
expr: Expression
# Figure out what code to generate!
if mnemonic in arch.instrs_ignore:
pass
elif mnemonic in arch.instrs_store or mnemonic in arch.instrs_store_update:
# Store a value in a permanent place.
if mnemonic in arch.instrs_store:
to_store = arch.instrs_store[mnemonic](args)
else:
# PPC specific store-and-update instructions
# `stwu r3, 8(r4)` is equivalent to `$r3 = *($r4 + 8); $r4 += 8;`
to_store = arch.instrs_store_update[mnemonic](args)
# Update the register in the second argument
update = args.memory_ref(1)
if not isinstance(update, AddressMode):
raise DecompFailure(
f"Unhandled store-and-update arg in {instr}: {update!r}"
)
set_reg(
update.rhs,
add_imm(args.regs[update.rhs], Literal(update.offset), stack_info),
)
if to_store is None:
# Elided register preserval.
pass
elif isinstance(to_store.dest, SubroutineArg):
# About to call a subroutine with this argument. Skip arguments for the
# first four stack slots; they are also passed in registers.
if to_store.dest.value >= 0x10:
subroutine_args[to_store.dest.value] = to_store.source
else:
if isinstance(to_store.dest, LocalVar):
stack_info.add_local_var(to_store.dest)
raw_value = to_store.source
if isinstance(raw_value, Cast) and raw_value.reinterpret:
# When preserving values on the stack across function calls,
# ignore the type of the stack variable. The same stack slot
# might be used to preserve values of different types.
raw_value = raw_value.expr
local_var_writes[to_store.dest] = (args.reg_ref(0), raw_value)
# Emit a write. This includes four steps:
# - mark the expression as used (since writes are always emitted)
# - mark the dest used (if it's a struct access it needs to be
# - mark other usages of the dest as "emit before this point if used".
# - emit the actual write.
#
# Note that the prevent_later_value_uses step happens after use(), since
# the stored expression is allowed to reference its destination var,
# but before the write is written, since prevent_later_value_uses might
# emit writes of its own that should go before this write. In practice
# that probably never occurs -- all relevant register contents should be
# EvalOnceExpr's that can be emitted at their point of creation, but
to_store.source.use()
to_store.dest.use()
prevent_later_value_uses(to_store.dest)
prevent_later_function_calls()
to_write.append(to_store)
elif mnemonic in arch.instrs_source_first:
set_reg(args.reg_ref(1), arch.instrs_source_first[mnemonic](args))
elif mnemonic in arch.instrs_branches:
assert branch_condition is None
branch_condition = arch.instrs_branches[mnemonic](args)
elif mnemonic in arch.instrs_float_branches:
assert branch_condition is None
cond_bit = regs[Register("condition_bit")]
if not isinstance(cond_bit, BinaryOp):
cond_bit = ExprCondition(cond_bit, type=cond_bit.type)
if arch_mnemonic == "mips:bc1t":
branch_condition = cond_bit
elif arch_mnemonic == "mips:bc1f":
branch_condition = cond_bit.negated()
elif mnemonic in arch.instrs_jumps:
if arch_mnemonic == "ppc:bctr":
# Switch jump
assert isinstance(node, SwitchNode)
switch_expr = args.regs[Register("ctr")]
elif arch_mnemonic == "mips:jr":
# MIPS:
if args.reg_ref(0) == arch.return_address_reg:
# Return from the function.
assert isinstance(node, ReturnNode)
else:
# Switch jump.
assert isinstance(node, SwitchNode)
switch_expr = args.reg(0)
elif arch_mnemonic == "ppc:blr":
assert isinstance(node, ReturnNode)
else:
assert False, f"Unhandled jump mnemonic {arch_mnemonic}"
elif mnemonic in arch.instrs_fn_call:
if arch_mnemonic in ["mips:jal", "ppc:bl"]:
fn_target = args.imm(0)
if not (
(
isinstance(fn_target, AddressOf)
and isinstance(fn_target.expr, GlobalSymbol)
)
or isinstance(fn_target, Literal)
):
raise DecompFailure(
f"Target of function call must be a symbol, not {fn_target}"
)
elif arch_mnemonic == "ppc:blrl":
fn_target = args.regs[Register("lr")]
elif arch_mnemonic == "ppc:bctrl":
fn_target = args.regs[Register("ctr")]
elif arch_mnemonic == "mips:jalr":
fn_target = args.reg(1)
else:
assert False, f"Unhandled fn call mnemonic {arch_mnemonic}"
fn_target = as_function_ptr(fn_target)
fn_sig = fn_target.type.get_function_pointer_signature()
assert fn_sig is not None, "known function pointers must have a signature"
likely_regs: Dict[Register, bool] = {}
for reg, data in regs.contents.items():
# We use a much stricter filter for PPC than MIPS, because the same
# registers can be used arguments & return values.
# The ABI can also mix & match the rN & fN registers, which makes the
# "require" heuristic less powerful.
#
# - `meta.inherited` will only be False for registers set in *this* basic block
# - `meta.function_return` will only be accurate for registers set within this
# basic block because we have not called `propagate_register_meta` yet.
# Within this block, it will be True for registers that were return values.
if arch.arch == Target.ArchEnum.PPC and (
data.meta.inherited or data.meta.function_return
):
likely_regs[reg] = False
elif data.meta.in_pattern:
# Like `meta.function_return` mentioned above, `meta.in_pattern` will only be
# accurate for registers set within this basic block.
likely_regs[reg] = False
elif isinstance(data.value, PassedInArg) and not data.value.copied:
likely_regs[reg] = False
else:
likely_regs[reg] = True
abi = arch.function_abi(fn_sig, likely_regs, for_call=True)
func_args: List[Expression] = []
for slot in abi.arg_slots:
if slot.reg:
expr = regs[slot.reg]
elif slot.offset in subroutine_args:
expr = subroutine_args.pop(slot.offset)
else:
expr = ErrorExpr(
f"Unable to find stack arg {slot.offset:#x} in block"
)
func_args.append(
CommentExpr.wrap(
as_type(expr, slot.type, True), prefix=slot.comment
)
)
for slot in abi.possible_slots:
assert slot.reg is not None
func_args.append(regs[slot.reg])
# Add the arguments after a3.
# TODO: limit this based on abi.arg_slots. If the function type is known
# and not variadic, this list should be empty.
for _, arg in sorted(subroutine_args.items()):
if fn_sig.params_known and not fn_sig.is_variadic:
func_args.append(CommentExpr.wrap(arg, prefix="extra?"))
else:
func_args.append(arg)
if not fn_sig.params_known:
while len(func_args) > len(fn_sig.params):
fn_sig.params.append(FunctionParam())
# When the function signature isn't provided, the we only assume that each
# Without that assumption, the logic from `function_abi` would be needed here.
for i, (arg_expr, param) in enumerate(zip(func_args, fn_sig.params)):
func_args[i] = as_type(arg_expr, param.type.decay(), True)
# Reset subroutine_args, for the next potential function call.
subroutine_args.clear()
call: Expression = FuncCall(
fn_target, func_args, fn_sig.return_type.weaken_void_ptr()
)
call = eval_once(call, emit_exactly_once=True, trivial=False, prefix="ret")
# Clear out caller-save registers, for clarity and to ensure that
# argument regs don't get passed into the next function.
clear_caller_save_regs()
maybe_clear_local_var_writes(func_args)
# and then this prevention should also be... but it's the best we
prevent_later_function_calls()
prevent_later_reads()
return_reg_vals = arch.function_return(call)
for out in instr.outputs:
if not isinstance(out, Register):
continue
val = return_reg_vals[out]
if not isinstance(val, SecondF64Half):
val = eval_once(
val,
emit_exactly_once=False,
trivial=False,
prefix=stack_info.function.reg_formatter.format(out),
)
regs.set_with_meta(out, val, RegMeta(function_return=True))
has_function_call = True
elif mnemonic in arch.instrs_float_comp:
expr = arch.instrs_float_comp[mnemonic](args)
regs[Register("condition_bit")] = expr
elif mnemonic in arch.instrs_hi_lo:
hi, lo = arch.instrs_hi_lo[mnemonic](args)
set_reg(Register("hi"), hi)
set_reg(Register("lo"), lo)
elif mnemonic in arch.instrs_implicit_destination:
reg, expr_fn = arch.instrs_implicit_destination[mnemonic]
set_reg(reg, expr_fn(args))
elif mnemonic in arch.instrs_ppc_compare:
if instr.args[0] != Register("cr0"):
raise DecompFailure(
f"Instruction {instr} not supported (first arg is not $cr0)"
)
set_reg(Register("cr0_eq"), arch.instrs_ppc_compare[mnemonic](args, "=="))
set_reg(Register("cr0_gt"), arch.instrs_ppc_compare[mnemonic](args, ">"))
set_reg(Register("cr0_lt"), arch.instrs_ppc_compare[mnemonic](args, "<"))
set_reg(Register("cr0_so"), Literal(0))
elif mnemonic in arch.instrs_no_dest:
stmt = arch.instrs_no_dest[mnemonic](args)
to_write.append(stmt)
elif mnemonic.rstrip(".") in arch.instrs_destination_first:
target = args.reg_ref(0)
val = arch.instrs_destination_first[mnemonic.rstrip(".")](args)
target_val = set_reg(target, val)
mn_parts = arch_mnemonic.split(".")
if arch_mnemonic.startswith("ppc:") and arch_mnemonic.endswith("."):
if target_val is None:
target_val = val
set_reg(
Register("cr0_eq"),
BinaryOp.icmp(target_val, "==", Literal(0, type=target_val.type)),
)
target_s32 = Cast(
target_val, reinterpret=True, silent=True, type=Type.s32()
)
set_reg(
Register("cr0_gt"),
BinaryOp(target_s32, ">", Literal(0), type=Type.s32()),
)
set_reg(
Register("cr0_lt"),
BinaryOp(target_s32, "<", Literal(0), type=Type.s32()),
)
set_reg(
Register("cr0_so"),
fn_op("MIPS2C_OVERFLOW", [target_val], type=Type.s32()),
)
elif (
len(mn_parts) >= 2
and mn_parts[0].startswith("mips:")
and mn_parts[1] == "d"
) or arch_mnemonic == "mips:ldc1":
set_reg(target.other_f64_reg(), SecondF64Half())
elif mnemonic in arch.instrs_load_update:
target = args.reg_ref(0)
val = arch.instrs_load_update[mnemonic](args)
set_reg(target, val)
if arch_mnemonic in ["ppc:lwzux", "ppc:lhzux", "ppc:lbzux"]:
# In `rD, rA, rB`, update `rA = rA + rB`
update_reg = args.reg_ref(1)
offset = args.reg(2)
else:
# In `rD, rA(N)`, update `rA = rA + N`
update = args.memory_ref(1)
if not isinstance(update, AddressMode):
raise DecompFailure(
f"Unhandled store-and-update arg in {instr}: {update!r}"
)
update_reg = update.rhs
offset = Literal(update.offset)
if update_reg == target:
raise DecompFailure(
f"Invalid instruction, rA and rD must be different in {instr}"
)
set_reg(update_reg, add_imm(args.regs[update_reg], offset, stack_info))
else:
expr = ErrorExpr(f"unknown instruction: {instr}")
if arch_mnemonic.startswith("ppc:") and arch_mnemonic.endswith("."):
# Unimplemented PPC instructions that modify CR0
set_reg(Register("cr0_eq"), expr)
set_reg(Register("cr0_gt"), expr)
set_reg(Register("cr0_lt"), expr)
set_reg(Register("cr0_so"), expr)
if args.count() >= 1 and isinstance(args.raw_arg(0), Register):
reg = args.reg_ref(0)
expr = eval_once(
expr,
emit_exactly_once=True,
trivial=False,
prefix=stack_info.function.reg_formatter.format(reg),
)
if reg != Register("zero"):
set_reg_maybe_return(reg, expr)
else:
to_write.append(ExprStmt(expr))
for instr in node.block.instructions:
with regs.current_instr(instr):
process_instr(instr)
if branch_condition is not None:
branch_condition.use()
switch_control: Optional[SwitchControl] = None
if switch_expr is not None:
switch_control = SwitchControl.from_expr(switch_expr)
switch_control.control_expr.use()
return BlockInfo(
to_write=to_write,
return_value=None,
switch_control=switch_control,
branch_condition=branch_condition,
final_register_states=regs,
has_function_call=has_function_call,
)
def translate_graph_from_block(
node: Node,
regs: RegInfo,
stack_info: StackInfo,
used_phis: List[PhiExpr],
return_blocks: List[BlockInfo],
options: Options,
) -> None:
if options.debug:
print(f"\nNode in question: {node}")
# Translate the given node and discover final register states.
try:
block_info = translate_node_body(node, regs, stack_info)
if options.debug:
print(block_info)
except Exception as e: # TODO: handle issues better
if options.stop_on_error:
raise
instr: Optional[Instruction] = None
if isinstance(e, InstrProcessingFailure) and isinstance(e.__cause__, Exception):
instr = e.instr
e = e.__cause__
if isinstance(e, DecompFailure):
emsg = str(e)
print(emsg)
else:
tb = e.__traceback__
traceback.print_exception(None, e, tb)
emsg = str(e) or traceback.format_tb(tb)[-1]
emsg = emsg.strip().split("\n")[-1].strip()
error_stmts: List[Statement] = [CommentStmt(f"Error: {emsg}")]
if instr is not None:
print(
f"Error occurred while processing instruction: {instr}", file=sys.stderr
)
error_stmts.append(CommentStmt(f"At instruction: {instr}"))
print(file=sys.stderr)
block_info = BlockInfo(
to_write=error_stmts,
return_value=None,
switch_control=None,
branch_condition=ErrorExpr(),
final_register_states=regs,
has_function_call=False,
)
node.block.add_block_info(block_info)
if isinstance(node, ReturnNode):
return_blocks.append(block_info)
# Translate everything dominated by this node, now that we know our own
# final register state. This will eventually reach every node.
for child in node.immediately_dominates:
if isinstance(child, TerminalNode):
continue
new_regs = RegInfo(stack_info=stack_info)
for reg, data in regs.contents.items():
new_regs.set_with_meta(
reg, data.value, RegMeta(inherited=True, force=data.meta.force)
)
phi_regs = (
r for r in locs_clobbered_until_dominator(child) if isinstance(r, Register)
)
for reg in phi_regs:
if reg_always_set(child, reg, dom_set=(reg in regs)):
expr: Optional[Expression] = stack_info.maybe_get_register_var(reg)
if expr is None:
expr = PhiExpr(
reg=reg, node=child, used_phis=used_phis, type=Type.any_reg()
)
new_regs.set_with_meta(reg, expr, RegMeta(inherited=True))
elif reg in new_regs:
del new_regs[reg]
translate_graph_from_block(
child, new_regs, stack_info, used_phis, return_blocks, options
)
def resolve_types_late(stack_info: StackInfo) -> None:
# Final check over stack var types. Because of delayed type unification, some
# locations should now be marked as "weak".
for location in stack_info.weak_stack_var_types.keys():
stack_info.get_stack_var(location, store=False)
# Use dereferences to determine pointer types
struct_type_map = stack_info.get_struct_type_map()
for var, offset_type_map in struct_type_map.items():
if len(offset_type_map) == 1 and 0 in offset_type_map:
# var was probably a plain pointer, not a struct
# Try to unify it with the appropriate pointer type,
# to fill in the type if it does not already have one
type = offset_type_map[0]
var.type.unify(Type.ptr(type))
@dataclass
class FunctionInfo:
stack_info: StackInfo
flow_graph: FlowGraph
return_type: Type
symbol: GlobalSymbol
@dataclass
class GlobalInfo:
asm_data: AsmData
arch: Arch
target: Target
local_functions: Set[str]
typemap: TypeMap
typepool: TypePool
global_symbol_map: Dict[str, GlobalSymbol] = field(default_factory=dict)
def asm_data_value(self, sym_name: str) -> Optional[AsmDataEntry]:
return self.asm_data.values.get(sym_name)
def address_of_gsym(self, sym_name: str) -> AddressOf:
if sym_name in self.global_symbol_map:
sym = self.global_symbol_map[sym_name]
else:
demangled_symbol: Optional[CxxSymbol] = None
demangled_str: Optional[str] = None
if self.target.language == Target.LanguageEnum.CXX:
try:
demangled_symbol = demangle_codewarrior_parse(sym_name)
except ValueError:
pass
else:
demangled_str = str(demangled_symbol)
sym = self.global_symbol_map[sym_name] = GlobalSymbol(
symbol_name=sym_name,
type=Type.any(),
asm_data_entry=self.asm_data_value(sym_name),
demangled_str=demangled_str,
)
# If the symbol is a C++ vtable, try to build a custom type for it by parsing it
if (
self.target.language == Target.LanguageEnum.CXX
and sym_name.startswith("__vt__")
and sym.asm_data_entry is not None
):
sym.type.unify(self.vtable_type(sym_name, sym.asm_data_entry))
fn = self.typemap.functions.get(sym_name)
ctype: Optional[CType]
if fn is not None:
ctype = fn.type
else:
ctype = self.typemap.var_types.get(sym_name)
if ctype is not None:
sym.symbol_in_context = True
sym.initializer_in_typemap = (
sym_name in self.typemap.vars_with_initializers
)
sym.type.unify(Type.ctype(ctype, self.typemap, self.typepool))
if sym_name not in self.typepool.unknown_decls:
sym.type_provided = True
elif sym_name in self.local_functions:
sym.type.unify(Type.function())
# Do this after unifying the type in the typemap, so that it has lower precedence
if demangled_symbol is not None:
sym.type.unify(
Type.demangled_symbol(self.typemap, self.typepool, demangled_symbol)
)
return AddressOf(sym, type=sym.type.reference())
def vtable_type(self, sym_name: str, asm_data_entry: AsmDataEntry) -> Type:
size = asm_data_entry.size_range_bytes()[1]
struct = StructDeclaration.unknown(
self.typepool, size=size, align=4, tag_name=sym_name
)
offset = 0
for entry in asm_data_entry.data:
if isinstance(entry, bytes):
# MWCC vtables start with a pointer to a typeid struct (or NULL) and an offset
if len(entry) % 4 != 0:
raise DecompFailure(
f"Unable to parse misaligned vtable data in {sym_name}"
)
for i in range(len(entry) // 4):
field_name = f"{struct.new_field_prefix}{offset:X}"
struct.try_add_field(
Type.reg32(likely_float=False), offset, field_name, size=4
)
offset += 4
else:
entry_name = entry
try:
demangled_field_sym = demangle_codewarrior_parse(entry)
if demangled_field_sym.name.qualified_name is not None:
entry_name = str(demangled_field_sym.name.qualified_name[-1])
except ValueError:
pass
field = struct.try_add_field(
self.address_of_gsym(entry).type,
offset,
name=entry_name,
size=4,
)
assert field is not None
field.known = True
offset += 4
return Type.struct(struct)
def is_function_known_void(self, sym_name: str) -> bool:
fn = self.typemap.functions.get(sym_name)
if fn is None:
return False
return fn.ret_type is None
def initializer_for_symbol(
self, sym: GlobalSymbol, fmt: Formatter
) -> Optional[str]:
assert sym.asm_data_entry is not None
data = sym.asm_data_entry.data[:]
def read_uint(n: int) -> Optional[int]:
assert 0 < n <= 8
if not data or not isinstance(data[0], bytes):
return None
if len(data[0]) < n:
return None
bs = data[0][:n]
data[0] = data[0][n:]
if not data[0]:
del data[0]
value = 0
for b in bs:
value = (value << 8) | b
return value
def read_pointer() -> Optional[Expression]:
if not data or not isinstance(data[0], str):
return None
label = data[0]
data.pop(0)
return self.address_of_gsym(label)
def for_type(type: Type) -> Optional[str]:
if type.is_struct() or type.is_array():
struct_fields = type.get_initializer_fields()
if not struct_fields:
return None
members = []
for field in struct_fields:
if isinstance(field, int):
# Check that all padding bytes are 0
for i in range(field):
padding = read_uint(1)
if padding != 0:
return None
else:
m = for_type(field)
if m is None:
return None
members.append(m)
return fmt.format_array(members)
if type.is_reg():
size = type.get_size_bytes()
if not size:
return None
if size == 4:
ptr = read_pointer()
if ptr is not None:
return as_type(ptr, type, silent=True).format(fmt)
value = read_uint(size)
if value is not None:
enum_name = type.get_enum_name(value)
if enum_name is not None:
return enum_name
expr = as_type(Literal(value), type, True)
return elide_casts_for_store(expr).format(fmt)
# Type kinds K_FN and K_VOID do not have initializers
return None
return for_type(sym.type)
def find_forward_declares_needed(self, functions: List[FunctionInfo]) -> Set[str]:
funcs_seen = set()
forward_declares_needed = self.asm_data.mentioned_labels
for func in functions:
funcs_seen.add(func.stack_info.function.name)
for instr in func.stack_info.function.body:
if not isinstance(instr, Instruction):
continue
for arg in instr.args:
if isinstance(arg, AsmGlobalSymbol):
func_name = arg.symbol_name
elif isinstance(arg, Macro) and isinstance(
arg.argument, AsmGlobalSymbol
):
func_name = arg.argument.symbol_name
else:
continue
if func_name in self.local_functions:
if func_name not in funcs_seen:
forward_declares_needed.add(func_name)
return forward_declares_needed
def global_decls(
self,
fmt: Formatter,
decls: Options.GlobalDeclsEnum,
functions: List[FunctionInfo],
) -> str:
# Format labels from symbol_type_map into global declarations.
# As the initializers are formatted, this may cause more symbols
# to be added to the global_symbol_map.
forward_declares_needed = self.find_forward_declares_needed(functions)
lines = []
processed_names: Set[str] = set()
while True:
names: AbstractSet[str] = self.global_symbol_map.keys()
if decls == Options.GlobalDeclsEnum.ALL:
names |= self.asm_data.values.keys()
names -= processed_names
if not names:
break
for name in sorted(names):
processed_names.add(name)
sym = self.address_of_gsym(name).expr
assert isinstance(sym, GlobalSymbol)
data_entry = sym.asm_data_entry
# Is the label defined in this unit (in the active AsmData file(s))
is_in_file = data_entry is not None or name in self.local_functions
# Is the label externally visible (mentioned in the context file)
is_global = sym.symbol_in_context
# Is the label a symbol in .rodata?
is_const = data_entry is not None and data_entry.is_readonly
if data_entry and data_entry.is_jtbl:
# Skip jump tables
continue
if is_in_file and is_global and sym.type.is_function():
# Skip externally-declared functions that are defined here
continue
if self.local_functions == {name}:
# Skip the function being decompiled if just a single one
continue
if not is_in_file and sym.type_provided:
# Skip externally-declared symbols that are defined in other files
continue
# TODO: Use original MIPSFile ordering for variables
sort_order = (
not sym.type.is_function(),
is_global,
is_in_file,
is_const,
name,
)
qualifier = ""
value: Optional[str] = None
comments = []
# Determine type qualifier: static, extern, or neither
if is_in_file and is_global:
qualifier = ""
elif is_in_file:
qualifier = "static"
else:
qualifier = "extern"
if sym.type.is_function():
comments.append(qualifier)
qualifier = ""
# Try to guess if the symbol is an array (and if it is, its dimension) if
# we have a data entry for it, and the symbol is either not in the typemap
# or was a variable-length array there ("VLA", e.g. `int []`)
# (Otherwise, if the dim is provided by the typemap, we trust it.)
element_type, array_dim = sym.type.get_array()
is_vla = element_type is not None and (
array_dim is None or array_dim <= 0
)
if data_entry and (not sym.type_provided or is_vla):
# The size of the data entry is uncertain, because of padding
# between sections. Generally `(max_data_size - data_size) < 16`.
min_data_size, max_data_size = data_entry.size_range_bytes()
# The size of the element type (not the size of the array type)
if element_type is None:
element_type = sym.type
# If we don't know the type, we can't guess the array_dim
type_size = element_type.get_size_bytes()
if type_size:
potential_dim, extra_bytes = sym.potential_array_dim(type_size)
if potential_dim == 0 and extra_bytes > 0:
# The type is too big for our data. (not an array)
comments.append(
f"type too large by {fmt.format_int(type_size - extra_bytes)}"
)
elif potential_dim > 1 or is_vla:
# NB: In general, replacing the types of Expressions can be sketchy.
# However, the GlobalSymbol here came from address_of_gsym(), which
# always returns a reference to the element_type.
array_dim = potential_dim
sym.type = Type.array(element_type, array_dim)
if potential_dim != 0 and extra_bytes > 0:
comments.append(
f"extra bytes: {fmt.format_int(extra_bytes)}"
)
# Try to convert the data from .data/.rodata into an initializer
if data_entry and not data_entry.is_bss:
value = self.initializer_for_symbol(sym, fmt)
if value is None:
# This warning helps distinguish .bss symbols from .data/.rodata,
# IDO only puts symbols in .bss if they don't have any initializer
comments.append("unable to generate initializer")
if is_const:
comments.append("const")
if sym.is_string_constant():
continue
if array_dim is None and sym.type.is_likely_float():
continue
if decls == Options.GlobalDeclsEnum.NONE:
continue
if decls != Options.GlobalDeclsEnum.ALL and sym.initializer_in_typemap:
continue
if (
decls != Options.GlobalDeclsEnum.ALL
and self.target.language == Target.LanguageEnum.CXX
and name.startswith("__vt__")
):
continue
if (
sym.type.is_function()
and decls != Options.GlobalDeclsEnum.ALL
and name in self.local_functions
and name not in forward_declares_needed
):
continue
qualifier = f"{qualifier} " if qualifier else ""
value = f" = {value}" if value else ""
lines.append(
(
sort_order,
fmt.with_comments(
f"{qualifier}{sym.type.to_decl(name, fmt)}{value};",
comments,
)
+ "\n",
)
)
lines.sort()
return "".join(line for _, line in lines)
def narrow_func_call_outputs(
function: Function,
global_info: GlobalInfo,
) -> None:
for instr in function.body:
if (
isinstance(instr, Instruction)
and isinstance(instr.function_target, AsmGlobalSymbol)
and global_info.is_function_known_void(instr.function_target.symbol_name)
):
instr.outputs.clear()
def translate_to_ast(
function: Function,
flow_graph: FlowGraph,
options: Options,
global_info: GlobalInfo,
) -> FunctionInfo:
stack_info = get_stack_info(function, global_info, flow_graph)
start_regs: RegInfo = RegInfo(stack_info=stack_info)
arch = global_info.arch
start_regs[arch.stack_pointer_reg] = GlobalSymbol("sp", type=Type.ptr())
for reg in arch.saved_regs:
start_regs[reg] = stack_info.saved_reg_symbol(reg.register_name)
fn_sym = global_info.address_of_gsym(function.name).expr
assert isinstance(fn_sym, GlobalSymbol)
fn_type = fn_sym.type
fn_type.unify(Type.function())
fn_sig = Type.ptr(fn_type).get_function_pointer_signature()
assert fn_sig is not None, "fn_type is known to be a function"
return_type = fn_sig.return_type
stack_info.is_variadic = fn_sig.is_variadic
def make_arg(offset: int, type: Type) -> PassedInArg:
assert offset % 4 == 0
return PassedInArg(offset, copied=False, stack_info=stack_info, type=type)
abi = arch.function_abi(
fn_sig,
likely_regs={reg: True for reg in arch.argument_regs},
for_call=False,
)
for slot in abi.arg_slots:
stack_info.add_known_param(slot.offset, slot.name, slot.type)
if slot.reg is not None:
start_regs.set_with_meta(
slot.reg, make_arg(slot.offset, slot.type), RegMeta(uninteresting=True)
)
for slot in abi.possible_slots:
if slot.reg is not None:
start_regs.set_with_meta(
slot.reg, make_arg(slot.offset, slot.type), RegMeta(uninteresting=True)
)
if options.reg_vars == ["saved"]:
reg_vars = arch.saved_regs
elif options.reg_vars == ["most"]:
reg_vars = arch.saved_regs + arch.simple_temp_regs
elif options.reg_vars == ["all"]:
reg_vars = arch.saved_regs + arch.simple_temp_regs + arch.argument_regs
else:
reg_vars = [
stack_info.function.reg_formatter.parse(x, arch) for x in options.reg_vars
]
for reg in reg_vars:
reg_name = stack_info.function.reg_formatter.format(reg)
stack_info.add_register_var(reg, reg_name)
if options.debug:
print(stack_info)
print("\nNow, we attempt to translate:")
used_phis: List[PhiExpr] = []
return_blocks: List[BlockInfo] = []
translate_graph_from_block(
flow_graph.entry_node(),
start_regs,
stack_info,
used_phis,
return_blocks,
options,
)
for reg in arch.base_return_regs:
propagate_register_meta(flow_graph.nodes, reg)
return_reg: Optional[Register] = None
if not options.void and not return_type.is_void():
return_reg = determine_return_register(
return_blocks, fn_sym.type_provided, arch
)
if return_reg is not None:
for b in return_blocks:
if return_reg in b.final_register_states:
ret_val = b.final_register_states[return_reg]
ret_val = as_type(ret_val, return_type, True)
ret_val.use()
b.return_value = ret_val
else:
return_type.unify(Type.void())
if not fn_sig.params_known:
while len(fn_sig.params) < len(stack_info.arguments):
fn_sig.params.append(FunctionParam())
for param, arg in zip(fn_sig.params, stack_info.arguments):
param.type.unify(arg.type)
if not param.name:
param.name = arg.format(Formatter())
assign_phis(used_phis, stack_info)
resolve_types_late(stack_info)
if options.pdb_translate:
import pdb
v: Dict[str, object] = {}
fmt = Formatter()
for local in stack_info.local_vars:
var_name = local.format(fmt)
v[var_name] = local
for temp in stack_info.temp_vars:
if temp.need_decl():
var_name = temp.expr.var.format(fmt)
v[var_name] = temp.expr
for phi in stack_info.phi_vars:
assert phi.name is not None
v[phi.name] = phi
pdb.set_trace()
return FunctionInfo(stack_info, flow_graph, return_type, fn_sym)
| true | true |
f725ad7f8cee325b6f43a8b62288b6b7f43cee75 | 2,727 | py | Python | ibis/backends/clickhouse/tests/test_types.py | GrapeBaBa/ibis | 507bb14efdcfd719a0487ee23fe1c85c177517f6 | [
"Apache-2.0"
] | 1 | 2015-11-05T15:40:12.000Z | 2015-11-05T15:40:12.000Z | ibis/backends/clickhouse/tests/test_types.py | GrapeBaBa/ibis | 507bb14efdcfd719a0487ee23fe1c85c177517f6 | [
"Apache-2.0"
] | 7 | 2021-09-02T21:18:10.000Z | 2022-01-31T12:03:40.000Z | ibis/backends/clickhouse/tests/test_types.py | GrapeBaBa/ibis | 507bb14efdcfd719a0487ee23fe1c85c177517f6 | [
"Apache-2.0"
] | null | null | null | import pytest
from pkg_resources import parse_version
import ibis.expr.datatypes as dt
from ibis.backends.clickhouse.client import ClickhouseDataType
def test_column_types(alltypes):
df = alltypes.execute()
assert df.tinyint_col.dtype.name == 'int8'
assert df.smallint_col.dtype.name == 'int16'
assert df.int_col.dtype.name == 'int32'
assert df.bigint_col.dtype.name == 'int64'
assert df.float_col.dtype.name == 'float32'
assert df.double_col.dtype.name == 'float64'
assert df.timestamp_col.dtype.name == 'datetime64[ns]'
def test_columns_types_with_additional_argument(con):
sql_types = ["toFixedString('foo', 8) AS fixedstring_col"]
if parse_version(con.version).base_version >= '1.1.54337':
sql_types.append(
"toDateTime('2018-07-02 00:00:00', 'UTC') AS datetime_col"
)
sql = 'SELECT {}'.format(', '.join(sql_types))
df = con.sql(sql).execute()
assert df.fixedstring_col.dtype.name == 'object'
if parse_version(con.version).base_version >= '1.1.54337':
assert df.datetime_col.dtype.name == 'datetime64[ns]'
@pytest.mark.parametrize(
('ch_type', 'ibis_type'),
[
('Array(Int8)', dt.Array(dt.Int8(nullable=False))),
('Array(Int16)', dt.Array(dt.Int16(nullable=False))),
('Array(Int32)', dt.Array(dt.Int32(nullable=False))),
('Array(Int64)', dt.Array(dt.Int64(nullable=False))),
('Array(UInt8)', dt.Array(dt.UInt8(nullable=False))),
('Array(UInt16)', dt.Array(dt.UInt16(nullable=False))),
('Array(UInt32)', dt.Array(dt.UInt32(nullable=False))),
('Array(UInt64)', dt.Array(dt.UInt64(nullable=False))),
('Array(Float32)', dt.Array(dt.Float32(nullable=False))),
('Array(Float64)', dt.Array(dt.Float64(nullable=False))),
('Array(String)', dt.Array(dt.String(nullable=False))),
('Array(FixedString(32))', dt.Array(dt.String(nullable=False))),
('Array(Date)', dt.Array(dt.Date(nullable=False))),
('Array(DateTime)', dt.Array(dt.Timestamp(nullable=False))),
('Array(DateTime64)', dt.Array(dt.Timestamp(nullable=False))),
('Array(Nothing)', dt.Array(dt.Null(nullable=False))),
('Array(Null)', dt.Array(dt.Null(nullable=False))),
('Array(Array(Int8))', dt.Array(dt.Array(dt.Int8(nullable=False)))),
(
'Array(Array(Array(Int8)))',
dt.Array(dt.Array(dt.Array(dt.Int8(nullable=False)))),
),
(
'Array(Array(Array(Array(Int8))))',
dt.Array(dt.Array(dt.Array(dt.Array(dt.Int8(nullable=False))))),
),
],
)
def test_array_type(ch_type, ibis_type):
assert ClickhouseDataType(ch_type).to_ibis() == ibis_type
| 41.953846 | 76 | 0.635497 | import pytest
from pkg_resources import parse_version
import ibis.expr.datatypes as dt
from ibis.backends.clickhouse.client import ClickhouseDataType
def test_column_types(alltypes):
df = alltypes.execute()
assert df.tinyint_col.dtype.name == 'int8'
assert df.smallint_col.dtype.name == 'int16'
assert df.int_col.dtype.name == 'int32'
assert df.bigint_col.dtype.name == 'int64'
assert df.float_col.dtype.name == 'float32'
assert df.double_col.dtype.name == 'float64'
assert df.timestamp_col.dtype.name == 'datetime64[ns]'
def test_columns_types_with_additional_argument(con):
sql_types = ["toFixedString('foo', 8) AS fixedstring_col"]
if parse_version(con.version).base_version >= '1.1.54337':
sql_types.append(
"toDateTime('2018-07-02 00:00:00', 'UTC') AS datetime_col"
)
sql = 'SELECT {}'.format(', '.join(sql_types))
df = con.sql(sql).execute()
assert df.fixedstring_col.dtype.name == 'object'
if parse_version(con.version).base_version >= '1.1.54337':
assert df.datetime_col.dtype.name == 'datetime64[ns]'
@pytest.mark.parametrize(
('ch_type', 'ibis_type'),
[
('Array(Int8)', dt.Array(dt.Int8(nullable=False))),
('Array(Int16)', dt.Array(dt.Int16(nullable=False))),
('Array(Int32)', dt.Array(dt.Int32(nullable=False))),
('Array(Int64)', dt.Array(dt.Int64(nullable=False))),
('Array(UInt8)', dt.Array(dt.UInt8(nullable=False))),
('Array(UInt16)', dt.Array(dt.UInt16(nullable=False))),
('Array(UInt32)', dt.Array(dt.UInt32(nullable=False))),
('Array(UInt64)', dt.Array(dt.UInt64(nullable=False))),
('Array(Float32)', dt.Array(dt.Float32(nullable=False))),
('Array(Float64)', dt.Array(dt.Float64(nullable=False))),
('Array(String)', dt.Array(dt.String(nullable=False))),
('Array(FixedString(32))', dt.Array(dt.String(nullable=False))),
('Array(Date)', dt.Array(dt.Date(nullable=False))),
('Array(DateTime)', dt.Array(dt.Timestamp(nullable=False))),
('Array(DateTime64)', dt.Array(dt.Timestamp(nullable=False))),
('Array(Nothing)', dt.Array(dt.Null(nullable=False))),
('Array(Null)', dt.Array(dt.Null(nullable=False))),
('Array(Array(Int8))', dt.Array(dt.Array(dt.Int8(nullable=False)))),
(
'Array(Array(Array(Int8)))',
dt.Array(dt.Array(dt.Array(dt.Int8(nullable=False)))),
),
(
'Array(Array(Array(Array(Int8))))',
dt.Array(dt.Array(dt.Array(dt.Array(dt.Int8(nullable=False))))),
),
],
)
def test_array_type(ch_type, ibis_type):
assert ClickhouseDataType(ch_type).to_ibis() == ibis_type
| true | true |
f725adf48debebf7012aabc4bd719b3c1bf15fb2 | 13,130 | py | Python | torchreid/data_manager/cuhk03.py | phoenix1712/deep-person-reid | 8537a9d94b31cbf85e475bd115bb2714086f9be0 | [
"MIT"
] | 11 | 2019-01-10T08:03:31.000Z | 2020-10-23T03:14:23.000Z | torchreid/data_manager/cuhk03.py | xinshengwang/deep-person-reid | 70365320f5319e180d7fce4993003382b06906b0 | [
"MIT"
] | 1 | 2020-11-13T08:20:53.000Z | 2021-01-12T23:06:55.000Z | torchreid/data_manager/cuhk03.py | xinshengwang/deep-person-reid | 70365320f5319e180d7fce4993003382b06906b0 | [
"MIT"
] | 8 | 2019-03-05T09:12:54.000Z | 2022-02-05T06:21:21.000Z | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import re
import sys
import urllib
import tarfile
import zipfile
import os.path as osp
from scipy.io import loadmat
import numpy as np
import h5py
from scipy.misc import imsave
from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json
class CUHK03(object):
"""
CUHK03
Reference:
Li et al. DeepReID: Deep Filter Pairing Neural Network for Person Re-identification. CVPR 2014.
URL: http://www.ee.cuhk.edu.hk/~xgwang/CUHK_identification.html#!
Dataset statistics:
# identities: 1360
# images: 13164
# cameras: 6
# splits: 20 (classic)
Args:
split_id (int): split index (default: 0)
cuhk03_labeled (bool): whether to load labeled images; if false, detected images are loaded (default: False)
"""
dataset_dir = 'cuhk03'
def __init__(self, root='data', split_id=0, cuhk03_labeled=False, cuhk03_classic_split=False, verbose=True, **kwargs):
super(CUHK03, self).__init__()
self.dataset_dir = osp.join(root, self.dataset_dir)
self.data_dir = osp.join(self.dataset_dir, 'cuhk03_release')
self.raw_mat_path = osp.join(self.data_dir, 'cuhk-03.mat')
self.imgs_detected_dir = osp.join(self.dataset_dir, 'images_detected')
self.imgs_labeled_dir = osp.join(self.dataset_dir, 'images_labeled')
self.split_classic_det_json_path = osp.join(self.dataset_dir, 'splits_classic_detected.json')
self.split_classic_lab_json_path = osp.join(self.dataset_dir, 'splits_classic_labeled.json')
self.split_new_det_json_path = osp.join(self.dataset_dir, 'splits_new_detected.json')
self.split_new_lab_json_path = osp.join(self.dataset_dir, 'splits_new_labeled.json')
self.split_new_det_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_detected.mat')
self.split_new_lab_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_labeled.mat')
self._check_before_run()
self._preprocess()
if cuhk03_labeled:
image_type = 'labeled'
split_path = self.split_classic_lab_json_path if cuhk03_classic_split else self.split_new_lab_json_path
else:
image_type = 'detected'
split_path = self.split_classic_det_json_path if cuhk03_classic_split else self.split_new_det_json_path
splits = read_json(split_path)
assert split_id < len(splits), "Condition split_id ({}) < len(splits) ({}) is false".format(split_id, len(splits))
split = splits[split_id]
print("Split index = {}".format(split_id))
train = split['train']
query = split['query']
gallery = split['gallery']
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_total_pids = num_train_pids + num_query_pids
num_train_imgs = split['num_train_imgs']
num_query_imgs = split['num_query_imgs']
num_gallery_imgs = split['num_gallery_imgs']
num_total_imgs = num_train_imgs + num_query_imgs
if verbose:
print("=> CUHK03 ({}) loaded".format(image_type))
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
print(" ------------------------------")
print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
print(" ------------------------------")
self.train = train
self.query = query
self.gallery = gallery
self.num_train_pids = num_train_pids
self.num_query_pids = num_query_pids
self.num_gallery_pids = num_gallery_pids
def _check_before_run(self):
"""Check if all files are available before going deeper"""
if not osp.exists(self.dataset_dir):
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
if not osp.exists(self.data_dir):
raise RuntimeError("'{}' is not available".format(self.data_dir))
if not osp.exists(self.raw_mat_path):
raise RuntimeError("'{}' is not available".format(self.raw_mat_path))
if not osp.exists(self.split_new_det_mat_path):
raise RuntimeError("'{}' is not available".format(self.split_new_det_mat_path))
if not osp.exists(self.split_new_lab_mat_path):
raise RuntimeError("'{}' is not available".format(self.split_new_lab_mat_path))
def _preprocess(self):
"""
This function is a bit complex and ugly, what it does is
1. Extract data from cuhk-03.mat and save as png images.
2. Create 20 classic splits. (Li et al. CVPR'14)
3. Create new split. (Zhong et al. CVPR'17)
"""
print("Note: if root path is changed, the previously generated json files need to be re-generated (delete them first)")
if osp.exists(self.imgs_labeled_dir) and \
osp.exists(self.imgs_detected_dir) and \
osp.exists(self.split_classic_det_json_path) and \
osp.exists(self.split_classic_lab_json_path) and \
osp.exists(self.split_new_det_json_path) and \
osp.exists(self.split_new_lab_json_path):
return
mkdir_if_missing(self.imgs_detected_dir)
mkdir_if_missing(self.imgs_labeled_dir)
print("Extract image data from {} and save as png".format(self.raw_mat_path))
mat = h5py.File(self.raw_mat_path, 'r')
def _deref(ref):
return mat[ref][:].T
def _process_images(img_refs, campid, pid, save_dir):
img_paths = [] # Note: some persons only have images for one view
for imgid, img_ref in enumerate(img_refs):
img = _deref(img_ref)
# skip empty cell
if img.size == 0 or img.ndim < 3: continue
# images are saved with the following format, index-1 (ensure uniqueness)
# campid: index of camera pair (1-5)
# pid: index of person in 'campid'-th camera pair
# viewid: index of view, {1, 2}
# imgid: index of image, (1-10)
viewid = 1 if imgid < 5 else 2
img_name = '{:01d}_{:03d}_{:01d}_{:02d}.png'.format(campid+1, pid+1, viewid, imgid+1)
img_path = osp.join(save_dir, img_name)
imsave(img_path, img)
img_paths.append(img_path)
return img_paths
def _extract_img(name):
print("Processing {} images (extract and save) ...".format(name))
meta_data = []
imgs_dir = self.imgs_detected_dir if name == 'detected' else self.imgs_labeled_dir
for campid, camp_ref in enumerate(mat[name][0]):
camp = _deref(camp_ref)
num_pids = camp.shape[0]
for pid in range(num_pids):
img_paths = _process_images(camp[pid,:], campid, pid, imgs_dir)
assert len(img_paths) > 0, "campid{}-pid{} has no images".format(campid, pid)
meta_data.append((campid+1, pid+1, img_paths))
print("done camera pair {} with {} identities".format(campid+1, num_pids))
return meta_data
meta_detected = _extract_img('detected')
meta_labeled = _extract_img('labeled')
def _extract_classic_split(meta_data, test_split):
train, test = [], []
num_train_pids, num_test_pids = 0, 0
num_train_imgs, num_test_imgs = 0, 0
for i, (campid, pid, img_paths) in enumerate(meta_data):
if [campid, pid] in test_split:
for img_path in img_paths:
camid = int(osp.basename(img_path).split('_')[2])
test.append((img_path, num_test_pids, camid))
num_test_pids += 1
num_test_imgs += len(img_paths)
else:
for img_path in img_paths:
camid = int(osp.basename(img_path).split('_')[2])
train.append((img_path, num_train_pids, camid))
num_train_pids += 1
num_train_imgs += len(img_paths)
return train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs
print("Creating classic splits (# = 20) ...")
splits_classic_det, splits_classic_lab = [], []
for split_ref in mat['testsets'][0]:
test_split = _deref(split_ref).tolist()
# create split for detected images
train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \
_extract_classic_split(meta_detected, test_split)
splits_classic_det.append({
'train': train, 'query': test, 'gallery': test,
'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,
'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,
'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,
})
# create split for labeled images
train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \
_extract_classic_split(meta_labeled, test_split)
splits_classic_lab.append({
'train': train, 'query': test, 'gallery': test,
'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,
'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,
'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,
})
write_json(splits_classic_det, self.split_classic_det_json_path)
write_json(splits_classic_lab, self.split_classic_lab_json_path)
def _extract_set(filelist, pids, pid2label, idxs, img_dir, relabel):
tmp_set = []
unique_pids = set()
for idx in idxs:
img_name = filelist[idx][0]
camid = int(img_name.split('_')[2])
pid = pids[idx]
if relabel: pid = pid2label[pid]
img_path = osp.join(img_dir, img_name)
tmp_set.append((img_path, int(pid), camid))
unique_pids.add(pid)
return tmp_set, len(unique_pids), len(idxs)
def _extract_new_split(split_dict, img_dir):
train_idxs = split_dict['train_idx'].flatten() - 1 # index-0
pids = split_dict['labels'].flatten()
train_pids = set(pids[train_idxs])
pid2label = {pid: label for label, pid in enumerate(train_pids)}
query_idxs = split_dict['query_idx'].flatten() - 1
gallery_idxs = split_dict['gallery_idx'].flatten() - 1
filelist = split_dict['filelist'].flatten()
train_info = _extract_set(filelist, pids, pid2label, train_idxs, img_dir, relabel=True)
query_info = _extract_set(filelist, pids, pid2label, query_idxs, img_dir, relabel=False)
gallery_info = _extract_set(filelist, pids, pid2label, gallery_idxs, img_dir, relabel=False)
return train_info, query_info, gallery_info
print("Creating new splits for detected images (767/700) ...")
train_info, query_info, gallery_info = _extract_new_split(
loadmat(self.split_new_det_mat_path),
self.imgs_detected_dir,
)
splits = [{
'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],
'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],
'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],
'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],
}]
write_json(splits, self.split_new_det_json_path)
print("Creating new splits for labeled images (767/700) ...")
train_info, query_info, gallery_info = _extract_new_split(
loadmat(self.split_new_lab_mat_path),
self.imgs_labeled_dir,
)
splits = [{
'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],
'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],
'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],
'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],
}]
write_json(splits, self.split_new_lab_json_path)
| 46.560284 | 127 | 0.6131 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import re
import sys
import urllib
import tarfile
import zipfile
import os.path as osp
from scipy.io import loadmat
import numpy as np
import h5py
from scipy.misc import imsave
from torchreid.utils.iotools import mkdir_if_missing, write_json, read_json
class CUHK03(object):
dataset_dir = 'cuhk03'
def __init__(self, root='data', split_id=0, cuhk03_labeled=False, cuhk03_classic_split=False, verbose=True, **kwargs):
super(CUHK03, self).__init__()
self.dataset_dir = osp.join(root, self.dataset_dir)
self.data_dir = osp.join(self.dataset_dir, 'cuhk03_release')
self.raw_mat_path = osp.join(self.data_dir, 'cuhk-03.mat')
self.imgs_detected_dir = osp.join(self.dataset_dir, 'images_detected')
self.imgs_labeled_dir = osp.join(self.dataset_dir, 'images_labeled')
self.split_classic_det_json_path = osp.join(self.dataset_dir, 'splits_classic_detected.json')
self.split_classic_lab_json_path = osp.join(self.dataset_dir, 'splits_classic_labeled.json')
self.split_new_det_json_path = osp.join(self.dataset_dir, 'splits_new_detected.json')
self.split_new_lab_json_path = osp.join(self.dataset_dir, 'splits_new_labeled.json')
self.split_new_det_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_detected.mat')
self.split_new_lab_mat_path = osp.join(self.dataset_dir, 'cuhk03_new_protocol_config_labeled.mat')
self._check_before_run()
self._preprocess()
if cuhk03_labeled:
image_type = 'labeled'
split_path = self.split_classic_lab_json_path if cuhk03_classic_split else self.split_new_lab_json_path
else:
image_type = 'detected'
split_path = self.split_classic_det_json_path if cuhk03_classic_split else self.split_new_det_json_path
splits = read_json(split_path)
assert split_id < len(splits), "Condition split_id ({}) < len(splits) ({}) is false".format(split_id, len(splits))
split = splits[split_id]
print("Split index = {}".format(split_id))
train = split['train']
query = split['query']
gallery = split['gallery']
num_train_pids = split['num_train_pids']
num_query_pids = split['num_query_pids']
num_gallery_pids = split['num_gallery_pids']
num_total_pids = num_train_pids + num_query_pids
num_train_imgs = split['num_train_imgs']
num_query_imgs = split['num_query_imgs']
num_gallery_imgs = split['num_gallery_imgs']
num_total_imgs = num_train_imgs + num_query_imgs
if verbose:
print("=> CUHK03 ({}) loaded".format(image_type))
print("Dataset statistics:")
print(" ------------------------------")
print(" subset | # ids | # images")
print(" ------------------------------")
print(" train | {:5d} | {:8d}".format(num_train_pids, num_train_imgs))
print(" query | {:5d} | {:8d}".format(num_query_pids, num_query_imgs))
print(" gallery | {:5d} | {:8d}".format(num_gallery_pids, num_gallery_imgs))
print(" ------------------------------")
print(" total | {:5d} | {:8d}".format(num_total_pids, num_total_imgs))
print(" ------------------------------")
self.train = train
self.query = query
self.gallery = gallery
self.num_train_pids = num_train_pids
self.num_query_pids = num_query_pids
self.num_gallery_pids = num_gallery_pids
def _check_before_run(self):
if not osp.exists(self.dataset_dir):
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
if not osp.exists(self.data_dir):
raise RuntimeError("'{}' is not available".format(self.data_dir))
if not osp.exists(self.raw_mat_path):
raise RuntimeError("'{}' is not available".format(self.raw_mat_path))
if not osp.exists(self.split_new_det_mat_path):
raise RuntimeError("'{}' is not available".format(self.split_new_det_mat_path))
if not osp.exists(self.split_new_lab_mat_path):
raise RuntimeError("'{}' is not available".format(self.split_new_lab_mat_path))
def _preprocess(self):
print("Note: if root path is changed, the previously generated json files need to be re-generated (delete them first)")
if osp.exists(self.imgs_labeled_dir) and \
osp.exists(self.imgs_detected_dir) and \
osp.exists(self.split_classic_det_json_path) and \
osp.exists(self.split_classic_lab_json_path) and \
osp.exists(self.split_new_det_json_path) and \
osp.exists(self.split_new_lab_json_path):
return
mkdir_if_missing(self.imgs_detected_dir)
mkdir_if_missing(self.imgs_labeled_dir)
print("Extract image data from {} and save as png".format(self.raw_mat_path))
mat = h5py.File(self.raw_mat_path, 'r')
def _deref(ref):
return mat[ref][:].T
def _process_images(img_refs, campid, pid, save_dir):
img_paths = []
for imgid, img_ref in enumerate(img_refs):
img = _deref(img_ref)
if img.size == 0 or img.ndim < 3: continue
viewid = 1 if imgid < 5 else 2
img_name = '{:01d}_{:03d}_{:01d}_{:02d}.png'.format(campid+1, pid+1, viewid, imgid+1)
img_path = osp.join(save_dir, img_name)
imsave(img_path, img)
img_paths.append(img_path)
return img_paths
def _extract_img(name):
print("Processing {} images (extract and save) ...".format(name))
meta_data = []
imgs_dir = self.imgs_detected_dir if name == 'detected' else self.imgs_labeled_dir
for campid, camp_ref in enumerate(mat[name][0]):
camp = _deref(camp_ref)
num_pids = camp.shape[0]
for pid in range(num_pids):
img_paths = _process_images(camp[pid,:], campid, pid, imgs_dir)
assert len(img_paths) > 0, "campid{}-pid{} has no images".format(campid, pid)
meta_data.append((campid+1, pid+1, img_paths))
print("done camera pair {} with {} identities".format(campid+1, num_pids))
return meta_data
meta_detected = _extract_img('detected')
meta_labeled = _extract_img('labeled')
def _extract_classic_split(meta_data, test_split):
train, test = [], []
num_train_pids, num_test_pids = 0, 0
num_train_imgs, num_test_imgs = 0, 0
for i, (campid, pid, img_paths) in enumerate(meta_data):
if [campid, pid] in test_split:
for img_path in img_paths:
camid = int(osp.basename(img_path).split('_')[2])
test.append((img_path, num_test_pids, camid))
num_test_pids += 1
num_test_imgs += len(img_paths)
else:
for img_path in img_paths:
camid = int(osp.basename(img_path).split('_')[2])
train.append((img_path, num_train_pids, camid))
num_train_pids += 1
num_train_imgs += len(img_paths)
return train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs
print("Creating classic splits (# = 20) ...")
splits_classic_det, splits_classic_lab = [], []
for split_ref in mat['testsets'][0]:
test_split = _deref(split_ref).tolist()
train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \
_extract_classic_split(meta_detected, test_split)
splits_classic_det.append({
'train': train, 'query': test, 'gallery': test,
'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,
'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,
'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,
})
train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \
_extract_classic_split(meta_labeled, test_split)
splits_classic_lab.append({
'train': train, 'query': test, 'gallery': test,
'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs,
'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs,
'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs,
})
write_json(splits_classic_det, self.split_classic_det_json_path)
write_json(splits_classic_lab, self.split_classic_lab_json_path)
def _extract_set(filelist, pids, pid2label, idxs, img_dir, relabel):
tmp_set = []
unique_pids = set()
for idx in idxs:
img_name = filelist[idx][0]
camid = int(img_name.split('_')[2])
pid = pids[idx]
if relabel: pid = pid2label[pid]
img_path = osp.join(img_dir, img_name)
tmp_set.append((img_path, int(pid), camid))
unique_pids.add(pid)
return tmp_set, len(unique_pids), len(idxs)
def _extract_new_split(split_dict, img_dir):
train_idxs = split_dict['train_idx'].flatten() - 1
pids = split_dict['labels'].flatten()
train_pids = set(pids[train_idxs])
pid2label = {pid: label for label, pid in enumerate(train_pids)}
query_idxs = split_dict['query_idx'].flatten() - 1
gallery_idxs = split_dict['gallery_idx'].flatten() - 1
filelist = split_dict['filelist'].flatten()
train_info = _extract_set(filelist, pids, pid2label, train_idxs, img_dir, relabel=True)
query_info = _extract_set(filelist, pids, pid2label, query_idxs, img_dir, relabel=False)
gallery_info = _extract_set(filelist, pids, pid2label, gallery_idxs, img_dir, relabel=False)
return train_info, query_info, gallery_info
print("Creating new splits for detected images (767/700) ...")
train_info, query_info, gallery_info = _extract_new_split(
loadmat(self.split_new_det_mat_path),
self.imgs_detected_dir,
)
splits = [{
'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],
'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],
'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],
'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],
}]
write_json(splits, self.split_new_det_json_path)
print("Creating new splits for labeled images (767/700) ...")
train_info, query_info, gallery_info = _extract_new_split(
loadmat(self.split_new_lab_mat_path),
self.imgs_labeled_dir,
)
splits = [{
'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0],
'num_train_pids': train_info[1], 'num_train_imgs': train_info[2],
'num_query_pids': query_info[1], 'num_query_imgs': query_info[2],
'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2],
}]
write_json(splits, self.split_new_lab_json_path)
| true | true |
f725ae061cedbfc0818ee7c7320e4bbe82b540ad | 62,902 | py | Python | pandas/core/groupby/generic.py | selasley/pandas | 5b5574520dba1e79ac95e5079724a41151c20b9a | [
"BSD-3-Clause"
] | null | null | null | pandas/core/groupby/generic.py | selasley/pandas | 5b5574520dba1e79ac95e5079724a41151c20b9a | [
"BSD-3-Clause"
] | null | null | null | pandas/core/groupby/generic.py | selasley/pandas | 5b5574520dba1e79ac95e5079724a41151c20b9a | [
"BSD-3-Clause"
] | null | null | null | """
Define the SeriesGroupBy and DataFrameGroupBy
classes that hold the groupby interfaces (and some implementations).
These are user facing as the result of the ``df.groupby(...)`` operations,
which here returns a DataFrameGroupBy object.
"""
from __future__ import annotations
from collections import abc
from functools import partial
from textwrap import dedent
from typing import (
Any,
Callable,
Hashable,
Iterable,
Mapping,
NamedTuple,
Sequence,
TypeVar,
Union,
cast,
)
import warnings
import numpy as np
from pandas._libs import (
Interval,
reduction as libreduction,
)
from pandas._typing import (
ArrayLike,
Manager,
Manager2D,
SingleManager,
)
from pandas.util._decorators import (
Appender,
Substitution,
doc,
)
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.common import (
ensure_int64,
is_bool,
is_categorical_dtype,
is_dict_like,
is_integer_dtype,
is_interval_dtype,
is_scalar,
)
from pandas.core.dtypes.missing import (
isna,
notna,
)
from pandas.core import (
algorithms,
nanops,
)
from pandas.core.apply import (
GroupByApply,
maybe_mangle_lambdas,
reconstruct_func,
validate_func_kwargs,
)
from pandas.core.base import SpecificationError
import pandas.core.common as com
from pandas.core.construction import create_series_with_explicit_dtype
from pandas.core.frame import DataFrame
from pandas.core.generic import NDFrame
from pandas.core.groupby import base
from pandas.core.groupby.groupby import (
GroupBy,
_agg_template,
_apply_docs,
_transform_template,
warn_dropping_nuisance_columns_deprecated,
)
from pandas.core.groupby.grouper import get_grouper
from pandas.core.indexes.api import (
Index,
MultiIndex,
all_indexes_same,
)
from pandas.core.series import Series
from pandas.core.shared_docs import _shared_docs
from pandas.core.util.numba_ import maybe_use_numba
from pandas.plotting import boxplot_frame_groupby
# TODO(typing) the return value on this callable should be any *scalar*.
AggScalar = Union[str, Callable[..., Any]]
# TODO: validate types on ScalarResult and move to _typing
# Blocked from using by https://github.com/python/mypy/issues/1484
# See note at _mangle_lambda_list
ScalarResult = TypeVar("ScalarResult")
class NamedAgg(NamedTuple):
column: Hashable
aggfunc: AggScalar
def generate_property(name: str, klass: type[DataFrame | Series]):
"""
Create a property for a GroupBy subclass to dispatch to DataFrame/Series.
Parameters
----------
name : str
klass : {DataFrame, Series}
Returns
-------
property
"""
def prop(self):
return self._make_wrapper(name)
parent_method = getattr(klass, name)
prop.__doc__ = parent_method.__doc__ or ""
prop.__name__ = name
return property(prop)
def pin_allowlisted_properties(
klass: type[DataFrame | Series], allowlist: frozenset[str]
):
"""
Create GroupBy member defs for DataFrame/Series names in a allowlist.
Parameters
----------
klass : DataFrame or Series class
class where members are defined.
allowlist : frozenset[str]
Set of names of klass methods to be constructed
Returns
-------
class decorator
Notes
-----
Since we don't want to override methods explicitly defined in the
base class, any such name is skipped.
"""
def pinner(cls):
for name in allowlist:
if hasattr(cls, name):
# don't override anything that was explicitly defined
# in the base class
continue
prop = generate_property(name, klass)
setattr(cls, name, prop)
return cls
return pinner
@pin_allowlisted_properties(Series, base.series_apply_allowlist)
class SeriesGroupBy(GroupBy[Series]):
_apply_allowlist = base.series_apply_allowlist
def _wrap_agged_manager(self, mgr: Manager) -> Series:
if mgr.ndim == 1:
mgr = cast(SingleManager, mgr)
single = mgr
else:
mgr = cast(Manager2D, mgr)
single = mgr.iget(0)
ser = self.obj._constructor(single, name=self.obj.name)
# NB: caller is responsible for setting ser.index
return ser
def _get_data_to_aggregate(self) -> SingleManager:
ser = self._obj_with_exclusions
single = ser._mgr
return single
def _iterate_slices(self) -> Iterable[Series]:
yield self._selected_obj
_agg_examples_doc = dedent(
"""
Examples
--------
>>> s = pd.Series([1, 2, 3, 4])
>>> s
0 1
1 2
2 3
3 4
dtype: int64
>>> s.groupby([1, 1, 2, 2]).min()
1 1
2 3
dtype: int64
>>> s.groupby([1, 1, 2, 2]).agg('min')
1 1
2 3
dtype: int64
>>> s.groupby([1, 1, 2, 2]).agg(['min', 'max'])
min max
1 1 2
2 3 4
The output column names can be controlled by passing
the desired column names and aggregations as keyword arguments.
>>> s.groupby([1, 1, 2, 2]).agg(
... minimum='min',
... maximum='max',
... )
minimum maximum
1 1 2
2 3 4
.. versionchanged:: 1.3.0
The resulting dtype will reflect the return value of the aggregating function.
>>> s.groupby([1, 1, 2, 2]).agg(lambda x: x.astype(float).min())
1 1.0
2 3.0
dtype: float64
"""
)
@Appender(
_apply_docs["template"].format(
input="series", examples=_apply_docs["series_examples"]
)
)
def apply(self, func, *args, **kwargs) -> Series:
return super().apply(func, *args, **kwargs)
@doc(_agg_template, examples=_agg_examples_doc, klass="Series")
def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs):
if maybe_use_numba(engine):
with self._group_selection_context():
data = self._selected_obj
result = self._aggregate_with_numba(
data.to_frame(), func, *args, engine_kwargs=engine_kwargs, **kwargs
)
index = self.grouper.result_index
return self.obj._constructor(result.ravel(), index=index, name=data.name)
relabeling = func is None
columns = None
if relabeling:
columns, func = validate_func_kwargs(kwargs)
kwargs = {}
if isinstance(func, str):
return getattr(self, func)(*args, **kwargs)
elif isinstance(func, abc.Iterable):
# Catch instances of lists / tuples
# but not the class list / tuple itself.
func = maybe_mangle_lambdas(func)
ret = self._aggregate_multiple_funcs(func)
if relabeling:
# error: Incompatible types in assignment (expression has type
# "Optional[List[str]]", variable has type "Index")
ret.columns = columns # type: ignore[assignment]
return ret
else:
cyfunc = com.get_cython_func(func)
if cyfunc and not args and not kwargs:
return getattr(self, cyfunc)()
if self.grouper.nkeys > 1:
return self._python_agg_general(func, *args, **kwargs)
try:
return self._python_agg_general(func, *args, **kwargs)
except KeyError:
# TODO: KeyError is raised in _python_agg_general,
# see test_groupby.test_basic
result = self._aggregate_named(func, *args, **kwargs)
# result is a dict whose keys are the elements of result_index
index = self.grouper.result_index
return create_series_with_explicit_dtype(
result, index=index, dtype_if_empty=object
)
agg = aggregate
def _aggregate_multiple_funcs(self, arg) -> DataFrame:
if isinstance(arg, dict):
# show the deprecation, but only if we
# have not shown a higher level one
# GH 15931
raise SpecificationError("nested renamer is not supported")
elif any(isinstance(x, (tuple, list)) for x in arg):
arg = [(x, x) if not isinstance(x, (tuple, list)) else x for x in arg]
# indicated column order
columns = next(zip(*arg))
else:
# list of functions / function names
columns = []
for f in arg:
columns.append(com.get_callable_name(f) or f)
arg = zip(columns, arg)
results: dict[base.OutputKey, DataFrame | Series] = {}
for idx, (name, func) in enumerate(arg):
key = base.OutputKey(label=name, position=idx)
results[key] = self.aggregate(func)
if any(isinstance(x, DataFrame) for x in results.values()):
from pandas import concat
res_df = concat(
results.values(), axis=1, keys=[key.label for key in results.keys()]
)
return res_df
indexed_output = {key.position: val for key, val in results.items()}
output = self.obj._constructor_expanddim(indexed_output, index=None)
output.columns = Index(key.label for key in results)
output = self._reindex_output(output)
return output
def _indexed_output_to_ndframe(
self, output: Mapping[base.OutputKey, ArrayLike]
) -> Series:
"""
Wrap the dict result of a GroupBy aggregation into a Series.
"""
assert len(output) == 1
values = next(iter(output.values()))
result = self.obj._constructor(values)
result.name = self.obj.name
return result
def _wrap_applied_output(
self,
data: Series,
values: list[Any],
not_indexed_same: bool = False,
override_group_keys: bool = False,
) -> DataFrame | Series:
"""
Wrap the output of SeriesGroupBy.apply into the expected result.
Parameters
----------
data : Series
Input data for groupby operation.
values : List[Any]
Applied output for each group.
not_indexed_same : bool, default False
Whether the applied outputs are not indexed the same as the group axes.
Returns
-------
DataFrame or Series
"""
if len(values) == 0:
# GH #6265
return self.obj._constructor(
[],
name=self.obj.name,
index=self.grouper.result_index,
dtype=data.dtype,
)
assert values is not None
if isinstance(values[0], dict):
# GH #823 #24880
index = self.grouper.result_index
res_df = self.obj._constructor_expanddim(values, index=index)
res_df = self._reindex_output(res_df)
# if self.observed is False,
# keep all-NaN rows created while re-indexing
res_ser = res_df.stack(dropna=self.observed)
res_ser.name = self.obj.name
return res_ser
elif isinstance(values[0], (Series, DataFrame)):
result = self._concat_objects(
values,
not_indexed_same=not_indexed_same,
override_group_keys=override_group_keys,
)
result.name = self.obj.name
return result
else:
# GH #6265 #24880
result = self.obj._constructor(
data=values, index=self.grouper.result_index, name=self.obj.name
)
return self._reindex_output(result)
def _aggregate_named(self, func, *args, **kwargs):
# Note: this is very similar to _aggregate_series_pure_python,
# but that does not pin group.name
result = {}
initialized = False
for name, group in self:
object.__setattr__(group, "name", name)
output = func(group, *args, **kwargs)
output = libreduction.extract_result(output)
if not initialized:
# We only do this validation on the first iteration
libreduction.check_result_array(output, group.dtype)
initialized = True
result[name] = output
return result
@Substitution(klass="Series")
@Appender(_transform_template)
def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
return self._transform(
func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
)
def _cython_transform(
self, how: str, numeric_only: bool = True, axis: int = 0, **kwargs
):
assert axis == 0 # handled by caller
obj = self._selected_obj
try:
result = self.grouper._cython_operation(
"transform", obj._values, how, axis, **kwargs
)
except NotImplementedError as err:
raise TypeError(f"{how} is not supported for {obj.dtype} dtype") from err
return obj._constructor(result, index=self.obj.index, name=obj.name)
def _transform_general(self, func: Callable, *args, **kwargs) -> Series:
"""
Transform with a callable func`.
"""
assert callable(func)
klass = type(self.obj)
results = []
for name, group in self:
# this setattr is needed for test_transform_lambda_with_datetimetz
object.__setattr__(group, "name", name)
res = func(group, *args, **kwargs)
results.append(klass(res, index=group.index))
# check for empty "results" to avoid concat ValueError
if results:
from pandas.core.reshape.concat import concat
concatenated = concat(results)
result = self._set_result_index_ordered(concatenated)
else:
result = self.obj._constructor(dtype=np.float64)
result.name = self.obj.name
return result
def filter(self, func, dropna: bool = True, *args, **kwargs):
"""
Return a copy of a Series excluding elements from groups that
do not satisfy the boolean criterion specified by func.
Parameters
----------
func : function
To apply to each group. Should return True or False.
dropna : Drop groups that do not pass the filter. True by default;
if False, groups that evaluate False are filled with NaNs.
Notes
-----
Functions that mutate the passed object can produce unexpected
behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
for more details.
Examples
--------
>>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
... 'foo', 'bar'],
... 'B' : [1, 2, 3, 4, 5, 6],
... 'C' : [2.0, 5., 8., 1., 2., 9.]})
>>> grouped = df.groupby('A')
>>> df.groupby('A').B.filter(lambda x: x.mean() > 3.)
1 2
3 4
5 6
Name: B, dtype: int64
Returns
-------
filtered : Series
"""
if isinstance(func, str):
wrapper = lambda x: getattr(x, func)(*args, **kwargs)
else:
wrapper = lambda x: func(x, *args, **kwargs)
# Interpret np.nan as False.
def true_and_notna(x) -> bool:
b = wrapper(x)
return b and notna(b)
try:
indices = [
self._get_index(name) for name, group in self if true_and_notna(group)
]
except (ValueError, TypeError) as err:
raise TypeError("the filter must return a boolean result") from err
filtered = self._apply_filter(indices, dropna)
return filtered
def nunique(self, dropna: bool = True) -> Series:
"""
Return number of unique elements in the group.
Returns
-------
Series
Number of unique values within each group.
"""
ids, _, _ = self.grouper.group_info
val = self.obj._values
codes, _ = algorithms.factorize(val, sort=False)
sorter = np.lexsort((codes, ids))
codes = codes[sorter]
ids = ids[sorter]
# group boundaries are where group ids change
# unique observations are where sorted values change
idx = np.r_[0, 1 + np.nonzero(ids[1:] != ids[:-1])[0]]
inc = np.r_[1, codes[1:] != codes[:-1]]
# 1st item of each group is a new unique observation
mask = codes == -1
if dropna:
inc[idx] = 1
inc[mask] = 0
else:
inc[mask & np.r_[False, mask[:-1]]] = 0
inc[idx] = 1
out = np.add.reduceat(inc, idx).astype("int64", copy=False)
if len(ids):
# NaN/NaT group exists if the head of ids is -1,
# so remove it from res and exclude its index from idx
if ids[0] == -1:
res = out[1:]
idx = idx[np.flatnonzero(idx)]
else:
res = out
else:
res = out[1:]
ri = self.grouper.result_index
# we might have duplications among the bins
if len(res) != len(ri):
res, out = np.zeros(len(ri), dtype=out.dtype), res
res[ids[idx]] = out
result = self.obj._constructor(res, index=ri, name=self.obj.name)
return self._reindex_output(result, fill_value=0)
@doc(Series.describe)
def describe(self, **kwargs):
return super().describe(**kwargs)
def value_counts(
self,
normalize: bool = False,
sort: bool = True,
ascending: bool = False,
bins=None,
dropna: bool = True,
):
from pandas.core.reshape.merge import get_join_indexers
from pandas.core.reshape.tile import cut
ids, _, _ = self.grouper.group_info
val = self.obj._values
names = self.grouper.names + [self.obj.name]
if is_categorical_dtype(val.dtype) or (
bins is not None and not np.iterable(bins)
):
# scalar bins cannot be done at top level
# in a backward compatible way
# GH38672 relates to categorical dtype
ser = self.apply(
Series.value_counts,
normalize=normalize,
sort=sort,
ascending=ascending,
bins=bins,
)
ser.index.names = names
return ser
# groupby removes null keys from groupings
mask = ids != -1
ids, val = ids[mask], val[mask]
if bins is None:
lab, lev = algorithms.factorize(val, sort=True)
llab = lambda lab, inc: lab[inc]
else:
# lab is a Categorical with categories an IntervalIndex
lab = cut(Series(val), bins, include_lowest=True)
# error: "ndarray" has no attribute "cat"
lev = lab.cat.categories # type: ignore[attr-defined]
# error: No overload variant of "take" of "_ArrayOrScalarCommon" matches
# argument types "Any", "bool", "Union[Any, float]"
lab = lev.take( # type: ignore[call-overload]
# error: "ndarray" has no attribute "cat"
lab.cat.codes, # type: ignore[attr-defined]
allow_fill=True,
# error: Item "ndarray" of "Union[ndarray, Index]" has no attribute
# "_na_value"
fill_value=lev._na_value, # type: ignore[union-attr]
)
llab = lambda lab, inc: lab[inc]._multiindex.codes[-1]
if is_interval_dtype(lab.dtype):
# TODO: should we do this inside II?
lab_interval = cast(Interval, lab)
sorter = np.lexsort((lab_interval.left, lab_interval.right, ids))
else:
sorter = np.lexsort((lab, ids))
ids, lab = ids[sorter], lab[sorter]
# group boundaries are where group ids change
idchanges = 1 + np.nonzero(ids[1:] != ids[:-1])[0]
idx = np.r_[0, idchanges]
if not len(ids):
idx = idchanges
# new values are where sorted labels change
lchanges = llab(lab, slice(1, None)) != llab(lab, slice(None, -1))
inc = np.r_[True, lchanges]
if not len(val):
inc = lchanges
inc[idx] = True # group boundaries are also new values
out = np.diff(np.nonzero(np.r_[inc, True])[0]) # value counts
# num. of times each group should be repeated
rep = partial(np.repeat, repeats=np.add.reduceat(inc, idx))
# multi-index components
codes = self.grouper.reconstructed_codes
codes = [rep(level_codes) for level_codes in codes] + [llab(lab, inc)]
# error: List item 0 has incompatible type "Union[ndarray[Any, Any], Index]";
# expected "Index"
levels = [ping.group_index for ping in self.grouper.groupings] + [
lev # type: ignore[list-item]
]
if dropna:
mask = codes[-1] != -1
if mask.all():
dropna = False
else:
out, codes = out[mask], [level_codes[mask] for level_codes in codes]
if normalize:
out = out.astype("float")
d = np.diff(np.r_[idx, len(ids)])
if dropna:
m = ids[lab == -1]
np.add.at(d, m, -1)
acc = rep(d)[mask]
else:
acc = rep(d)
out /= acc
if sort and bins is None:
cat = ids[inc][mask] if dropna else ids[inc]
sorter = np.lexsort((out if ascending else -out, cat))
out, codes[-1] = out[sorter], codes[-1][sorter]
if bins is not None:
# for compat. with libgroupby.value_counts need to ensure every
# bin is present at every index level, null filled with zeros
diff = np.zeros(len(out), dtype="bool")
for level_codes in codes[:-1]:
diff |= np.r_[True, level_codes[1:] != level_codes[:-1]]
ncat, nbin = diff.sum(), len(levels[-1])
left = [np.repeat(np.arange(ncat), nbin), np.tile(np.arange(nbin), ncat)]
right = [diff.cumsum() - 1, codes[-1]]
_, idx = get_join_indexers(left, right, sort=False, how="left")
out = np.where(idx != -1, out[idx], 0)
if sort:
sorter = np.lexsort((out if ascending else -out, left[0]))
out, left[-1] = out[sorter], left[-1][sorter]
# build the multi-index w/ full levels
def build_codes(lev_codes: np.ndarray) -> np.ndarray:
return np.repeat(lev_codes[diff], nbin)
codes = [build_codes(lev_codes) for lev_codes in codes[:-1]]
codes.append(left[-1])
mi = MultiIndex(levels=levels, codes=codes, names=names, verify_integrity=False)
if is_integer_dtype(out.dtype):
out = ensure_int64(out)
return self.obj._constructor(out, index=mi, name=self.obj.name)
@doc(Series.nlargest)
def nlargest(self, n: int = 5, keep: str = "first"):
f = partial(Series.nlargest, n=n, keep=keep)
data = self._obj_with_exclusions
# Don't change behavior if result index happens to be the same, i.e.
# already ordered and n >= all group sizes.
result = self._python_apply_general(f, data, not_indexed_same=True)
return result
@doc(Series.nsmallest)
def nsmallest(self, n: int = 5, keep: str = "first"):
f = partial(Series.nsmallest, n=n, keep=keep)
data = self._obj_with_exclusions
# Don't change behavior if result index happens to be the same, i.e.
# already ordered and n >= all group sizes.
result = self._python_apply_general(f, data, not_indexed_same=True)
return result
@pin_allowlisted_properties(DataFrame, base.dataframe_apply_allowlist)
class DataFrameGroupBy(GroupBy[DataFrame]):
_apply_allowlist = base.dataframe_apply_allowlist
_agg_examples_doc = dedent(
"""
Examples
--------
>>> df = pd.DataFrame(
... {
... "A": [1, 1, 2, 2],
... "B": [1, 2, 3, 4],
... "C": [0.362838, 0.227877, 1.267767, -0.562860],
... }
... )
>>> df
A B C
0 1 1 0.362838
1 1 2 0.227877
2 2 3 1.267767
3 2 4 -0.562860
The aggregation is for each column.
>>> df.groupby('A').agg('min')
B C
A
1 1 0.227877
2 3 -0.562860
Multiple aggregations
>>> df.groupby('A').agg(['min', 'max'])
B C
min max min max
A
1 1 2 0.227877 0.362838
2 3 4 -0.562860 1.267767
Select a column for aggregation
>>> df.groupby('A').B.agg(['min', 'max'])
min max
A
1 1 2
2 3 4
Different aggregations per column
>>> df.groupby('A').agg({'B': ['min', 'max'], 'C': 'sum'})
B C
min max sum
A
1 1 2 0.590715
2 3 4 0.704907
To control the output names with different aggregations per column,
pandas supports "named aggregation"
>>> df.groupby("A").agg(
... b_min=pd.NamedAgg(column="B", aggfunc="min"),
... c_sum=pd.NamedAgg(column="C", aggfunc="sum"))
b_min c_sum
A
1 1 0.590715
2 3 0.704907
- The keywords are the *output* column names
- The values are tuples whose first element is the column to select
and the second element is the aggregation to apply to that column.
Pandas provides the ``pandas.NamedAgg`` namedtuple with the fields
``['column', 'aggfunc']`` to make it clearer what the arguments are.
As usual, the aggregation can be a callable or a string alias.
See :ref:`groupby.aggregate.named` for more.
.. versionchanged:: 1.3.0
The resulting dtype will reflect the return value of the aggregating function.
>>> df.groupby("A")[["B"]].agg(lambda x: x.astype(float).min())
B
A
1 1.0
2 3.0
"""
)
@doc(_agg_template, examples=_agg_examples_doc, klass="DataFrame")
def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs):
if maybe_use_numba(engine):
with self._group_selection_context():
data = self._selected_obj
result = self._aggregate_with_numba(
data, func, *args, engine_kwargs=engine_kwargs, **kwargs
)
index = self.grouper.result_index
return self.obj._constructor(result, index=index, columns=data.columns)
relabeling, func, columns, order = reconstruct_func(func, **kwargs)
func = maybe_mangle_lambdas(func)
op = GroupByApply(self, func, args, kwargs)
result = op.agg()
if not is_dict_like(func) and result is not None:
return result
elif relabeling and result is not None:
# this should be the only (non-raising) case with relabeling
# used reordered index of columns
result = result.iloc[:, order]
result.columns = columns
if result is None:
# grouper specific aggregations
if self.grouper.nkeys > 1:
# test_groupby_as_index_series_scalar gets here with 'not self.as_index'
return self._python_agg_general(func, *args, **kwargs)
elif args or kwargs:
# test_pass_args_kwargs gets here (with and without as_index)
# can't return early
result = self._aggregate_frame(func, *args, **kwargs)
elif self.axis == 1:
# _aggregate_multiple_funcs does not allow self.axis == 1
# Note: axis == 1 precludes 'not self.as_index', see __init__
result = self._aggregate_frame(func)
return result
else:
# try to treat as if we are passing a list
gba = GroupByApply(self, [func], args=(), kwargs={})
try:
result = gba.agg()
except ValueError as err:
if "no results" not in str(err):
# raised directly by _aggregate_multiple_funcs
raise
result = self._aggregate_frame(func)
else:
sobj = self._selected_obj
if isinstance(sobj, Series):
# GH#35246 test_groupby_as_index_select_column_sum_empty_df
result.columns = self._obj_with_exclusions.columns.copy()
else:
# Retain our column names
result.columns._set_names(
sobj.columns.names, level=list(range(sobj.columns.nlevels))
)
# select everything except for the last level, which is the one
# containing the name of the function(s), see GH#32040
result.columns = result.columns.droplevel(-1)
if not self.as_index:
self._insert_inaxis_grouper_inplace(result)
result.index = Index(range(len(result)))
return result
agg = aggregate
def _iterate_slices(self) -> Iterable[Series]:
obj = self._selected_obj
if self.axis == 1:
obj = obj.T
if isinstance(obj, Series) and obj.name not in self.exclusions:
# Occurs when doing DataFrameGroupBy(...)["X"]
yield obj
else:
for label, values in obj.items():
if label in self.exclusions:
continue
yield values
def _aggregate_frame(self, func, *args, **kwargs) -> DataFrame:
if self.grouper.nkeys != 1:
raise AssertionError("Number of keys must be 1")
obj = self._obj_with_exclusions
result: dict[Hashable, NDFrame | np.ndarray] = {}
if self.axis == 0:
# test_pass_args_kwargs_duplicate_columns gets here with non-unique columns
for name, data in self:
fres = func(data, *args, **kwargs)
result[name] = fres
else:
# we get here in a number of test_multilevel tests
for name in self.indices:
grp_df = self.get_group(name, obj=obj)
fres = func(grp_df, *args, **kwargs)
result[name] = fres
result_index = self.grouper.result_index
other_ax = obj.axes[1 - self.axis]
out = self.obj._constructor(result, index=other_ax, columns=result_index)
if self.axis == 0:
out = out.T
return out
def _aggregate_item_by_item(self, func, *args, **kwargs) -> DataFrame:
# only for axis==0
# tests that get here with non-unique cols:
# test_resample_with_timedelta_yields_no_empty_groups,
# test_resample_apply_product
obj = self._obj_with_exclusions
result: dict[int, NDFrame] = {}
for i, (item, sgb) in enumerate(self._iterate_column_groupbys(obj)):
result[i] = sgb.aggregate(func, *args, **kwargs)
res_df = self.obj._constructor(result)
res_df.columns = obj.columns
return res_df
def _wrap_applied_output(
self,
data: DataFrame,
values: list,
not_indexed_same: bool = False,
override_group_keys: bool = False,
):
if len(values) == 0:
result = self.obj._constructor(
index=self.grouper.result_index, columns=data.columns
)
result = result.astype(data.dtypes, copy=False)
return result
# GH12824
first_not_none = next(com.not_none(*values), None)
if first_not_none is None:
# GH9684 - All values are None, return an empty frame.
return self.obj._constructor()
elif isinstance(first_not_none, DataFrame):
return self._concat_objects(
values,
not_indexed_same=not_indexed_same,
override_group_keys=override_group_keys,
)
key_index = self.grouper.result_index if self.as_index else None
if isinstance(first_not_none, (np.ndarray, Index)):
# GH#1738: values is list of arrays of unequal lengths
# fall through to the outer else clause
# TODO: sure this is right? we used to do this
# after raising AttributeError above
return self.obj._constructor_sliced(
values, index=key_index, name=self._selection
)
elif not isinstance(first_not_none, Series):
# values are not series or array-like but scalars
# self._selection not passed through to Series as the
# result should not take the name of original selection
# of columns
if self.as_index:
return self.obj._constructor_sliced(values, index=key_index)
else:
result = self.obj._constructor(values, columns=[self._selection])
self._insert_inaxis_grouper_inplace(result)
return result
else:
# values are Series
return self._wrap_applied_output_series(
values,
not_indexed_same,
first_not_none,
key_index,
override_group_keys,
)
def _wrap_applied_output_series(
self,
values: list[Series],
not_indexed_same: bool,
first_not_none,
key_index,
override_group_keys: bool,
) -> DataFrame | Series:
# this is to silence a DeprecationWarning
# TODO(2.0): Remove when default dtype of empty Series is object
kwargs = first_not_none._construct_axes_dict()
backup = create_series_with_explicit_dtype(dtype_if_empty=object, **kwargs)
values = [x if (x is not None) else backup for x in values]
all_indexed_same = all_indexes_same(x.index for x in values)
# GH3596
# provide a reduction (Frame -> Series) if groups are
# unique
if self.squeeze:
applied_index = self._selected_obj._get_axis(self.axis)
singular_series = len(values) == 1 and applied_index.nlevels == 1
if singular_series:
# GH2893
# we have series in the values array, we want to
# produce a series:
# if any of the sub-series are not indexed the same
# OR we don't have a multi-index and we have only a
# single values
return self._concat_objects(
values,
not_indexed_same=not_indexed_same,
override_group_keys=override_group_keys,
)
# still a series
# path added as of GH 5545
elif all_indexed_same:
from pandas.core.reshape.concat import concat
return concat(values)
if not all_indexed_same:
# GH 8467
return self._concat_objects(
values,
not_indexed_same=True,
override_group_keys=override_group_keys,
)
# Combine values
# vstack+constructor is faster than concat and handles MI-columns
stacked_values = np.vstack([np.asarray(v) for v in values])
if self.axis == 0:
index = key_index
columns = first_not_none.index.copy()
if columns.name is None:
# GH6124 - propagate name of Series when it's consistent
names = {v.name for v in values}
if len(names) == 1:
columns.name = list(names)[0]
else:
index = first_not_none.index
columns = key_index
stacked_values = stacked_values.T
if stacked_values.dtype == object:
# We'll have the DataFrame constructor do inference
stacked_values = stacked_values.tolist()
result = self.obj._constructor(stacked_values, index=index, columns=columns)
if not self.as_index:
self._insert_inaxis_grouper_inplace(result)
return self._reindex_output(result)
def _cython_transform(
self, how: str, numeric_only: bool = True, axis: int = 0, **kwargs
) -> DataFrame:
assert axis == 0 # handled by caller
# TODO: no tests with self.ndim == 1 for DataFrameGroupBy
# With self.axis == 0, we have multi-block tests
# e.g. test_rank_min_int, test_cython_transform_frame
# test_transform_numeric_ret
# With self.axis == 1, _get_data_to_aggregate does a transpose
# so we always have a single block.
mgr: Manager2D = self._get_data_to_aggregate()
if numeric_only:
mgr = mgr.get_numeric_data(copy=False)
def arr_func(bvalues: ArrayLike) -> ArrayLike:
return self.grouper._cython_operation(
"transform", bvalues, how, 1, **kwargs
)
# We could use `mgr.apply` here and not have to set_axis, but
# we would have to do shape gymnastics for ArrayManager compat
res_mgr = mgr.grouped_reduce(arr_func, ignore_failures=True)
res_mgr.set_axis(1, mgr.axes[1])
if len(res_mgr) < len(mgr):
warn_dropping_nuisance_columns_deprecated(type(self), how)
res_df = self.obj._constructor(res_mgr)
if self.axis == 1:
res_df = res_df.T
return res_df
def _transform_general(self, func, *args, **kwargs):
from pandas.core.reshape.concat import concat
applied = []
obj = self._obj_with_exclusions
gen = self.grouper.get_iterator(obj, axis=self.axis)
fast_path, slow_path = self._define_paths(func, *args, **kwargs)
# Determine whether to use slow or fast path by evaluating on the first group.
# Need to handle the case of an empty generator and process the result so that
# it does not need to be computed again.
try:
name, group = next(gen)
except StopIteration:
pass
else:
object.__setattr__(group, "name", name)
try:
path, res = self._choose_path(fast_path, slow_path, group)
except TypeError:
return self._transform_item_by_item(obj, fast_path)
except ValueError as err:
msg = "transform must return a scalar value for each group"
raise ValueError(msg) from err
if group.size > 0:
res = _wrap_transform_general_frame(self.obj, group, res)
applied.append(res)
# Compute and process with the remaining groups
for name, group in gen:
if group.size == 0:
continue
object.__setattr__(group, "name", name)
res = path(group)
res = _wrap_transform_general_frame(self.obj, group, res)
applied.append(res)
concat_index = obj.columns if self.axis == 0 else obj.index
other_axis = 1 if self.axis == 0 else 0 # switches between 0 & 1
concatenated = concat(applied, axis=self.axis, verify_integrity=False)
concatenated = concatenated.reindex(concat_index, axis=other_axis, copy=False)
return self._set_result_index_ordered(concatenated)
@Substitution(klass="DataFrame")
@Appender(_transform_template)
def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
return self._transform(
func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
)
def _define_paths(self, func, *args, **kwargs):
if isinstance(func, str):
fast_path = lambda group: getattr(group, func)(*args, **kwargs)
slow_path = lambda group: group.apply(
lambda x: getattr(x, func)(*args, **kwargs), axis=self.axis
)
else:
fast_path = lambda group: func(group, *args, **kwargs)
slow_path = lambda group: group.apply(
lambda x: func(x, *args, **kwargs), axis=self.axis
)
return fast_path, slow_path
def _choose_path(self, fast_path: Callable, slow_path: Callable, group: DataFrame):
path = slow_path
res = slow_path(group)
if self.ngroups == 1:
# no need to evaluate multiple paths when only
# a single group exists
return path, res
# if we make it here, test if we can use the fast path
try:
res_fast = fast_path(group)
except AssertionError:
raise # pragma: no cover
except Exception:
# GH#29631 For user-defined function, we can't predict what may be
# raised; see test_transform.test_transform_fastpath_raises
return path, res
# verify fast path returns either:
# a DataFrame with columns equal to group.columns
# OR a Series with index equal to group.columns
if isinstance(res_fast, DataFrame):
if not res_fast.columns.equals(group.columns):
return path, res
elif isinstance(res_fast, Series):
if not res_fast.index.equals(group.columns):
return path, res
else:
return path, res
if res_fast.equals(res):
path = fast_path
return path, res
def _transform_item_by_item(self, obj: DataFrame, wrapper) -> DataFrame:
# iterate through columns, see test_transform_exclude_nuisance
# gets here with non-unique columns
output = {}
inds = []
for i, (colname, sgb) in enumerate(self._iterate_column_groupbys(obj)):
try:
output[i] = sgb.transform(wrapper)
except TypeError:
# e.g. trying to call nanmean with string values
warn_dropping_nuisance_columns_deprecated(type(self), "transform")
else:
inds.append(i)
if not output:
raise TypeError("Transform function invalid for data types")
columns = obj.columns.take(inds)
result = self.obj._constructor(output, index=obj.index)
result.columns = columns
return result
def filter(self, func, dropna=True, *args, **kwargs):
"""
Return a copy of a DataFrame excluding filtered elements.
Elements from groups are filtered if they do not satisfy the
boolean criterion specified by func.
Parameters
----------
func : function
Function to apply to each subframe. Should return True or False.
dropna : Drop groups that do not pass the filter. True by default;
If False, groups that evaluate False are filled with NaNs.
Returns
-------
filtered : DataFrame
Notes
-----
Each subframe is endowed the attribute 'name' in case you need to know
which group you are working on.
Functions that mutate the passed object can produce unexpected
behavior or errors and are not supported. See :ref:`gotchas.udf-mutation`
for more details.
Examples
--------
>>> df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
... 'foo', 'bar'],
... 'B' : [1, 2, 3, 4, 5, 6],
... 'C' : [2.0, 5., 8., 1., 2., 9.]})
>>> grouped = df.groupby('A')
>>> grouped.filter(lambda x: x['B'].mean() > 3.)
A B C
1 bar 2 5.0
3 bar 4 1.0
5 bar 6 9.0
"""
indices = []
obj = self._selected_obj
gen = self.grouper.get_iterator(obj, axis=self.axis)
for name, group in gen:
object.__setattr__(group, "name", name)
res = func(group, *args, **kwargs)
try:
res = res.squeeze()
except AttributeError: # allow e.g., scalars and frames to pass
pass
# interpret the result of the filter
if is_bool(res) or (is_scalar(res) and isna(res)):
if res and notna(res):
indices.append(self._get_index(name))
else:
# non scalars aren't allowed
raise TypeError(
f"filter function returned a {type(res).__name__}, "
"but expected a scalar bool"
)
return self._apply_filter(indices, dropna)
def __getitem__(self, key) -> DataFrameGroupBy | SeriesGroupBy:
if self.axis == 1:
# GH 37725
raise ValueError("Cannot subset columns when using axis=1")
# per GH 23566
if isinstance(key, tuple) and len(key) > 1:
# if len == 1, then it becomes a SeriesGroupBy and this is actually
# valid syntax, so don't raise warning
warnings.warn(
"Indexing with multiple keys (implicitly converted to a tuple "
"of keys) will be deprecated, use a list instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
return super().__getitem__(key)
def _gotitem(self, key, ndim: int, subset=None):
"""
sub-classes to define
return a sliced object
Parameters
----------
key : string / list of selections
ndim : {1, 2}
requested ndim of result
subset : object, default None
subset to act on
"""
if ndim == 2:
if subset is None:
subset = self.obj
return DataFrameGroupBy(
subset,
self.grouper,
axis=self.axis,
level=self.level,
grouper=self.grouper,
exclusions=self.exclusions,
selection=key,
as_index=self.as_index,
sort=self.sort,
group_keys=self.group_keys,
squeeze=self.squeeze,
observed=self.observed,
mutated=self.mutated,
dropna=self.dropna,
)
elif ndim == 1:
if subset is None:
subset = self.obj[key]
return SeriesGroupBy(
subset,
level=self.level,
grouper=self.grouper,
selection=key,
sort=self.sort,
group_keys=self.group_keys,
squeeze=self.squeeze,
observed=self.observed,
dropna=self.dropna,
)
raise AssertionError("invalid ndim for _gotitem")
def _get_data_to_aggregate(self) -> Manager2D:
obj = self._obj_with_exclusions
if self.axis == 1:
return obj.T._mgr
else:
return obj._mgr
def _insert_inaxis_grouper_inplace(self, result: DataFrame) -> None:
# zip in reverse so we can always insert at loc 0
columns = result.columns
for name, lev, in_axis in zip(
reversed(self.grouper.names),
reversed(self.grouper.get_group_levels()),
reversed([grp.in_axis for grp in self.grouper.groupings]),
):
# GH #28549
# When using .apply(-), name will be in columns already
if in_axis and name not in columns:
result.insert(0, name, lev)
def _indexed_output_to_ndframe(
self, output: Mapping[base.OutputKey, ArrayLike]
) -> DataFrame:
"""
Wrap the dict result of a GroupBy aggregation into a DataFrame.
"""
indexed_output = {key.position: val for key, val in output.items()}
columns = Index([key.label for key in output])
columns._set_names(self._obj_with_exclusions._get_axis(1 - self.axis).names)
result = self.obj._constructor(indexed_output)
result.columns = columns
return result
def _wrap_agged_manager(self, mgr: Manager2D) -> DataFrame:
if not self.as_index:
# GH 41998 - empty mgr always gets index of length 0
rows = mgr.shape[1] if mgr.shape[0] > 0 else 0
index = Index(range(rows))
mgr.set_axis(1, index)
result = self.obj._constructor(mgr)
self._insert_inaxis_grouper_inplace(result)
result = result._consolidate()
else:
index = self.grouper.result_index
mgr.set_axis(1, index)
result = self.obj._constructor(mgr)
if self.axis == 1:
result = result.T
# Note: we only need to pass datetime=True in order to get numeric
# values converted
return self._reindex_output(result)._convert(datetime=True)
def _iterate_column_groupbys(self, obj: DataFrame | Series):
for i, colname in enumerate(obj.columns):
yield colname, SeriesGroupBy(
obj.iloc[:, i],
selection=colname,
grouper=self.grouper,
exclusions=self.exclusions,
observed=self.observed,
)
def _apply_to_column_groupbys(self, func, obj: DataFrame | Series) -> DataFrame:
from pandas.core.reshape.concat import concat
columns = obj.columns
results = [
func(col_groupby) for _, col_groupby in self._iterate_column_groupbys(obj)
]
if not len(results):
# concat would raise
return DataFrame([], columns=columns, index=self.grouper.result_index)
else:
return concat(results, keys=columns, axis=1)
def nunique(self, dropna: bool = True) -> DataFrame:
"""
Return DataFrame with counts of unique elements in each position.
Parameters
----------
dropna : bool, default True
Don't include NaN in the counts.
Returns
-------
nunique: DataFrame
Examples
--------
>>> df = pd.DataFrame({'id': ['spam', 'egg', 'egg', 'spam',
... 'ham', 'ham'],
... 'value1': [1, 5, 5, 2, 5, 5],
... 'value2': list('abbaxy')})
>>> df
id value1 value2
0 spam 1 a
1 egg 5 b
2 egg 5 b
3 spam 2 a
4 ham 5 x
5 ham 5 y
>>> df.groupby('id').nunique()
value1 value2
id
egg 1 1
ham 1 2
spam 2 1
Check for rows with the same id but conflicting values:
>>> df.groupby('id').filter(lambda g: (g.nunique() > 1).any())
id value1 value2
0 spam 1 a
3 spam 2 a
4 ham 5 x
5 ham 5 y
"""
if self.axis != 0:
# see test_groupby_crash_on_nunique
return self._python_agg_general(lambda sgb: sgb.nunique(dropna))
obj = self._obj_with_exclusions
results = self._apply_to_column_groupbys(
lambda sgb: sgb.nunique(dropna), obj=obj
)
if not self.as_index:
results.index = Index(range(len(results)))
self._insert_inaxis_grouper_inplace(results)
return results
@doc(
_shared_docs["idxmax"],
numeric_only_default="True for axis=0, False for axis=1",
)
def idxmax(self, axis=0, skipna: bool = True, numeric_only: bool | None = None):
axis = DataFrame._get_axis_number(axis)
if numeric_only is None:
numeric_only = None if axis == 0 else False
def func(df):
# NB: here we use numeric_only=None, in DataFrame it is False GH#38217
res = df._reduce(
nanops.nanargmax,
"argmax",
axis=axis,
skipna=skipna,
numeric_only=numeric_only,
)
indices = res._values
index = df._get_axis(axis)
result = [index[i] if i >= 0 else np.nan for i in indices]
return df._constructor_sliced(result, index=res.index)
func.__name__ = "idxmax"
return self._python_apply_general(func, self._obj_with_exclusions)
@doc(
_shared_docs["idxmin"],
numeric_only_default="True for axis=0, False for axis=1",
)
def idxmin(self, axis=0, skipna: bool = True, numeric_only: bool | None = None):
axis = DataFrame._get_axis_number(axis)
if numeric_only is None:
numeric_only = None if axis == 0 else False
def func(df):
# NB: here we use numeric_only=None, in DataFrame it is False GH#46560
res = df._reduce(
nanops.nanargmin,
"argmin",
axis=axis,
skipna=skipna,
numeric_only=numeric_only,
)
indices = res._values
index = df._get_axis(axis)
result = [index[i] if i >= 0 else np.nan for i in indices]
return df._constructor_sliced(result, index=res.index)
func.__name__ = "idxmin"
return self._python_apply_general(func, self._obj_with_exclusions)
boxplot = boxplot_frame_groupby
def value_counts(
self,
subset: Sequence[Hashable] | None = None,
normalize: bool = False,
sort: bool = True,
ascending: bool = False,
dropna: bool = True,
) -> DataFrame | Series:
"""
Return a Series or DataFrame containing counts of unique rows.
.. versionadded:: 1.4.0
Parameters
----------
subset : list-like, optional
Columns to use when counting unique combinations.
normalize : bool, default False
Return proportions rather than frequencies.
sort : bool, default True
Sort by frequencies.
ascending : bool, default False
Sort in ascending order.
dropna : bool, default True
Don’t include counts of rows that contain NA values.
Returns
-------
Series or DataFrame
Series if the groupby as_index is True, otherwise DataFrame.
See Also
--------
Series.value_counts: Equivalent method on Series.
DataFrame.value_counts: Equivalent method on DataFrame.
SeriesGroupBy.value_counts: Equivalent method on SeriesGroupBy.
Notes
-----
- If the groupby as_index is True then the returned Series will have a
MultiIndex with one level per input column.
- If the groupby as_index is False then the returned DataFrame will have an
additional column with the value_counts. The column is labelled 'count' or
'proportion', depending on the ``normalize`` parameter.
By default, rows that contain any NA values are omitted from
the result.
By default, the result will be in descending order so that the
first element of each group is the most frequently-occurring row.
Examples
--------
>>> df = pd.DataFrame({
... 'gender': ['male', 'male', 'female', 'male', 'female', 'male'],
... 'education': ['low', 'medium', 'high', 'low', 'high', 'low'],
... 'country': ['US', 'FR', 'US', 'FR', 'FR', 'FR']
... })
>>> df
gender education country
0 male low US
1 male medium FR
2 female high US
3 male low FR
4 female high FR
5 male low FR
>>> df.groupby('gender').value_counts()
gender education country
female high FR 1
US 1
male low FR 2
US 1
medium FR 1
dtype: int64
>>> df.groupby('gender').value_counts(ascending=True)
gender education country
female high FR 1
US 1
male low US 1
medium FR 1
low FR 2
dtype: int64
>>> df.groupby('gender').value_counts(normalize=True)
gender education country
female high FR 0.50
US 0.50
male low FR 0.50
US 0.25
medium FR 0.25
dtype: float64
>>> df.groupby('gender', as_index=False).value_counts()
gender education country count
0 female high FR 1
1 female high US 1
2 male low FR 2
3 male low US 1
4 male medium FR 1
>>> df.groupby('gender', as_index=False).value_counts(normalize=True)
gender education country proportion
0 female high FR 0.50
1 female high US 0.50
2 male low FR 0.50
3 male low US 0.25
4 male medium FR 0.25
"""
if self.axis == 1:
raise NotImplementedError(
"DataFrameGroupBy.value_counts only handles axis=0"
)
with self._group_selection_context():
df = self.obj
in_axis_names = {
grouping.name for grouping in self.grouper.groupings if grouping.in_axis
}
if isinstance(self._selected_obj, Series):
name = self._selected_obj.name
keys = [] if name in in_axis_names else [self._selected_obj]
else:
keys = [
# Can't use .values because the column label needs to be preserved
self._selected_obj.iloc[:, idx]
for idx, name in enumerate(self._selected_obj.columns)
if name not in in_axis_names
]
if subset is not None:
clashing = set(subset) & set(in_axis_names)
if clashing:
raise ValueError(
f"Keys {clashing} in subset cannot be in "
"the groupby column keys"
)
groupings = list(self.grouper.groupings)
for key in keys:
grouper, _, _ = get_grouper(
df,
key=key,
axis=self.axis,
sort=self.sort,
dropna=dropna,
)
groupings += list(grouper.groupings)
# Take the size of the overall columns
gb = df.groupby(
groupings,
sort=self.sort,
observed=self.observed,
dropna=self.dropna,
)
result_series = cast(Series, gb.size())
if normalize:
# Normalize the results by dividing by the original group sizes.
# We are guaranteed to have the first N levels be the
# user-requested grouping.
levels = list(
range(len(self.grouper.groupings), result_series.index.nlevels)
)
indexed_group_size = result_series.groupby(
result_series.index.droplevel(levels),
sort=self.sort,
observed=self.observed,
dropna=self.dropna,
).transform("sum")
result_series /= indexed_group_size
if sort:
# Sort the values and then resort by the main grouping
index_level = range(len(self.grouper.groupings))
result_series = result_series.sort_values(
ascending=ascending
).sort_index(level=index_level, sort_remaining=False)
result: Series | DataFrame
if self.as_index:
result = result_series
else:
# Convert to frame
name = "proportion" if normalize else "count"
index = result_series.index
columns = com.fill_missing_names(index.names)
if name in columns:
raise ValueError(
f"Column label '{name}' is duplicate of result column"
)
result_series.name = name
result_series.index = index.set_names(range(len(columns)))
result_frame = result_series.reset_index()
result_frame.columns = columns + [name]
result = result_frame
return result.__finalize__(self.obj, method="value_counts")
def _wrap_transform_general_frame(
obj: DataFrame, group: DataFrame, res: DataFrame | Series
) -> DataFrame:
from pandas import concat
if isinstance(res, Series):
# we need to broadcast across the
# other dimension; this will preserve dtypes
# GH14457
if res.index.is_(obj.index):
res_frame = concat([res] * len(group.columns), axis=1)
res_frame.columns = group.columns
res_frame.index = group.index
else:
res_frame = obj._constructor(
np.tile(res.values, (len(group.index), 1)),
columns=group.columns,
index=group.index,
)
assert isinstance(res_frame, DataFrame)
return res_frame
else:
return res
| 34.204459 | 88 | 0.555833 | from __future__ import annotations
from collections import abc
from functools import partial
from textwrap import dedent
from typing import (
Any,
Callable,
Hashable,
Iterable,
Mapping,
NamedTuple,
Sequence,
TypeVar,
Union,
cast,
)
import warnings
import numpy as np
from pandas._libs import (
Interval,
reduction as libreduction,
)
from pandas._typing import (
ArrayLike,
Manager,
Manager2D,
SingleManager,
)
from pandas.util._decorators import (
Appender,
Substitution,
doc,
)
from pandas.util._exceptions import find_stack_level
from pandas.core.dtypes.common import (
ensure_int64,
is_bool,
is_categorical_dtype,
is_dict_like,
is_integer_dtype,
is_interval_dtype,
is_scalar,
)
from pandas.core.dtypes.missing import (
isna,
notna,
)
from pandas.core import (
algorithms,
nanops,
)
from pandas.core.apply import (
GroupByApply,
maybe_mangle_lambdas,
reconstruct_func,
validate_func_kwargs,
)
from pandas.core.base import SpecificationError
import pandas.core.common as com
from pandas.core.construction import create_series_with_explicit_dtype
from pandas.core.frame import DataFrame
from pandas.core.generic import NDFrame
from pandas.core.groupby import base
from pandas.core.groupby.groupby import (
GroupBy,
_agg_template,
_apply_docs,
_transform_template,
warn_dropping_nuisance_columns_deprecated,
)
from pandas.core.groupby.grouper import get_grouper
from pandas.core.indexes.api import (
Index,
MultiIndex,
all_indexes_same,
)
from pandas.core.series import Series
from pandas.core.shared_docs import _shared_docs
from pandas.core.util.numba_ import maybe_use_numba
from pandas.plotting import boxplot_frame_groupby
AggScalar = Union[str, Callable[..., Any]]
ScalarResult = TypeVar("ScalarResult")
class NamedAgg(NamedTuple):
column: Hashable
aggfunc: AggScalar
def generate_property(name: str, klass: type[DataFrame | Series]):
def prop(self):
return self._make_wrapper(name)
parent_method = getattr(klass, name)
prop.__doc__ = parent_method.__doc__ or ""
prop.__name__ = name
return property(prop)
def pin_allowlisted_properties(
klass: type[DataFrame | Series], allowlist: frozenset[str]
):
def pinner(cls):
for name in allowlist:
if hasattr(cls, name):
# in the base class
continue
prop = generate_property(name, klass)
setattr(cls, name, prop)
return cls
return pinner
@pin_allowlisted_properties(Series, base.series_apply_allowlist)
class SeriesGroupBy(GroupBy[Series]):
_apply_allowlist = base.series_apply_allowlist
def _wrap_agged_manager(self, mgr: Manager) -> Series:
if mgr.ndim == 1:
mgr = cast(SingleManager, mgr)
single = mgr
else:
mgr = cast(Manager2D, mgr)
single = mgr.iget(0)
ser = self.obj._constructor(single, name=self.obj.name)
# NB: caller is responsible for setting ser.index
return ser
def _get_data_to_aggregate(self) -> SingleManager:
ser = self._obj_with_exclusions
single = ser._mgr
return single
def _iterate_slices(self) -> Iterable[Series]:
yield self._selected_obj
_agg_examples_doc = dedent(
"""
Examples
--------
>>> s = pd.Series([1, 2, 3, 4])
>>> s
0 1
1 2
2 3
3 4
dtype: int64
>>> s.groupby([1, 1, 2, 2]).min()
1 1
2 3
dtype: int64
>>> s.groupby([1, 1, 2, 2]).agg('min')
1 1
2 3
dtype: int64
>>> s.groupby([1, 1, 2, 2]).agg(['min', 'max'])
min max
1 1 2
2 3 4
The output column names can be controlled by passing
the desired column names and aggregations as keyword arguments.
>>> s.groupby([1, 1, 2, 2]).agg(
... minimum='min',
... maximum='max',
... )
minimum maximum
1 1 2
2 3 4
.. versionchanged:: 1.3.0
The resulting dtype will reflect the return value of the aggregating function.
>>> s.groupby([1, 1, 2, 2]).agg(lambda x: x.astype(float).min())
1 1.0
2 3.0
dtype: float64
"""
)
@Appender(
_apply_docs["template"].format(
input="series", examples=_apply_docs["series_examples"]
)
)
def apply(self, func, *args, **kwargs) -> Series:
return super().apply(func, *args, **kwargs)
@doc(_agg_template, examples=_agg_examples_doc, klass="Series")
def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs):
if maybe_use_numba(engine):
with self._group_selection_context():
data = self._selected_obj
result = self._aggregate_with_numba(
data.to_frame(), func, *args, engine_kwargs=engine_kwargs, **kwargs
)
index = self.grouper.result_index
return self.obj._constructor(result.ravel(), index=index, name=data.name)
relabeling = func is None
columns = None
if relabeling:
columns, func = validate_func_kwargs(kwargs)
kwargs = {}
if isinstance(func, str):
return getattr(self, func)(*args, **kwargs)
elif isinstance(func, abc.Iterable):
# Catch instances of lists / tuples
# but not the class list / tuple itself.
func = maybe_mangle_lambdas(func)
ret = self._aggregate_multiple_funcs(func)
if relabeling:
# error: Incompatible types in assignment (expression has type
# "Optional[List[str]]", variable has type "Index")
ret.columns = columns # type: ignore[assignment]
return ret
else:
cyfunc = com.get_cython_func(func)
if cyfunc and not args and not kwargs:
return getattr(self, cyfunc)()
if self.grouper.nkeys > 1:
return self._python_agg_general(func, *args, **kwargs)
try:
return self._python_agg_general(func, *args, **kwargs)
except KeyError:
# TODO: KeyError is raised in _python_agg_general,
# see test_groupby.test_basic
result = self._aggregate_named(func, *args, **kwargs)
# result is a dict whose keys are the elements of result_index
index = self.grouper.result_index
return create_series_with_explicit_dtype(
result, index=index, dtype_if_empty=object
)
agg = aggregate
def _aggregate_multiple_funcs(self, arg) -> DataFrame:
if isinstance(arg, dict):
# show the deprecation, but only if we
# have not shown a higher level one
# GH 15931
raise SpecificationError("nested renamer is not supported")
elif any(isinstance(x, (tuple, list)) for x in arg):
arg = [(x, x) if not isinstance(x, (tuple, list)) else x for x in arg]
# indicated column order
columns = next(zip(*arg))
else:
# list of functions / function names
columns = []
for f in arg:
columns.append(com.get_callable_name(f) or f)
arg = zip(columns, arg)
results: dict[base.OutputKey, DataFrame | Series] = {}
for idx, (name, func) in enumerate(arg):
key = base.OutputKey(label=name, position=idx)
results[key] = self.aggregate(func)
if any(isinstance(x, DataFrame) for x in results.values()):
from pandas import concat
res_df = concat(
results.values(), axis=1, keys=[key.label for key in results.keys()]
)
return res_df
indexed_output = {key.position: val for key, val in results.items()}
output = self.obj._constructor_expanddim(indexed_output, index=None)
output.columns = Index(key.label for key in results)
output = self._reindex_output(output)
return output
def _indexed_output_to_ndframe(
self, output: Mapping[base.OutputKey, ArrayLike]
) -> Series:
assert len(output) == 1
values = next(iter(output.values()))
result = self.obj._constructor(values)
result.name = self.obj.name
return result
def _wrap_applied_output(
self,
data: Series,
values: list[Any],
not_indexed_same: bool = False,
override_group_keys: bool = False,
) -> DataFrame | Series:
if len(values) == 0:
# GH #6265
return self.obj._constructor(
[],
name=self.obj.name,
index=self.grouper.result_index,
dtype=data.dtype,
)
assert values is not None
if isinstance(values[0], dict):
# GH #823 #24880
index = self.grouper.result_index
res_df = self.obj._constructor_expanddim(values, index=index)
res_df = self._reindex_output(res_df)
# if self.observed is False,
# keep all-NaN rows created while re-indexing
res_ser = res_df.stack(dropna=self.observed)
res_ser.name = self.obj.name
return res_ser
elif isinstance(values[0], (Series, DataFrame)):
result = self._concat_objects(
values,
not_indexed_same=not_indexed_same,
override_group_keys=override_group_keys,
)
result.name = self.obj.name
return result
else:
# GH #6265 #24880
result = self.obj._constructor(
data=values, index=self.grouper.result_index, name=self.obj.name
)
return self._reindex_output(result)
def _aggregate_named(self, func, *args, **kwargs):
# Note: this is very similar to _aggregate_series_pure_python,
# but that does not pin group.name
result = {}
initialized = False
for name, group in self:
object.__setattr__(group, "name", name)
output = func(group, *args, **kwargs)
output = libreduction.extract_result(output)
if not initialized:
# We only do this validation on the first iteration
libreduction.check_result_array(output, group.dtype)
initialized = True
result[name] = output
return result
@Substitution(klass="Series")
@Appender(_transform_template)
def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
return self._transform(
func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
)
def _cython_transform(
self, how: str, numeric_only: bool = True, axis: int = 0, **kwargs
):
assert axis == 0 # handled by caller
obj = self._selected_obj
try:
result = self.grouper._cython_operation(
"transform", obj._values, how, axis, **kwargs
)
except NotImplementedError as err:
raise TypeError(f"{how} is not supported for {obj.dtype} dtype") from err
return obj._constructor(result, index=self.obj.index, name=obj.name)
def _transform_general(self, func: Callable, *args, **kwargs) -> Series:
assert callable(func)
klass = type(self.obj)
results = []
for name, group in self:
# this setattr is needed for test_transform_lambda_with_datetimetz
object.__setattr__(group, "name", name)
res = func(group, *args, **kwargs)
results.append(klass(res, index=group.index))
# check for empty "results" to avoid concat ValueError
if results:
from pandas.core.reshape.concat import concat
concatenated = concat(results)
result = self._set_result_index_ordered(concatenated)
else:
result = self.obj._constructor(dtype=np.float64)
result.name = self.obj.name
return result
def filter(self, func, dropna: bool = True, *args, **kwargs):
if isinstance(func, str):
wrapper = lambda x: getattr(x, func)(*args, **kwargs)
else:
wrapper = lambda x: func(x, *args, **kwargs)
# Interpret np.nan as False.
def true_and_notna(x) -> bool:
b = wrapper(x)
return b and notna(b)
try:
indices = [
self._get_index(name) for name, group in self if true_and_notna(group)
]
except (ValueError, TypeError) as err:
raise TypeError("the filter must return a boolean result") from err
filtered = self._apply_filter(indices, dropna)
return filtered
def nunique(self, dropna: bool = True) -> Series:
ids, _, _ = self.grouper.group_info
val = self.obj._values
codes, _ = algorithms.factorize(val, sort=False)
sorter = np.lexsort((codes, ids))
codes = codes[sorter]
ids = ids[sorter]
# group boundaries are where group ids change
# unique observations are where sorted values change
idx = np.r_[0, 1 + np.nonzero(ids[1:] != ids[:-1])[0]]
inc = np.r_[1, codes[1:] != codes[:-1]]
# 1st item of each group is a new unique observation
mask = codes == -1
if dropna:
inc[idx] = 1
inc[mask] = 0
else:
inc[mask & np.r_[False, mask[:-1]]] = 0
inc[idx] = 1
out = np.add.reduceat(inc, idx).astype("int64", copy=False)
if len(ids):
# NaN/NaT group exists if the head of ids is -1,
# so remove it from res and exclude its index from idx
if ids[0] == -1:
res = out[1:]
idx = idx[np.flatnonzero(idx)]
else:
res = out
else:
res = out[1:]
ri = self.grouper.result_index
# we might have duplications among the bins
if len(res) != len(ri):
res, out = np.zeros(len(ri), dtype=out.dtype), res
res[ids[idx]] = out
result = self.obj._constructor(res, index=ri, name=self.obj.name)
return self._reindex_output(result, fill_value=0)
@doc(Series.describe)
def describe(self, **kwargs):
return super().describe(**kwargs)
def value_counts(
self,
normalize: bool = False,
sort: bool = True,
ascending: bool = False,
bins=None,
dropna: bool = True,
):
from pandas.core.reshape.merge import get_join_indexers
from pandas.core.reshape.tile import cut
ids, _, _ = self.grouper.group_info
val = self.obj._values
names = self.grouper.names + [self.obj.name]
if is_categorical_dtype(val.dtype) or (
bins is not None and not np.iterable(bins)
):
# scalar bins cannot be done at top level
# in a backward compatible way
# GH38672 relates to categorical dtype
ser = self.apply(
Series.value_counts,
normalize=normalize,
sort=sort,
ascending=ascending,
bins=bins,
)
ser.index.names = names
return ser
# groupby removes null keys from groupings
mask = ids != -1
ids, val = ids[mask], val[mask]
if bins is None:
lab, lev = algorithms.factorize(val, sort=True)
llab = lambda lab, inc: lab[inc]
else:
# lab is a Categorical with categories an IntervalIndex
lab = cut(Series(val), bins, include_lowest=True)
# error: "ndarray" has no attribute "cat"
lev = lab.cat.categories # type: ignore[attr-defined]
# error: No overload variant of "take" of "_ArrayOrScalarCommon" matches
# argument types "Any", "bool", "Union[Any, float]"
lab = lev.take( # type: ignore[call-overload]
# error: "ndarray" has no attribute "cat"
lab.cat.codes, # type: ignore[attr-defined]
allow_fill=True,
# error: Item "ndarray" of "Union[ndarray, Index]" has no attribute
# "_na_value"
fill_value=lev._na_value, # type: ignore[union-attr]
)
llab = lambda lab, inc: lab[inc]._multiindex.codes[-1]
if is_interval_dtype(lab.dtype):
# TODO: should we do this inside II?
lab_interval = cast(Interval, lab)
sorter = np.lexsort((lab_interval.left, lab_interval.right, ids))
else:
sorter = np.lexsort((lab, ids))
ids, lab = ids[sorter], lab[sorter]
# group boundaries are where group ids change
idchanges = 1 + np.nonzero(ids[1:] != ids[:-1])[0]
idx = np.r_[0, idchanges]
if not len(ids):
idx = idchanges
# new values are where sorted labels change
lchanges = llab(lab, slice(1, None)) != llab(lab, slice(None, -1))
inc = np.r_[True, lchanges]
if not len(val):
inc = lchanges
inc[idx] = True # group boundaries are also new values
out = np.diff(np.nonzero(np.r_[inc, True])[0]) # value counts
# num. of times each group should be repeated
rep = partial(np.repeat, repeats=np.add.reduceat(inc, idx))
# multi-index components
codes = self.grouper.reconstructed_codes
codes = [rep(level_codes) for level_codes in codes] + [llab(lab, inc)]
# error: List item 0 has incompatible type "Union[ndarray[Any, Any], Index]";
# expected "Index"
levels = [ping.group_index for ping in self.grouper.groupings] + [
lev # type: ignore[list-item]
]
if dropna:
mask = codes[-1] != -1
if mask.all():
dropna = False
else:
out, codes = out[mask], [level_codes[mask] for level_codes in codes]
if normalize:
out = out.astype("float")
d = np.diff(np.r_[idx, len(ids)])
if dropna:
m = ids[lab == -1]
np.add.at(d, m, -1)
acc = rep(d)[mask]
else:
acc = rep(d)
out /= acc
if sort and bins is None:
cat = ids[inc][mask] if dropna else ids[inc]
sorter = np.lexsort((out if ascending else -out, cat))
out, codes[-1] = out[sorter], codes[-1][sorter]
if bins is not None:
# for compat. with libgroupby.value_counts need to ensure every
# bin is present at every index level, null filled with zeros
diff = np.zeros(len(out), dtype="bool")
for level_codes in codes[:-1]:
diff |= np.r_[True, level_codes[1:] != level_codes[:-1]]
ncat, nbin = diff.sum(), len(levels[-1])
left = [np.repeat(np.arange(ncat), nbin), np.tile(np.arange(nbin), ncat)]
right = [diff.cumsum() - 1, codes[-1]]
_, idx = get_join_indexers(left, right, sort=False, how="left")
out = np.where(idx != -1, out[idx], 0)
if sort:
sorter = np.lexsort((out if ascending else -out, left[0]))
out, left[-1] = out[sorter], left[-1][sorter]
# build the multi-index w/ full levels
def build_codes(lev_codes: np.ndarray) -> np.ndarray:
return np.repeat(lev_codes[diff], nbin)
codes = [build_codes(lev_codes) for lev_codes in codes[:-1]]
codes.append(left[-1])
mi = MultiIndex(levels=levels, codes=codes, names=names, verify_integrity=False)
if is_integer_dtype(out.dtype):
out = ensure_int64(out)
return self.obj._constructor(out, index=mi, name=self.obj.name)
@doc(Series.nlargest)
def nlargest(self, n: int = 5, keep: str = "first"):
f = partial(Series.nlargest, n=n, keep=keep)
data = self._obj_with_exclusions
# Don't change behavior if result index happens to be the same, i.e.
result = self._python_apply_general(f, data, not_indexed_same=True)
return result
@doc(Series.nsmallest)
def nsmallest(self, n: int = 5, keep: str = "first"):
f = partial(Series.nsmallest, n=n, keep=keep)
data = self._obj_with_exclusions
# already ordered and n >= all group sizes.
result = self._python_apply_general(f, data, not_indexed_same=True)
return result
@pin_allowlisted_properties(DataFrame, base.dataframe_apply_allowlist)
class DataFrameGroupBy(GroupBy[DataFrame]):
_apply_allowlist = base.dataframe_apply_allowlist
_agg_examples_doc = dedent(
"""
Examples
--------
>>> df = pd.DataFrame(
... {
... "A": [1, 1, 2, 2],
... "B": [1, 2, 3, 4],
... "C": [0.362838, 0.227877, 1.267767, -0.562860],
... }
... )
>>> df
A B C
0 1 1 0.362838
1 1 2 0.227877
2 2 3 1.267767
3 2 4 -0.562860
The aggregation is for each column.
>>> df.groupby('A').agg('min')
B C
A
1 1 0.227877
2 3 -0.562860
Multiple aggregations
>>> df.groupby('A').agg(['min', 'max'])
B C
min max min max
A
1 1 2 0.227877 0.362838
2 3 4 -0.562860 1.267767
Select a column for aggregation
>>> df.groupby('A').B.agg(['min', 'max'])
min max
A
1 1 2
2 3 4
Different aggregations per column
>>> df.groupby('A').agg({'B': ['min', 'max'], 'C': 'sum'})
B C
min max sum
A
1 1 2 0.590715
2 3 4 0.704907
To control the output names with different aggregations per column,
pandas supports "named aggregation"
>>> df.groupby("A").agg(
... b_min=pd.NamedAgg(column="B", aggfunc="min"),
... c_sum=pd.NamedAgg(column="C", aggfunc="sum"))
b_min c_sum
A
1 1 0.590715
2 3 0.704907
- The keywords are the *output* column names
- The values are tuples whose first element is the column to select
and the second element is the aggregation to apply to that column.
Pandas provides the ``pandas.NamedAgg`` namedtuple with the fields
``['column', 'aggfunc']`` to make it clearer what the arguments are.
As usual, the aggregation can be a callable or a string alias.
See :ref:`groupby.aggregate.named` for more.
.. versionchanged:: 1.3.0
The resulting dtype will reflect the return value of the aggregating function.
>>> df.groupby("A")[["B"]].agg(lambda x: x.astype(float).min())
B
A
1 1.0
2 3.0
"""
)
@doc(_agg_template, examples=_agg_examples_doc, klass="DataFrame")
def aggregate(self, func=None, *args, engine=None, engine_kwargs=None, **kwargs):
if maybe_use_numba(engine):
with self._group_selection_context():
data = self._selected_obj
result = self._aggregate_with_numba(
data, func, *args, engine_kwargs=engine_kwargs, **kwargs
)
index = self.grouper.result_index
return self.obj._constructor(result, index=index, columns=data.columns)
relabeling, func, columns, order = reconstruct_func(func, **kwargs)
func = maybe_mangle_lambdas(func)
op = GroupByApply(self, func, args, kwargs)
result = op.agg()
if not is_dict_like(func) and result is not None:
return result
elif relabeling and result is not None:
# this should be the only (non-raising) case with relabeling
# used reordered index of columns
result = result.iloc[:, order]
result.columns = columns
if result is None:
# grouper specific aggregations
if self.grouper.nkeys > 1:
# test_groupby_as_index_series_scalar gets here with 'not self.as_index'
return self._python_agg_general(func, *args, **kwargs)
elif args or kwargs:
# test_pass_args_kwargs gets here (with and without as_index)
# can't return early
result = self._aggregate_frame(func, *args, **kwargs)
elif self.axis == 1:
result = self._aggregate_frame(func)
return result
else:
gba = GroupByApply(self, [func], args=(), kwargs={})
try:
result = gba.agg()
except ValueError as err:
if "no results" not in str(err):
raise
result = self._aggregate_frame(func)
else:
sobj = self._selected_obj
if isinstance(sobj, Series):
h_exclusions.columns.copy()
else:
result.columns._set_names(
sobj.columns.names, level=list(range(sobj.columns.nlevels))
)
result.columns = result.columns.droplevel(-1)
if not self.as_index:
self._insert_inaxis_grouper_inplace(result)
result.index = Index(range(len(result)))
return result
agg = aggregate
def _iterate_slices(self) -> Iterable[Series]:
obj = self._selected_obj
if self.axis == 1:
obj = obj.T
if isinstance(obj, Series) and obj.name not in self.exclusions:
yield obj
else:
for label, values in obj.items():
if label in self.exclusions:
continue
yield values
def _aggregate_frame(self, func, *args, **kwargs) -> DataFrame:
if self.grouper.nkeys != 1:
raise AssertionError("Number of keys must be 1")
obj = self._obj_with_exclusions
result: dict[Hashable, NDFrame | np.ndarray] = {}
if self.axis == 0:
for name, data in self:
fres = func(data, *args, **kwargs)
result[name] = fres
else:
for name in self.indices:
grp_df = self.get_group(name, obj=obj)
fres = func(grp_df, *args, **kwargs)
result[name] = fres
result_index = self.grouper.result_index
other_ax = obj.axes[1 - self.axis]
out = self.obj._constructor(result, index=other_ax, columns=result_index)
if self.axis == 0:
out = out.T
return out
def _aggregate_item_by_item(self, func, *args, **kwargs) -> DataFrame:
obj = self._obj_with_exclusions
result: dict[int, NDFrame] = {}
for i, (item, sgb) in enumerate(self._iterate_column_groupbys(obj)):
result[i] = sgb.aggregate(func, *args, **kwargs)
res_df = self.obj._constructor(result)
res_df.columns = obj.columns
return res_df
def _wrap_applied_output(
self,
data: DataFrame,
values: list,
not_indexed_same: bool = False,
override_group_keys: bool = False,
):
if len(values) == 0:
result = self.obj._constructor(
index=self.grouper.result_index, columns=data.columns
)
result = result.astype(data.dtypes, copy=False)
return result
first_not_none = next(com.not_none(*values), None)
if first_not_none is None:
return self.obj._constructor()
elif isinstance(first_not_none, DataFrame):
return self._concat_objects(
values,
not_indexed_same=not_indexed_same,
override_group_keys=override_group_keys,
)
key_index = self.grouper.result_index if self.as_index else None
if isinstance(first_not_none, (np.ndarray, Index)):
return self.obj._constructor_sliced(
values, index=key_index, name=self._selection
)
elif not isinstance(first_not_none, Series):
if self.as_index:
return self.obj._constructor_sliced(values, index=key_index)
else:
result = self.obj._constructor(values, columns=[self._selection])
self._insert_inaxis_grouper_inplace(result)
return result
else:
return self._wrap_applied_output_series(
values,
not_indexed_same,
first_not_none,
key_index,
override_group_keys,
)
def _wrap_applied_output_series(
self,
values: list[Series],
not_indexed_same: bool,
first_not_none,
key_index,
override_group_keys: bool,
) -> DataFrame | Series:
kwargs = first_not_none._construct_axes_dict()
backup = create_series_with_explicit_dtype(dtype_if_empty=object, **kwargs)
values = [x if (x is not None) else backup for x in values]
all_indexed_same = all_indexes_same(x.index for x in values)
if self.squeeze:
applied_index = self._selected_obj._get_axis(self.axis)
singular_series = len(values) == 1 and applied_index.nlevels == 1
if singular_series:
# single values
return self._concat_objects(
values,
not_indexed_same=not_indexed_same,
override_group_keys=override_group_keys,
)
# still a series
# path added as of GH 5545
elif all_indexed_same:
from pandas.core.reshape.concat import concat
return concat(values)
if not all_indexed_same:
# GH 8467
return self._concat_objects(
values,
not_indexed_same=True,
override_group_keys=override_group_keys,
)
# Combine values
# vstack+constructor is faster than concat and handles MI-columns
stacked_values = np.vstack([np.asarray(v) for v in values])
if self.axis == 0:
index = key_index
columns = first_not_none.index.copy()
if columns.name is None:
# GH6124 - propagate name of Series when it's consistent
names = {v.name for v in values}
if len(names) == 1:
columns.name = list(names)[0]
else:
index = first_not_none.index
columns = key_index
stacked_values = stacked_values.T
if stacked_values.dtype == object:
stacked_values = stacked_values.tolist()
result = self.obj._constructor(stacked_values, index=index, columns=columns)
if not self.as_index:
self._insert_inaxis_grouper_inplace(result)
return self._reindex_output(result)
def _cython_transform(
self, how: str, numeric_only: bool = True, axis: int = 0, **kwargs
) -> DataFrame:
assert axis == 0 # handled by caller
# TODO: no tests with self.ndim == 1 for DataFrameGroupBy
# With self.axis == 0, we have multi-block tests
# e.g. test_rank_min_int, test_cython_transform_frame
# test_transform_numeric_ret
# With self.axis == 1, _get_data_to_aggregate does a transpose
# so we always have a single block.
mgr: Manager2D = self._get_data_to_aggregate()
if numeric_only:
mgr = mgr.get_numeric_data(copy=False)
def arr_func(bvalues: ArrayLike) -> ArrayLike:
return self.grouper._cython_operation(
"transform", bvalues, how, 1, **kwargs
)
# We could use `mgr.apply` here and not have to set_axis, but
# we would have to do shape gymnastics for ArrayManager compat
res_mgr = mgr.grouped_reduce(arr_func, ignore_failures=True)
res_mgr.set_axis(1, mgr.axes[1])
if len(res_mgr) < len(mgr):
warn_dropping_nuisance_columns_deprecated(type(self), how)
res_df = self.obj._constructor(res_mgr)
if self.axis == 1:
res_df = res_df.T
return res_df
def _transform_general(self, func, *args, **kwargs):
from pandas.core.reshape.concat import concat
applied = []
obj = self._obj_with_exclusions
gen = self.grouper.get_iterator(obj, axis=self.axis)
fast_path, slow_path = self._define_paths(func, *args, **kwargs)
# Determine whether to use slow or fast path by evaluating on the first group.
# Need to handle the case of an empty generator and process the result so that
# it does not need to be computed again.
try:
name, group = next(gen)
except StopIteration:
pass
else:
object.__setattr__(group, "name", name)
try:
path, res = self._choose_path(fast_path, slow_path, group)
except TypeError:
return self._transform_item_by_item(obj, fast_path)
except ValueError as err:
msg = "transform must return a scalar value for each group"
raise ValueError(msg) from err
if group.size > 0:
res = _wrap_transform_general_frame(self.obj, group, res)
applied.append(res)
# Compute and process with the remaining groups
for name, group in gen:
if group.size == 0:
continue
object.__setattr__(group, "name", name)
res = path(group)
res = _wrap_transform_general_frame(self.obj, group, res)
applied.append(res)
concat_index = obj.columns if self.axis == 0 else obj.index
other_axis = 1 if self.axis == 0 else 0 # switches between 0 & 1
concatenated = concat(applied, axis=self.axis, verify_integrity=False)
concatenated = concatenated.reindex(concat_index, axis=other_axis, copy=False)
return self._set_result_index_ordered(concatenated)
@Substitution(klass="DataFrame")
@Appender(_transform_template)
def transform(self, func, *args, engine=None, engine_kwargs=None, **kwargs):
return self._transform(
func, *args, engine=engine, engine_kwargs=engine_kwargs, **kwargs
)
def _define_paths(self, func, *args, **kwargs):
if isinstance(func, str):
fast_path = lambda group: getattr(group, func)(*args, **kwargs)
slow_path = lambda group: group.apply(
lambda x: getattr(x, func)(*args, **kwargs), axis=self.axis
)
else:
fast_path = lambda group: func(group, *args, **kwargs)
slow_path = lambda group: group.apply(
lambda x: func(x, *args, **kwargs), axis=self.axis
)
return fast_path, slow_path
def _choose_path(self, fast_path: Callable, slow_path: Callable, group: DataFrame):
path = slow_path
res = slow_path(group)
if self.ngroups == 1:
# no need to evaluate multiple paths when only
# a single group exists
return path, res
# if we make it here, test if we can use the fast path
try:
res_fast = fast_path(group)
except AssertionError:
raise # pragma: no cover
except Exception:
# GH#29631 For user-defined function, we can't predict what may be
return path, res
if isinstance(res_fast, DataFrame):
if not res_fast.columns.equals(group.columns):
return path, res
elif isinstance(res_fast, Series):
if not res_fast.index.equals(group.columns):
return path, res
else:
return path, res
if res_fast.equals(res):
path = fast_path
return path, res
def _transform_item_by_item(self, obj: DataFrame, wrapper) -> DataFrame:
output = {}
inds = []
for i, (colname, sgb) in enumerate(self._iterate_column_groupbys(obj)):
try:
output[i] = sgb.transform(wrapper)
except TypeError:
warn_dropping_nuisance_columns_deprecated(type(self), "transform")
else:
inds.append(i)
if not output:
raise TypeError("Transform function invalid for data types")
columns = obj.columns.take(inds)
result = self.obj._constructor(output, index=obj.index)
result.columns = columns
return result
def filter(self, func, dropna=True, *args, **kwargs):
indices = []
obj = self._selected_obj
gen = self.grouper.get_iterator(obj, axis=self.axis)
for name, group in gen:
object.__setattr__(group, "name", name)
res = func(group, *args, **kwargs)
try:
res = res.squeeze()
except AttributeError:
pass
if is_bool(res) or (is_scalar(res) and isna(res)):
if res and notna(res):
indices.append(self._get_index(name))
else:
raise TypeError(
f"filter function returned a {type(res).__name__}, "
"but expected a scalar bool"
)
return self._apply_filter(indices, dropna)
def __getitem__(self, key) -> DataFrameGroupBy | SeriesGroupBy:
if self.axis == 1:
# GH 37725
raise ValueError("Cannot subset columns when using axis=1")
# per GH 23566
if isinstance(key, tuple) and len(key) > 1:
# if len == 1, then it becomes a SeriesGroupBy and this is actually
# valid syntax, so don't raise warning
warnings.warn(
"Indexing with multiple keys (implicitly converted to a tuple "
"of keys) will be deprecated, use a list instead.",
FutureWarning,
stacklevel=find_stack_level(),
)
return super().__getitem__(key)
def _gotitem(self, key, ndim: int, subset=None):
if ndim == 2:
if subset is None:
subset = self.obj
return DataFrameGroupBy(
subset,
self.grouper,
axis=self.axis,
level=self.level,
grouper=self.grouper,
exclusions=self.exclusions,
selection=key,
as_index=self.as_index,
sort=self.sort,
group_keys=self.group_keys,
squeeze=self.squeeze,
observed=self.observed,
mutated=self.mutated,
dropna=self.dropna,
)
elif ndim == 1:
if subset is None:
subset = self.obj[key]
return SeriesGroupBy(
subset,
level=self.level,
grouper=self.grouper,
selection=key,
sort=self.sort,
group_keys=self.group_keys,
squeeze=self.squeeze,
observed=self.observed,
dropna=self.dropna,
)
raise AssertionError("invalid ndim for _gotitem")
def _get_data_to_aggregate(self) -> Manager2D:
obj = self._obj_with_exclusions
if self.axis == 1:
return obj.T._mgr
else:
return obj._mgr
def _insert_inaxis_grouper_inplace(self, result: DataFrame) -> None:
columns = result.columns
for name, lev, in_axis in zip(
reversed(self.grouper.names),
reversed(self.grouper.get_group_levels()),
reversed([grp.in_axis for grp in self.grouper.groupings]),
):
if in_axis and name not in columns:
result.insert(0, name, lev)
def _indexed_output_to_ndframe(
self, output: Mapping[base.OutputKey, ArrayLike]
) -> DataFrame:
indexed_output = {key.position: val for key, val in output.items()}
columns = Index([key.label for key in output])
columns._set_names(self._obj_with_exclusions._get_axis(1 - self.axis).names)
result = self.obj._constructor(indexed_output)
result.columns = columns
return result
def _wrap_agged_manager(self, mgr: Manager2D) -> DataFrame:
if not self.as_index:
rows = mgr.shape[1] if mgr.shape[0] > 0 else 0
index = Index(range(rows))
mgr.set_axis(1, index)
result = self.obj._constructor(mgr)
self._insert_inaxis_grouper_inplace(result)
result = result._consolidate()
else:
index = self.grouper.result_index
mgr.set_axis(1, index)
result = self.obj._constructor(mgr)
if self.axis == 1:
result = result.T
return self._reindex_output(result)._convert(datetime=True)
def _iterate_column_groupbys(self, obj: DataFrame | Series):
for i, colname in enumerate(obj.columns):
yield colname, SeriesGroupBy(
obj.iloc[:, i],
selection=colname,
grouper=self.grouper,
exclusions=self.exclusions,
observed=self.observed,
)
def _apply_to_column_groupbys(self, func, obj: DataFrame | Series) -> DataFrame:
from pandas.core.reshape.concat import concat
columns = obj.columns
results = [
func(col_groupby) for _, col_groupby in self._iterate_column_groupbys(obj)
]
if not len(results):
return DataFrame([], columns=columns, index=self.grouper.result_index)
else:
return concat(results, keys=columns, axis=1)
def nunique(self, dropna: bool = True) -> DataFrame:
if self.axis != 0:
return self._python_agg_general(lambda sgb: sgb.nunique(dropna))
obj = self._obj_with_exclusions
results = self._apply_to_column_groupbys(
lambda sgb: sgb.nunique(dropna), obj=obj
)
if not self.as_index:
results.index = Index(range(len(results)))
self._insert_inaxis_grouper_inplace(results)
return results
@doc(
_shared_docs["idxmax"],
numeric_only_default="True for axis=0, False for axis=1",
)
def idxmax(self, axis=0, skipna: bool = True, numeric_only: bool | None = None):
axis = DataFrame._get_axis_number(axis)
if numeric_only is None:
numeric_only = None if axis == 0 else False
def func(df):
res = df._reduce(
nanops.nanargmax,
"argmax",
axis=axis,
skipna=skipna,
numeric_only=numeric_only,
)
indices = res._values
index = df._get_axis(axis)
result = [index[i] if i >= 0 else np.nan for i in indices]
return df._constructor_sliced(result, index=res.index)
func.__name__ = "idxmax"
return self._python_apply_general(func, self._obj_with_exclusions)
@doc(
_shared_docs["idxmin"],
numeric_only_default="True for axis=0, False for axis=1",
)
def idxmin(self, axis=0, skipna: bool = True, numeric_only: bool | None = None):
axis = DataFrame._get_axis_number(axis)
if numeric_only is None:
numeric_only = None if axis == 0 else False
def func(df):
res = df._reduce(
nanops.nanargmin,
"argmin",
axis=axis,
skipna=skipna,
numeric_only=numeric_only,
)
indices = res._values
index = df._get_axis(axis)
result = [index[i] if i >= 0 else np.nan for i in indices]
return df._constructor_sliced(result, index=res.index)
func.__name__ = "idxmin"
return self._python_apply_general(func, self._obj_with_exclusions)
boxplot = boxplot_frame_groupby
def value_counts(
self,
subset: Sequence[Hashable] | None = None,
normalize: bool = False,
sort: bool = True,
ascending: bool = False,
dropna: bool = True,
) -> DataFrame | Series:
if self.axis == 1:
raise NotImplementedError(
"DataFrameGroupBy.value_counts only handles axis=0"
)
with self._group_selection_context():
df = self.obj
in_axis_names = {
grouping.name for grouping in self.grouper.groupings if grouping.in_axis
}
if isinstance(self._selected_obj, Series):
name = self._selected_obj.name
keys = [] if name in in_axis_names else [self._selected_obj]
else:
keys = [
self._selected_obj.iloc[:, idx]
for idx, name in enumerate(self._selected_obj.columns)
if name not in in_axis_names
]
if subset is not None:
clashing = set(subset) & set(in_axis_names)
if clashing:
raise ValueError(
f"Keys {clashing} in subset cannot be in "
"the groupby column keys"
)
groupings = list(self.grouper.groupings)
for key in keys:
grouper, _, _ = get_grouper(
df,
key=key,
axis=self.axis,
sort=self.sort,
dropna=dropna,
)
groupings += list(grouper.groupings)
# Take the size of the overall columns
gb = df.groupby(
groupings,
sort=self.sort,
observed=self.observed,
dropna=self.dropna,
)
result_series = cast(Series, gb.size())
if normalize:
# Normalize the results by dividing by the original group sizes.
# We are guaranteed to have the first N levels be the
# user-requested grouping.
levels = list(
range(len(self.grouper.groupings), result_series.index.nlevels)
)
indexed_group_size = result_series.groupby(
result_series.index.droplevel(levels),
sort=self.sort,
observed=self.observed,
dropna=self.dropna,
).transform("sum")
result_series /= indexed_group_size
if sort:
# Sort the values and then resort by the main grouping
index_level = range(len(self.grouper.groupings))
result_series = result_series.sort_values(
ascending=ascending
).sort_index(level=index_level, sort_remaining=False)
result: Series | DataFrame
if self.as_index:
result = result_series
else:
# Convert to frame
name = "proportion" if normalize else "count"
index = result_series.index
columns = com.fill_missing_names(index.names)
if name in columns:
raise ValueError(
f"Column label '{name}' is duplicate of result column"
)
result_series.name = name
result_series.index = index.set_names(range(len(columns)))
result_frame = result_series.reset_index()
result_frame.columns = columns + [name]
result = result_frame
return result.__finalize__(self.obj, method="value_counts")
def _wrap_transform_general_frame(
obj: DataFrame, group: DataFrame, res: DataFrame | Series
) -> DataFrame:
from pandas import concat
if isinstance(res, Series):
# we need to broadcast across the
# other dimension; this will preserve dtypes
# GH14457
if res.index.is_(obj.index):
res_frame = concat([res] * len(group.columns), axis=1)
res_frame.columns = group.columns
res_frame.index = group.index
else:
res_frame = obj._constructor(
np.tile(res.values, (len(group.index), 1)),
columns=group.columns,
index=group.index,
)
assert isinstance(res_frame, DataFrame)
return res_frame
else:
return res
| true | true |
f725aeade897e373631ced4d97f1f6a440b377d9 | 1,600 | py | Python | utils/runner/optimizer/builder.py | UESTC-Liuxin/CVMI_Sementic_Segmentation | dc5bf6e940cf6961ef65abb6e7ec372f29d55249 | [
"Apache-2.0"
] | null | null | null | utils/runner/optimizer/builder.py | UESTC-Liuxin/CVMI_Sementic_Segmentation | dc5bf6e940cf6961ef65abb6e7ec372f29d55249 | [
"Apache-2.0"
] | null | null | null | utils/runner/optimizer/builder.py | UESTC-Liuxin/CVMI_Sementic_Segmentation | dc5bf6e940cf6961ef65abb6e7ec372f29d55249 | [
"Apache-2.0"
] | null | null | null | '''
Author: Liu Xin
Date: 2021-11-21 18:10:58
LastEditors: Liu Xin
LastEditTime: 2021-11-21 21:38:30
Description: file content
FilePath: /CVMI_Sementic_Segmentation/utils/runner/optimizer/builder.py
'''
import copy
import inspect
import torch
from utils.registry import Registry, build
OPTIMIZERS = Registry('optimizer')
OPTIMIZER_BUILDERS = Registry('optimizer builder')
def register_torch_optimizers():
"""
@description : 通过扫描torvh.optim文件，获取torch实现的优化器对象
@param :
@Returns :
"""
torch_optimizers = []
for module_name in dir(torch.optim):
if module_name.startswith('__'):
continue
_optim = getattr(torch.optim, module_name)
if inspect.isclass(_optim) and issubclass(_optim,
torch.optim.Optimizer):
OPTIMIZERS.register_module(_optim.__name__)(_optim)
torch_optimizers.append(module_name)
return torch_optimizers
TORCH_OPTIMIZERS = register_torch_optimizers()
def build_optimizer_constructor(cfg):
return build(cfg, OPTIMIZER_BUILDERS)
def build_optimizer(model, cfg):
optimizer_cfg = copy.deepcopy(cfg)
constructor_type = optimizer_cfg.pop('constructor',
'DefaultOptimizerConstructor')
paramwise_cfg = optimizer_cfg.pop('paramwise_cfg', None)
optim_constructor = build_optimizer_constructor(
dict(
name=constructor_type,
optimizer_cfg=optimizer_cfg,
paramwise_cfg=paramwise_cfg))
optimizer = optim_constructor(model)
return optimizer | 30.188679 | 73 | 0.683125 | import copy
import inspect
import torch
from utils.registry import Registry, build
OPTIMIZERS = Registry('optimizer')
OPTIMIZER_BUILDERS = Registry('optimizer builder')
def register_torch_optimizers():
torch_optimizers = []
for module_name in dir(torch.optim):
if module_name.startswith('__'):
continue
_optim = getattr(torch.optim, module_name)
if inspect.isclass(_optim) and issubclass(_optim,
torch.optim.Optimizer):
OPTIMIZERS.register_module(_optim.__name__)(_optim)
torch_optimizers.append(module_name)
return torch_optimizers
TORCH_OPTIMIZERS = register_torch_optimizers()
def build_optimizer_constructor(cfg):
return build(cfg, OPTIMIZER_BUILDERS)
def build_optimizer(model, cfg):
optimizer_cfg = copy.deepcopy(cfg)
constructor_type = optimizer_cfg.pop('constructor',
'DefaultOptimizerConstructor')
paramwise_cfg = optimizer_cfg.pop('paramwise_cfg', None)
optim_constructor = build_optimizer_constructor(
dict(
name=constructor_type,
optimizer_cfg=optimizer_cfg,
paramwise_cfg=paramwise_cfg))
optimizer = optim_constructor(model)
return optimizer | true | true |
f725afde370dd3d08133a4a847cf11682d4bc65e | 30 | py | Python | py_yahoo/__init__.py | satheshrgs/py_yahoo | c18d680a9c75bb28364b95ada8b4dead0302d6ed | [
"MIT"
] | null | null | null | py_yahoo/__init__.py | satheshrgs/py_yahoo | c18d680a9c75bb28364b95ada8b4dead0302d6ed | [
"MIT"
] | null | null | null | py_yahoo/__init__.py | satheshrgs/py_yahoo | c18d680a9c75bb28364b95ada8b4dead0302d6ed | [
"MIT"
] | null | null | null | from .py_yahoo import YWeather | 30 | 30 | 0.866667 | from .py_yahoo import YWeather | true | true |
f725b02ef1848fb30f10583d1d0ba4ed093eebfa | 878 | py | Python | examples/demo1/main.py | Layto888/laylib-1.0.1 | c7317c29659a476adf6e90eb729b09ce4c49e219 | [
"MIT"
] | 1 | 2018-08-04T14:44:42.000Z | 2018-08-04T14:44:42.000Z | examples/demo1/main.py | Layto888/laylib-1.0 | c7317c29659a476adf6e90eb729b09ce4c49e219 | [
"MIT"
] | null | null | null | examples/demo1/main.py | Layto888/laylib-1.0 | c7317c29659a476adf6e90eb729b09ce4c49e219 | [
"MIT"
] | null | null | null | """
Demo 01 Made with python and pygame
to test and improve the laylib-pygame framework for fast game prototyping.
----
Author: Amardjia Amine
Date: 20/10/18
Github: ---
- This demo shows how the Resources manager loads and
separates the different data and their associated variables.
All resources are showed in the terminal after running your program.
"""
from laylib import Environment
from engine import Engine
import sys
def main():
# init global environment
demo = Environment(
800, # width
600, # height
False, # full screen
'2D Demo' # window title
)
# load resources, set the game
demo.load_complete(Engine(), '../data')
# play
demo.gInstance.main_loop()
# quit the game
demo.destroy()
if __name__ == "__main__":
sys.exit(main())
| 22.512821 | 75 | 0.632118 |
from laylib import Environment
from engine import Engine
import sys
def main():
demo = Environment(
800,
600,
False,
'2D Demo'
)
demo.load_complete(Engine(), '../data')
demo.gInstance.main_loop()
demo.destroy()
if __name__ == "__main__":
sys.exit(main())
| true | true |
f725b0a65f050af516439f7b25d526190cc56ff4 | 15,585 | py | Python | mpunet/callbacks/callbacks.py | sandeepsinghsengar/MPUNet2Plus | fd97800cd349ee47d2c9cce1851a332dcbcb047c | [
"MIT"
] | 156 | 2018-12-19T19:21:30.000Z | 2022-03-10T13:14:52.000Z | mpunet/callbacks/callbacks.py | sandeepsinghsengar/MPUNet2Plus | fd97800cd349ee47d2c9cce1851a332dcbcb047c | [
"MIT"
] | 25 | 2019-07-30T07:45:26.000Z | 2022-02-10T00:38:31.000Z | mpunet/callbacks/callbacks.py | sandeepsinghsengar/MPUNet2Plus | fd97800cd349ee47d2c9cce1851a332dcbcb047c | [
"MIT"
] | 33 | 2019-01-26T16:34:50.000Z | 2022-02-20T13:48:44.000Z | import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import tensorflow as tf
import psutil
import numpy as np
import os
from tensorflow.keras.callbacks import Callback
from datetime import datetime
from mpunet.logging import ScreenLogger
from mpunet.utils.plotting import (imshow_with_label_overlay, imshow,
plot_all_training_curves)
class DividerLine(Callback):
"""
Simply prints a line to screen after each epoch
"""
def __init__(self, logger=None):
"""
Args:
logger: An instance of a MultiPlanar Logger that prints to screen
and/or file
"""
super().__init__()
self.logger = logger or ScreenLogger()
def on_epoch_end(self, epoch, logs=None):
self.logger("-"*45 + "\n")
class LearningCurve(Callback):
"""
On epoch end this callback looks for all csv files matching the 'csv_regex'
regex within the dir 'out_dir' and attempts to create a learning curve for
each file that will be saved to 'out_dir'.
Note: Failure to plot a learning curve based on a given csv file will
is handled in the plot_all_training_curves function and will not
cause the LearningCurve callback to raise an exception.
"""
def __init__(self, log_dir="logs", out_dir="logs", fname="curve.png",
csv_regex="*training.csv", logger=None, **plot_kwargs):
"""
Args:
log_dir: Relative path from the
out_dir:
fname:
csv_regex:
logger:
"""
super().__init__()
out_dir = os.path.abspath(out_dir)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
self.csv_regex = os.path.join(os.path.abspath(log_dir), csv_regex)
self.save_path = os.path.join(out_dir, fname)
self.logger = logger or ScreenLogger()
self.plot_kwargs = plot_kwargs
def on_epoch_end(self, epoch, logs={}):
plot_all_training_curves(self.csv_regex,
self.save_path,
logy=True,
raise_error=False,
logger=self.logger,
**self.plot_kwargs)
class MemoryConsumption(Callback):
def __init__(self, max_gib=None, round_=2, logger=None):
self.max_gib = max_gib
self.logger = logger
self.round_ = round_
def on_epoch_end(self, epoch, logs={}):
process = psutil.Process(os.getpid())
mem_bytes = process.memory_info().rss
mem_gib = round(mem_bytes / (1024**3), self.round_)
logs['memory_usage_gib'] = mem_gib
if self.max_gib and mem_gib >= self.max_gib:
self.warn("Stopping training from callback 'MemoryConsumption'! "
"Total memory consumption of {} GiB exceeds limitation"
" (self.max_gib = {}) ".format(mem_gib, self.max_gib))
self.model.stop_training = True
class DelayedCallback(object):
"""
Callback wrapper that delays the functionality of another callback by N
number of epochs.
"""
def __init__(self, callback, start_from=0, logger=None):
"""
Args:
callback: A tf.keras callback
start_from: Delay the activity of 'callback' until this epoch
'start_from'
logger: An instance of a MultiPlanar Logger that prints to screen
and/or file
"""
self.logger = logger or ScreenLogger()
self.callback = callback
self.start_from = start_from
def __getattr__(self, item):
return getattr(self.callback, item)
def on_epoch_end(self, epoch, logs=None):
if epoch >= self.start_from-1:
self.callback.on_epoch_end(epoch, logs=logs)
else:
self.logger("[%s] Not active at epoch %i - will be at %i" %
(self.callback.__class__.__name__,
epoch+1, self.start_from))
class TrainTimer(Callback):
"""
Appends train timing information to the log.
If called prior to tf.keras.callbacks.CSVLogger this information will
be written to disk.
"""
def __init__(self, logger=None, max_minutes=None, verbose=1):
super().__init__()
self.logger = logger or ScreenLogger()
self.max_minutes = int(max_minutes) if max_minutes else None
self.verbose = bool(verbose)
# Timing attributes
self.train_begin_time = None
self.prev_epoch_time = None
def on_train_begin(self, logs=None):
self.train_begin_time = datetime.now()
def on_epoch_begin(self, epoch, logs=None):
self.prev_epoch_time = datetime.now()
def on_epoch_end(self, epoch, logs=None):
# Compute epoch execution time
end_time = datetime.now()
epoch_time = end_time - self.prev_epoch_time
train_time = end_time - self.train_begin_time
# Update attributes
self.prev_epoch_time = end_time
# Add to logs
train_hours = round(train_time.total_seconds() / 3600, 4)
epoch_minutes = round(epoch_time.total_seconds() / 60, 4)
logs["epoch_minutes"] = epoch_minutes
logs["train_hours"] = train_hours
if self.verbose:
self.logger("[TrainTimer] Epoch time: %.2f minutes "
"- Total train time: %.2f hours"
% (epoch_minutes, train_hours))
if self.max_minutes and train_hours*60 > self.max_minutes:
self.logger("Stopping training. Training ran for {} minutes, "
"max_minutes of {} was specified on the TrainTimer "
"callback.".format(train_hours*60, self.max_minutes))
self.model.stop_training = True
class FGBatchBalancer(Callback):
"""
mpunet callback.
Sets the forced FG fraction in a batch at each epoch to 1-recall over the
validation data at the previous epoch
"""
def __init__(self, train_data, val_data=None, logger=None):
"""
Args:
train_data: A mpunet.sequence object representing the
training data
val_data: A mpunet.sequence object representing the
validation data
logger: An instance of a MultiPlanar Logger that prints to screen
and/or file
"""
super().__init__()
self.data = (("train", train_data), ("val", val_data))
self.logger = logger or ScreenLogger()
self.active = True
def on_epoch_end(self, epoch, logs=None):
if not self.active:
return None
recall = logs.get("val_recall")
if recall is None:
self.logger("[FGBatchBalancer] No val_recall in logs. "
"Disabling callback. "
"Did you put this callback before the validation "
"callback?")
self.active = False
else:
# Always at least 1 image slice
fraction = max(0.01, 1 - recall)
for name, data in self.data:
if data is not None:
data.fg_batch_fraction = fraction
self.logger("[FGBatchBalancer] Setting FG fraction for %s "
"to: %.4f - Now %s/%s" % (name,
fraction,
data.n_fg_slices,
data.batch_size))
class MeanReduceLogArrays(Callback):
"""
On epoch end, goes through the log and replaces any array entries with
their mean value.
"""
def __init__(self):
super().__init__()
def on_epoch_end(self, epoch, logs={}):
for key, value in logs.items():
if isinstance(value, (np.ndarray, list)):
logs[key] = np.mean(value)
class PrintLayerWeights(Callback):
"""
Print the weights of a specified layer every some epoch or batch.
"""
def __init__(self, layer, every=10, first=10, per_epoch=False, logger=None):
"""
Args:
layer: A tf.keras layer
every: Print the weights every 'every' batch or epoch if
per_epoch=True
first: Print the first 'first' elements of each weight matrix
per_epoch: Print after 'every' epoch instead of batch
logger: An instance of a MultiPlanar Logger that prints to screen
and/or file
"""
super().__init__()
if isinstance(layer, int):
self.layer = self.model.layers[layer]
else:
self.layer = layer
self.first = first
self.every = every
self.logger = logger or ScreenLogger()
self.per_epoch = per_epoch
if per_epoch:
# Apply on every epoch instead of per batches
self.on_epoch_begin = self.on_batch_begin
self.on_batch_begin = lambda *args, **kwargs: None
self.log()
def log(self):
self.logger("PrintLayerWeights Callback")
self.logger("Layer: ", self.layer)
self.logger("Every: ", self.every)
self.logger("First: ", self.first)
self.logger("Per epoch: ", self.per_epoch)
def on_batch_begin(self, batch, logs=None):
if batch % self.every:
return
weights = self.layer.get_weights()
self.logger("Weights for layer '%s'" % self.layer)
self.logger("Weights:\n%s" % weights[0].ravel()[:self.first])
try:
self.logger("Baises:\n%s" % weights[1].ravel()[:self.first])
except IndexError:
pass
class SaveOutputAs2DImage(Callback):
"""
Save random 2D slices from the output of a given layer during training.
"""
def __init__(self, layer, sequence, model, out_dir, every=10, logger=None):
"""
Args:
layer: A tf.keras layer
sequence: A MultiPlanar.sequence object from which batches are
sampled and pushed through the graph to output of layer
model: A tf.keras model object
out_dir: Path to directory (existing or non-existing) in which
images will be stored
every: Perform this operation every 'every' batches
"""
super().__init__()
self.every = every
self.seq = sequence
self.layer = layer
self.epoch = None
self.model = model
self.logger = logger or ScreenLogger()
self.out_dir = out_dir
if not os.path.exists(out_dir):
os.makedirs(self.out_dir)
self.log()
def log(self):
self.logger("Save Output as 2D Image Callback")
self.logger("Layer: ", self.layer)
self.logger("Every: ", self.every)
def on_epoch_begin(self, epoch, logs=None):
self.epoch = epoch
def on_batch_end(self, batch, logs=None):
if batch % self.every:
return
# Get output of layer
self.model.predict_on_batch()
sess = tf.keras.backend.get_session()
X, _, _ = self.seq[0]
outs = sess.run([self.layer.output], feed_dict={self.model.input: X})[0]
if isinstance(outs, list):
outs = outs[0]
for i, (model_in, layer_out) in enumerate(zip(X, outs)):
fig = plt.figure(figsize=(12, 6))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
# Plot model input and layer outputs on each ax
chl1, axis, slice = imshow(ax1, model_in)
chl2, _, _ = imshow(ax2, layer_out, axis=axis, slice=slice)
# Set labels and save figure
ax1.set_title("Model input - Channel %i - Axis %i - Slice %i"
% (chl1, axis,slice), size=22)
ax2.set_title("Layer output - Channel %i - Axis %i - Slice %i"
% (chl2, axis, slice), size=22)
fig.tight_layout()
fig.savefig(os.path.join(self.out_dir, "epoch_%i_batch_%i_im_%i" %
(self.epoch, batch, i)))
plt.close(fig)
class SavePredictionImages(Callback):
"""
Save images after each epoch of training of the model on a batch of
training and a batch of validation data sampled from sequence objects.
Saves the input image with ground truth overlay as well as the predicted
label masks.
"""
def __init__(self, train_data, val_data, outdir='images'):
"""
Args:
train_data: A mpunet.sequence object from which training
data can be sampled via the __getitem__ method.
val_data: A mpunet.sequence object from which validation
data can be sampled via the __getitem__ method.
outdir: Path to directory (existing or non-existing) in which
images will be stored.
"""
super().__init__()
self.train_data = train_data
self.val_data = val_data
self.save_path = os.path.abspath(os.path.join(outdir, "pred_images_at_epoch"))
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
def pred_and_save(self, data, subdir):
# Get a random batch
X, y, _ = data[np.random.randint(len(data), dtype=np.int64)]
# Predict on the batch
pred = self.model.predict(X)
subdir = os.path.join(self.save_path, subdir)
if not os.path.exists(subdir):
os.mkdir(subdir)
# Plot each sample in the batch
for i, (im, lab, p) in enumerate(zip(X, y, pred)):
fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(12, 6))
lab = lab.reshape(im.shape[:-1] + (lab.shape[-1],))
p = p.reshape(im.shape[:-1] + (p.shape[-1],))
# Imshow ground truth on ax2
# This function will determine which channel, axis and slice to
# show and return so that we can use them for the other 2 axes
chnl, axis, slice = imshow_with_label_overlay(ax2, im, lab, lab_alpha=1.0)
# Imshow pred on ax3
imshow_with_label_overlay(ax3, im, p, lab_alpha=1.0,
channel=chnl, axis=axis, slice=slice)
# Imshow raw image on ax1
# Chose the same slice, channel and axis as above
im = im[..., chnl]
im = np.moveaxis(im, axis, 0)
if slice is not None:
# Only for 3D imges
im = im[slice]
ax1.imshow(im, cmap="gray")
# Set labels
ax1.set_title("Image", size=18)
ax2.set_title("True labels", size=18)
ax3.set_title("Prediction", size=18)
fig.tight_layout()
with np.testing.suppress_warnings() as sup:
sup.filter(UserWarning)
fig.savefig(os.path.join(subdir, str(i) + ".png"))
plt.close(fig.number)
def on_epoch_end(self, epoch, logs={}):
self.pred_and_save(self.train_data, "train_%s" % epoch)
if self.val_data is not None:
self.pred_and_save(self.val_data, "val_%s" % epoch)
| 37.019002 | 86 | 0.569714 | import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
import tensorflow as tf
import psutil
import numpy as np
import os
from tensorflow.keras.callbacks import Callback
from datetime import datetime
from mpunet.logging import ScreenLogger
from mpunet.utils.plotting import (imshow_with_label_overlay, imshow,
plot_all_training_curves)
class DividerLine(Callback):
def __init__(self, logger=None):
super().__init__()
self.logger = logger or ScreenLogger()
def on_epoch_end(self, epoch, logs=None):
self.logger("-"*45 + "\n")
class LearningCurve(Callback):
def __init__(self, log_dir="logs", out_dir="logs", fname="curve.png",
csv_regex="*training.csv", logger=None, **plot_kwargs):
super().__init__()
out_dir = os.path.abspath(out_dir)
if not os.path.exists(out_dir):
os.makedirs(out_dir)
self.csv_regex = os.path.join(os.path.abspath(log_dir), csv_regex)
self.save_path = os.path.join(out_dir, fname)
self.logger = logger or ScreenLogger()
self.plot_kwargs = plot_kwargs
def on_epoch_end(self, epoch, logs={}):
plot_all_training_curves(self.csv_regex,
self.save_path,
logy=True,
raise_error=False,
logger=self.logger,
**self.plot_kwargs)
class MemoryConsumption(Callback):
def __init__(self, max_gib=None, round_=2, logger=None):
self.max_gib = max_gib
self.logger = logger
self.round_ = round_
def on_epoch_end(self, epoch, logs={}):
process = psutil.Process(os.getpid())
mem_bytes = process.memory_info().rss
mem_gib = round(mem_bytes / (1024**3), self.round_)
logs['memory_usage_gib'] = mem_gib
if self.max_gib and mem_gib >= self.max_gib:
self.warn("Stopping training from callback 'MemoryConsumption'! "
"Total memory consumption of {} GiB exceeds limitation"
" (self.max_gib = {}) ".format(mem_gib, self.max_gib))
self.model.stop_training = True
class DelayedCallback(object):
def __init__(self, callback, start_from=0, logger=None):
self.logger = logger or ScreenLogger()
self.callback = callback
self.start_from = start_from
def __getattr__(self, item):
return getattr(self.callback, item)
def on_epoch_end(self, epoch, logs=None):
if epoch >= self.start_from-1:
self.callback.on_epoch_end(epoch, logs=logs)
else:
self.logger("[%s] Not active at epoch %i - will be at %i" %
(self.callback.__class__.__name__,
epoch+1, self.start_from))
class TrainTimer(Callback):
def __init__(self, logger=None, max_minutes=None, verbose=1):
super().__init__()
self.logger = logger or ScreenLogger()
self.max_minutes = int(max_minutes) if max_minutes else None
self.verbose = bool(verbose)
self.train_begin_time = None
self.prev_epoch_time = None
def on_train_begin(self, logs=None):
self.train_begin_time = datetime.now()
def on_epoch_begin(self, epoch, logs=None):
self.prev_epoch_time = datetime.now()
def on_epoch_end(self, epoch, logs=None):
end_time = datetime.now()
epoch_time = end_time - self.prev_epoch_time
train_time = end_time - self.train_begin_time
self.prev_epoch_time = end_time
train_hours = round(train_time.total_seconds() / 3600, 4)
epoch_minutes = round(epoch_time.total_seconds() / 60, 4)
logs["epoch_minutes"] = epoch_minutes
logs["train_hours"] = train_hours
if self.verbose:
self.logger("[TrainTimer] Epoch time: %.2f minutes "
"- Total train time: %.2f hours"
% (epoch_minutes, train_hours))
if self.max_minutes and train_hours*60 > self.max_minutes:
self.logger("Stopping training. Training ran for {} minutes, "
"max_minutes of {} was specified on the TrainTimer "
"callback.".format(train_hours*60, self.max_minutes))
self.model.stop_training = True
class FGBatchBalancer(Callback):
def __init__(self, train_data, val_data=None, logger=None):
super().__init__()
self.data = (("train", train_data), ("val", val_data))
self.logger = logger or ScreenLogger()
self.active = True
def on_epoch_end(self, epoch, logs=None):
if not self.active:
return None
recall = logs.get("val_recall")
if recall is None:
self.logger("[FGBatchBalancer] No val_recall in logs. "
"Disabling callback. "
"Did you put this callback before the validation "
"callback?")
self.active = False
else:
fraction = max(0.01, 1 - recall)
for name, data in self.data:
if data is not None:
data.fg_batch_fraction = fraction
self.logger("[FGBatchBalancer] Setting FG fraction for %s "
"to: %.4f - Now %s/%s" % (name,
fraction,
data.n_fg_slices,
data.batch_size))
class MeanReduceLogArrays(Callback):
def __init__(self):
super().__init__()
def on_epoch_end(self, epoch, logs={}):
for key, value in logs.items():
if isinstance(value, (np.ndarray, list)):
logs[key] = np.mean(value)
class PrintLayerWeights(Callback):
def __init__(self, layer, every=10, first=10, per_epoch=False, logger=None):
super().__init__()
if isinstance(layer, int):
self.layer = self.model.layers[layer]
else:
self.layer = layer
self.first = first
self.every = every
self.logger = logger or ScreenLogger()
self.per_epoch = per_epoch
if per_epoch:
self.on_epoch_begin = self.on_batch_begin
self.on_batch_begin = lambda *args, **kwargs: None
self.log()
def log(self):
self.logger("PrintLayerWeights Callback")
self.logger("Layer: ", self.layer)
self.logger("Every: ", self.every)
self.logger("First: ", self.first)
self.logger("Per epoch: ", self.per_epoch)
def on_batch_begin(self, batch, logs=None):
if batch % self.every:
return
weights = self.layer.get_weights()
self.logger("Weights for layer '%s'" % self.layer)
self.logger("Weights:\n%s" % weights[0].ravel()[:self.first])
try:
self.logger("Baises:\n%s" % weights[1].ravel()[:self.first])
except IndexError:
pass
class SaveOutputAs2DImage(Callback):
def __init__(self, layer, sequence, model, out_dir, every=10, logger=None):
super().__init__()
self.every = every
self.seq = sequence
self.layer = layer
self.epoch = None
self.model = model
self.logger = logger or ScreenLogger()
self.out_dir = out_dir
if not os.path.exists(out_dir):
os.makedirs(self.out_dir)
self.log()
def log(self):
self.logger("Save Output as 2D Image Callback")
self.logger("Layer: ", self.layer)
self.logger("Every: ", self.every)
def on_epoch_begin(self, epoch, logs=None):
self.epoch = epoch
def on_batch_end(self, batch, logs=None):
if batch % self.every:
return
self.model.predict_on_batch()
sess = tf.keras.backend.get_session()
X, _, _ = self.seq[0]
outs = sess.run([self.layer.output], feed_dict={self.model.input: X})[0]
if isinstance(outs, list):
outs = outs[0]
for i, (model_in, layer_out) in enumerate(zip(X, outs)):
fig = plt.figure(figsize=(12, 6))
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
chl1, axis, slice = imshow(ax1, model_in)
chl2, _, _ = imshow(ax2, layer_out, axis=axis, slice=slice)
ax1.set_title("Model input - Channel %i - Axis %i - Slice %i"
% (chl1, axis,slice), size=22)
ax2.set_title("Layer output - Channel %i - Axis %i - Slice %i"
% (chl2, axis, slice), size=22)
fig.tight_layout()
fig.savefig(os.path.join(self.out_dir, "epoch_%i_batch_%i_im_%i" %
(self.epoch, batch, i)))
plt.close(fig)
class SavePredictionImages(Callback):
def __init__(self, train_data, val_data, outdir='images'):
super().__init__()
self.train_data = train_data
self.val_data = val_data
self.save_path = os.path.abspath(os.path.join(outdir, "pred_images_at_epoch"))
if not os.path.exists(self.save_path):
os.makedirs(self.save_path)
def pred_and_save(self, data, subdir):
X, y, _ = data[np.random.randint(len(data), dtype=np.int64)]
pred = self.model.predict(X)
subdir = os.path.join(self.save_path, subdir)
if not os.path.exists(subdir):
os.mkdir(subdir)
for i, (im, lab, p) in enumerate(zip(X, y, pred)):
fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(12, 6))
lab = lab.reshape(im.shape[:-1] + (lab.shape[-1],))
p = p.reshape(im.shape[:-1] + (p.shape[-1],))
chnl, axis, slice = imshow_with_label_overlay(ax2, im, lab, lab_alpha=1.0)
imshow_with_label_overlay(ax3, im, p, lab_alpha=1.0,
channel=chnl, axis=axis, slice=slice)
im = im[..., chnl]
im = np.moveaxis(im, axis, 0)
if slice is not None:
im = im[slice]
ax1.imshow(im, cmap="gray")
ax1.set_title("Image", size=18)
ax2.set_title("True labels", size=18)
ax3.set_title("Prediction", size=18)
fig.tight_layout()
with np.testing.suppress_warnings() as sup:
sup.filter(UserWarning)
fig.savefig(os.path.join(subdir, str(i) + ".png"))
plt.close(fig.number)
def on_epoch_end(self, epoch, logs={}):
self.pred_and_save(self.train_data, "train_%s" % epoch)
if self.val_data is not None:
self.pred_and_save(self.val_data, "val_%s" % epoch)
| true | true |
f725b10ac41ecee5ca5b5e982437b8bbd0484e3c | 273 | py | Python | src/password_generator.py | thiamsantos/python-labs | 91262d64b3772526d91db96976f2df77011a2343 | [
"MIT"
] | null | null | null | src/password_generator.py | thiamsantos/python-labs | 91262d64b3772526d91db96976f2df77011a2343 | [
"MIT"
] | null | null | null | src/password_generator.py | thiamsantos/python-labs | 91262d64b3772526d91db96976f2df77011a2343 | [
"MIT"
] | null | null | null | import string
import random
def generate_password():
chars = string.ascii_letters + string.digits + string.punctuation
return ''.join([random.choice(chars) for i in range(0, 15)])
def main():
print(generate_password())
if __name__ == '__main__':
main()
| 19.5 | 69 | 0.692308 | import string
import random
def generate_password():
chars = string.ascii_letters + string.digits + string.punctuation
return ''.join([random.choice(chars) for i in range(0, 15)])
def main():
print(generate_password())
if __name__ == '__main__':
main()
| true | true |
f725b1ae42f7b8692600bca55736971aa629fa7d | 4,921 | py | Python | lib/jnpr/healthbot/swagger/models/rule_schema_formula_min.py | Juniper/healthbot-py-client | 49f0884b5d01ac8430aa7ed4c9acb4e7a2b717a6 | [
"Apache-2.0"
] | 10 | 2019-10-23T12:54:37.000Z | 2022-02-07T19:24:30.000Z | lib/jnpr/healthbot/swagger/models/rule_schema_formula_min.py | Juniper/healthbot-py-client | 49f0884b5d01ac8430aa7ed4c9acb4e7a2b717a6 | [
"Apache-2.0"
] | 5 | 2019-09-30T04:29:25.000Z | 2022-02-16T12:21:06.000Z | lib/jnpr/healthbot/swagger/models/rule_schema_formula_min.py | Juniper/healthbot-py-client | 49f0884b5d01ac8430aa7ed4c9acb4e7a2b717a6 | [
"Apache-2.0"
] | 4 | 2019-09-30T01:17:48.000Z | 2020-08-25T07:27:54.000Z | # coding: utf-8
"""
Paragon Insights APIs
API interface for PI application # noqa: E501
OpenAPI spec version: 4.0.0
Contact: healthbot-feedback@juniper.net
Generated by: https://github.com/swagger-api/swagger-codegen.git
"""
import pprint
import re # noqa: F401
import six
class RuleSchemaFormulaMin(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is attribute type.
attribute_map (dict): The key is attribute name
and the value is json key in definition.
"""
swagger_types = {
'field_name': 'str',
'time_range': 'str'
}
attribute_map = {
'field_name': 'field-name',
'time_range': 'time-range'
}
def __init__(self, field_name=None, time_range=None): # noqa: E501
"""RuleSchemaFormulaMin - a model defined in Swagger""" # noqa: E501
self._field_name = None
self._time_range = None
self.discriminator = None
self.field_name = field_name
self.time_range = time_range
@property
def field_name(self):
"""Gets the field_name of this RuleSchemaFormulaMin. # noqa: E501
Field name on which min operation needs to be performed # noqa: E501
:return: The field_name of this RuleSchemaFormulaMin. # noqa: E501
:rtype: str
"""
return self._field_name
@field_name.setter
def field_name(self, field_name):
"""Sets the field_name of this RuleSchemaFormulaMin.
Field name on which min operation needs to be performed # noqa: E501
:param field_name: The field_name of this RuleSchemaFormulaMin. # noqa: E501
:type: str
"""
if field_name is None:
raise ValueError("Invalid value for `field_name`, must not be `None`") # noqa: E501
self._field_name = field_name
@property
def time_range(self):
"""Gets the time_range of this RuleSchemaFormulaMin. # noqa: E501
How much back in time should we look for data. Specify positive integer followed by s/m/h/d/w/y/o representing seconds/minutes/hours/days/weeks/years/offset. Eg: 2s # noqa: E501
:return: The time_range of this RuleSchemaFormulaMin. # noqa: E501
:rtype: str
"""
return self._time_range
@time_range.setter
def time_range(self, time_range):
"""Sets the time_range of this RuleSchemaFormulaMin.
How much back in time should we look for data. Specify positive integer followed by s/m/h/d/w/y/o representing seconds/minutes/hours/days/weeks/years/offset. Eg: 2s # noqa: E501
:param time_range: The time_range of this RuleSchemaFormulaMin. # noqa: E501
:type: str
"""
if time_range is None:
raise ValueError("Invalid value for `time_range`, must not be `None`") # noqa: E501
if time_range is not None and not re.search(r'^[1-9][0-9]*(\\.[0-9]+)?(o|s|m|h|d|w|y|offset)$', time_range): # noqa: E501
raise ValueError(r"Invalid value for `time_range`, must be a follow pattern or equal to `/^[1-9][0-9]*(\\.[0-9]+)?(o|s|m|h|d|w|y|offset)$/`") # noqa: E501
self._time_range = time_range
def to_dict(self):
"""Returns the model properties as a dict"""
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(RuleSchemaFormulaMin, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
"""Returns the string representation of the model"""
return pprint.pformat(self.to_dict())
def __repr__(self):
"""For `print` and `pprint`"""
return self.to_str()
def __eq__(self, other):
"""Returns true if both objects are equal"""
if not isinstance(other, RuleSchemaFormulaMin):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
"""Returns true if both objects are not equal"""
return not self == other
| 32.806667 | 186 | 0.59622 |
import pprint
import re
import six
class RuleSchemaFormulaMin(object):
swagger_types = {
'field_name': 'str',
'time_range': 'str'
}
attribute_map = {
'field_name': 'field-name',
'time_range': 'time-range'
}
def __init__(self, field_name=None, time_range=None):
self._field_name = None
self._time_range = None
self.discriminator = None
self.field_name = field_name
self.time_range = time_range
@property
def field_name(self):
return self._field_name
@field_name.setter
def field_name(self, field_name):
if field_name is None:
raise ValueError("Invalid value for `field_name`, must not be `None`")
self._field_name = field_name
@property
def time_range(self):
return self._time_range
@time_range.setter
def time_range(self, time_range):
if time_range is None:
raise ValueError("Invalid value for `time_range`, must not be `None`")
if time_range is not None and not re.search(r'^[1-9][0-9]*(\\.[0-9]+)?(o|s|m|h|d|w|y|offset)$', time_range):
raise ValueError(r"Invalid value for `time_range`, must be a follow pattern or equal to `/^[1-9][0-9]*(\\.[0-9]+)?(o|s|m|h|d|w|y|offset)$/`")
self._time_range = time_range
def to_dict(self):
result = {}
for attr, _ in six.iteritems(self.swagger_types):
value = getattr(self, attr)
if isinstance(value, list):
result[attr] = list(map(
lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
value
))
elif hasattr(value, "to_dict"):
result[attr] = value.to_dict()
elif isinstance(value, dict):
result[attr] = dict(map(
lambda item: (item[0], item[1].to_dict())
if hasattr(item[1], "to_dict") else item,
value.items()
))
else:
result[attr] = value
if issubclass(RuleSchemaFormulaMin, dict):
for key, value in self.items():
result[key] = value
return result
def to_str(self):
return pprint.pformat(self.to_dict())
def __repr__(self):
return self.to_str()
def __eq__(self, other):
if not isinstance(other, RuleSchemaFormulaMin):
return False
return self.__dict__ == other.__dict__
def __ne__(self, other):
return not self == other
| true | true |
f725b253adc761c90bb78171d6e459fcd4106d11 | 5,638 | py | Python | tests/integration/climsoft/controller/test_regkey_controller.py | opencdms/opencdms-api | f1ed6e1d883025a8658746fe457e0c975718c7be | [
"MIT"
] | 3 | 2020-12-01T09:25:18.000Z | 2022-02-14T23:57:34.000Z | tests/integration/climsoft/controller/test_regkey_controller.py | opencdms/opencdms-api | f1ed6e1d883025a8658746fe457e0c975718c7be | [
"MIT"
] | 11 | 2021-12-05T10:09:00.000Z | 2022-02-17T08:11:22.000Z | tests/integration/climsoft/controller/test_regkey_controller.py | opencdms/opencdms-api | f1ed6e1d883025a8658746fe457e0c975718c7be | [
"MIT"
] | 2 | 2021-03-10T19:03:05.000Z | 2021-12-11T08:36:04.000Z | import json
import pytest
from sqlalchemy.sql import text as sa_text
from sqlalchemy.orm.session import sessionmaker
from opencdms.models.climsoft import v4_1_1_core as climsoft_models
from apps.climsoft.db.engine import db_engine
from apps.climsoft.schemas import regkey_schema
from datagen.climsoft import regkey as climsoft_regkey
from faker import Faker
from fastapi.testclient import TestClient
from apps.auth.db.engine import db_engine as auth_db_engine
from apps.auth.db.models import user_model
from passlib.hash import django_pbkdf2_sha256 as handler
fake = Faker()
def setup_module(module):
with auth_db_engine.connect().execution_options(autocommit=True) as connection:
with connection.begin():
auth_db_engine.execute(sa_text(f'''
TRUNCATE TABLE {user_model.AuthUser.__tablename__} RESTART IDENTITY CASCADE
''').execution_options(autocommit=True))
with db_engine.connect().execution_options(autocommit=True) as connection:
with connection.begin():
db_engine.execute(sa_text(f"""
SET FOREIGN_KEY_CHECKS = 0;
TRUNCATE TABLE {climsoft_models.Regkey.__tablename__};
SET FOREIGN_KEY_CHECKS = 1;
"""))
Session = sessionmaker(bind=db_engine)
db_session = Session()
for i in range(1, 11):
db_session.add(climsoft_models.Regkey(
**climsoft_regkey.get_valid_reg_key_input().dict()
))
db_session.commit()
db_session.close()
AuthSession = sessionmaker(bind=auth_db_engine)
auth_session = AuthSession()
user = user_model.AuthUser(
username="testuser",
password=handler.hash("password"),
first_name=fake.first_name(),
last_name=fake.last_name(),
email=fake.email()
)
auth_session.add(user)
auth_session.commit()
auth_session.close()
def teardown_module(module):
with auth_db_engine.connect().execution_options(autocommit=True) as connection:
with connection.begin():
auth_db_engine.execute(sa_text(f'''
TRUNCATE TABLE {user_model.AuthUser.__tablename__} RESTART IDENTITY CASCADE
''').execution_options(autocommit=True))
with db_engine.connect().execution_options(autocommit=True) as connection:
with connection.begin():
db_engine.execute(sa_text(f"""
SET FOREIGN_KEY_CHECKS = 0;
TRUNCATE TABLE {climsoft_models.Regkey.__tablename__};
SET FOREIGN_KEY_CHECKS = 1;
"""))
@pytest.fixture
def get_access_token(user_access_token: str) -> str:
return user_access_token
@pytest.fixture
def get_reg_key():
Session = sessionmaker(bind=db_engine)
session = Session()
reg_key = climsoft_models.Regkey(**climsoft_regkey.get_valid_reg_key_input().dict())
session.add(reg_key)
session.commit()
yield reg_key
session.close()
def test_should_return_first_five_reg_keys(client: TestClient, get_access_token: str):
response = client.get("/climsoft/v1/reg-keys", params={"limit": 5}, headers={
"Authorization": f"Bearer {get_access_token}"
})
assert response.status_code == 200
response_data = response.json()
assert len(response_data["result"]) == 5
def test_should_return_single_reg_key(client: TestClient, get_reg_key: climsoft_models.Regkey, get_access_token: str):
response = client.get(f"/climsoft/v1/reg-keys/{get_reg_key.keyName}", headers={
"Authorization": f"Bearer {get_access_token}"
})
assert response.status_code == 200
response_data = response.json()
assert len(response_data["result"]) == 1
def test_should_create_a_reg_key(client: TestClient, get_access_token: str):
reg_key_data = climsoft_regkey.get_valid_reg_key_input().dict(by_alias=True)
response = client.post("/climsoft/v1/reg-keys", data=json.dumps(reg_key_data, default=str), headers={
"Authorization": f"Bearer {get_access_token}"
})
assert response.status_code == 200
response_data = response.json()
assert len(response_data["result"]) == 1
def test_should_raise_validation_error(client: TestClient, get_access_token: str):
reg_key_data = {"aaa": "bbbbbbb"}
response = client.post("/climsoft/v1/reg-keys", data=json.dumps(reg_key_data, default=str), headers={
"Authorization": f"Bearer {get_access_token}"
})
assert response.status_code == 422
def test_should_update_reg_key(client: TestClient, get_reg_key, get_access_token: str):
reg_key_data = regkey_schema.RegKey.from_orm(get_reg_key).dict(by_alias=True)
key_name = reg_key_data.pop("key_name")
updates = {**reg_key_data, "key_description": "updated name"}
response = client.put(f"/climsoft/v1/reg-keys/{key_name}", data=json.dumps(updates, default=str), headers={
"Authorization": f"Bearer {get_access_token}"
})
response_data = response.json()
assert response.status_code == 200
assert response_data["result"][0]["key_description"] == updates["key_description"]
def test_should_delete_reg_key(client: TestClient, get_reg_key, get_access_token: str):
reg_key_data = regkey_schema.RegKey.from_orm(get_reg_key).dict(by_alias=True)
key_name = reg_key_data.pop("key_name")
response = client.delete(f"/climsoft/v1/reg-keys/{key_name}", headers={
"Authorization": f"Bearer {get_access_token}"
})
assert response.status_code == 200
response = client.get(f"/climsoft/v1/reg-keys/{key_name}", headers={
"Authorization": f"Bearer {get_access_token}"
})
assert response.status_code == 404
| 37.586667 | 118 | 0.707166 | import json
import pytest
from sqlalchemy.sql import text as sa_text
from sqlalchemy.orm.session import sessionmaker
from opencdms.models.climsoft import v4_1_1_core as climsoft_models
from apps.climsoft.db.engine import db_engine
from apps.climsoft.schemas import regkey_schema
from datagen.climsoft import regkey as climsoft_regkey
from faker import Faker
from fastapi.testclient import TestClient
from apps.auth.db.engine import db_engine as auth_db_engine
from apps.auth.db.models import user_model
from passlib.hash import django_pbkdf2_sha256 as handler
fake = Faker()
def setup_module(module):
with auth_db_engine.connect().execution_options(autocommit=True) as connection:
with connection.begin():
auth_db_engine.execute(sa_text(f'''
TRUNCATE TABLE {user_model.AuthUser.__tablename__} RESTART IDENTITY CASCADE
''').execution_options(autocommit=True))
with db_engine.connect().execution_options(autocommit=True) as connection:
with connection.begin():
db_engine.execute(sa_text(f"""
SET FOREIGN_KEY_CHECKS = 0;
TRUNCATE TABLE {climsoft_models.Regkey.__tablename__};
SET FOREIGN_KEY_CHECKS = 1;
"""))
Session = sessionmaker(bind=db_engine)
db_session = Session()
for i in range(1, 11):
db_session.add(climsoft_models.Regkey(
**climsoft_regkey.get_valid_reg_key_input().dict()
))
db_session.commit()
db_session.close()
AuthSession = sessionmaker(bind=auth_db_engine)
auth_session = AuthSession()
user = user_model.AuthUser(
username="testuser",
password=handler.hash("password"),
first_name=fake.first_name(),
last_name=fake.last_name(),
email=fake.email()
)
auth_session.add(user)
auth_session.commit()
auth_session.close()
def teardown_module(module):
with auth_db_engine.connect().execution_options(autocommit=True) as connection:
with connection.begin():
auth_db_engine.execute(sa_text(f'''
TRUNCATE TABLE {user_model.AuthUser.__tablename__} RESTART IDENTITY CASCADE
''').execution_options(autocommit=True))
with db_engine.connect().execution_options(autocommit=True) as connection:
with connection.begin():
db_engine.execute(sa_text(f"""
SET FOREIGN_KEY_CHECKS = 0;
TRUNCATE TABLE {climsoft_models.Regkey.__tablename__};
SET FOREIGN_KEY_CHECKS = 1;
"""))
@pytest.fixture
def get_access_token(user_access_token: str) -> str:
return user_access_token
@pytest.fixture
def get_reg_key():
Session = sessionmaker(bind=db_engine)
session = Session()
reg_key = climsoft_models.Regkey(**climsoft_regkey.get_valid_reg_key_input().dict())
session.add(reg_key)
session.commit()
yield reg_key
session.close()
def test_should_return_first_five_reg_keys(client: TestClient, get_access_token: str):
response = client.get("/climsoft/v1/reg-keys", params={"limit": 5}, headers={
"Authorization": f"Bearer {get_access_token}"
})
assert response.status_code == 200
response_data = response.json()
assert len(response_data["result"]) == 5
def test_should_return_single_reg_key(client: TestClient, get_reg_key: climsoft_models.Regkey, get_access_token: str):
response = client.get(f"/climsoft/v1/reg-keys/{get_reg_key.keyName}", headers={
"Authorization": f"Bearer {get_access_token}"
})
assert response.status_code == 200
response_data = response.json()
assert len(response_data["result"]) == 1
def test_should_create_a_reg_key(client: TestClient, get_access_token: str):
reg_key_data = climsoft_regkey.get_valid_reg_key_input().dict(by_alias=True)
response = client.post("/climsoft/v1/reg-keys", data=json.dumps(reg_key_data, default=str), headers={
"Authorization": f"Bearer {get_access_token}"
})
assert response.status_code == 200
response_data = response.json()
assert len(response_data["result"]) == 1
def test_should_raise_validation_error(client: TestClient, get_access_token: str):
reg_key_data = {"aaa": "bbbbbbb"}
response = client.post("/climsoft/v1/reg-keys", data=json.dumps(reg_key_data, default=str), headers={
"Authorization": f"Bearer {get_access_token}"
})
assert response.status_code == 422
def test_should_update_reg_key(client: TestClient, get_reg_key, get_access_token: str):
reg_key_data = regkey_schema.RegKey.from_orm(get_reg_key).dict(by_alias=True)
key_name = reg_key_data.pop("key_name")
updates = {**reg_key_data, "key_description": "updated name"}
response = client.put(f"/climsoft/v1/reg-keys/{key_name}", data=json.dumps(updates, default=str), headers={
"Authorization": f"Bearer {get_access_token}"
})
response_data = response.json()
assert response.status_code == 200
assert response_data["result"][0]["key_description"] == updates["key_description"]
def test_should_delete_reg_key(client: TestClient, get_reg_key, get_access_token: str):
reg_key_data = regkey_schema.RegKey.from_orm(get_reg_key).dict(by_alias=True)
key_name = reg_key_data.pop("key_name")
response = client.delete(f"/climsoft/v1/reg-keys/{key_name}", headers={
"Authorization": f"Bearer {get_access_token}"
})
assert response.status_code == 200
response = client.get(f"/climsoft/v1/reg-keys/{key_name}", headers={
"Authorization": f"Bearer {get_access_token}"
})
assert response.status_code == 404
| true | true |
f725b3453489b66fbfc7b49799e450b8abfbfc00 | 11,264 | py | Python | deployer/src/config_creator.py | yugabyte/docsearch-scraper | 8b58d364c7721cbce892843e946834a3ccc5fcd7 | [
"MIT"
] | null | null | null | deployer/src/config_creator.py | yugabyte/docsearch-scraper | 8b58d364c7721cbce892843e946834a3ccc5fcd7 | [
"MIT"
] | null | null | null | deployer/src/config_creator.py | yugabyte/docsearch-scraper | 8b58d364c7721cbce892843e946834a3ccc5fcd7 | [
"MIT"
] | 1 | 2020-04-01T22:01:17.000Z | 2020-04-01T22:01:17.000Z | from collections import OrderedDict
import tldextract
import re
from . import helpers
from . import helpdesk_helper
from urllib.parse import urlparse
def extract_root_from_input(input_string):
# We cant parse the url since user might have not enter a proper link
# We assume that the string is already the proper root
if input_string.endswith('/'):
return input_string
# extracting substring before the first isolated / (not //)
domain = re.match(".+?([^/]/(?!/))",
input_string)
try:
url_parsed = urlparse(input_string)
# Removing unused parameters
url_parsed._replace(params='', query='', fragment='')
path_splited = url_parsed.path.split('/')
# Path is redirecting to a page
if ('html' in path_splited[-1]):
url_parsed = url_parsed._replace(path='/'.join(path_splited[: -1]))
# We are fine
else:
pass
return url_parsed.geturl() + '/'
except ValueError:
return domain.group() if domain else input_string
def to_docusaurus_config(config, urls=None):
if urls:
config["sitemap_urls"] = [
extract_root_from_input(urls[0]) + "sitemap.xml"]
config["sitemap_alternate_links"] = True
config["custom_settings"] = {"attributesForFaceting": ["language",
"version"]
}
config["selectors"]["lvl0"] = OrderedDict((
("selector",
"//*[contains(@class,'navGroups')]//*[contains(@class,'navListItemActive')]/preceding::h3[1]"),
("type", "xpath"),
("global", True),
("default_value", "Documentation")
))
config["selectors"]["lvl1"] = ".post h1"
config["selectors"]["lvl2"] = ".post h2"
config["selectors"]["lvl3"] = ".post h3"
config["selectors"]["lvl4"] = ".post h4"
config["selectors"]["lvl5"] = ".post h5"
config["selectors"]["text"] = ".post article p, .post article li"
config["selectors_exclude"] = [".hash-link"]
return config
def to_gitbook_config(config):
config["selectors"]["lvl0"] = ".markdown-section h1"
config["selectors"]["lvl1"] = ".markdown-section h2"
config["selectors"]["lvl2"] = ".markdown-section h3"
config["selectors"]["lvl3"] = ".markdown-section h4"
config["selectors"]["lvl4"] = ".markdown-section h4"
config["selectors"]["lvl5"] = ".markdown-section h5"
config["selectors"]["text"] = ".markdown-section p, .markdown-section li"
return config
def to_pkgdown_config(config, urls=None):
if urls:
root = extract_root_from_input(urls[0])
config["start_urls"] = [{
"url": root + "index.html",
"selectors_key": "homepage",
"tags": [
"homepage"
]
},
{
"url": root + "reference",
"selectors_key": "reference",
"tags": [
"reference"
]
},
{
"url": root + "articles",
"selectors_key": "articles",
"tags": [
"articles"
]
}]
config["sitemap_urls"] = [
root + "sitemap.xml"]
config["selectors"] = OrderedDict((
("homepage", OrderedDict((
("lvl0", OrderedDict((
("selector", ".contents h1"),
("default_value", "pkgdown Home page")
))),
("lvl1", ".contents h2"),
("lvl2", OrderedDict((
("selector", ".contents h3"),
("default_value", "Context")
))),
("lvl3", ".ref-arguments td, .ref-description"),
("text", ".contents p, .contents li, .contents .pre")
))),
("reference", OrderedDict((
("lvl0", ".contents h1"),
("lvl1", OrderedDict((
("selector", ".contents .name"),
("default_value", "Argument")
))),
("lvl2", OrderedDict((
("selector", ".ref-arguments th"),
("default_value", "Description")
))),
("lvl3", ".ref-arguments td, .ref-description"),
("text", ".contents p, .contents li")
))),
("articles", OrderedDict((
("lvl0", ".contents h1"),
("lvl1", ".contents .name"),
("lvl2", OrderedDict((
("selector", ".contents h2, .contents h3"),
("default_value", "Context")
))),
("text",
".contents p, .contents li")
))),
("default", OrderedDict((
("lvl1", ".contents h2"),
("lvl2", ".contents h3, .contents th"),
("lvl3", ".contents h4"),
("lvl4", ".contents h5"),
("text",
".contents p, .contents li, .usage, .template-article .contents .pre")
)))
))
config["selectors_exclude"] = [".dont-index"]
config["stop_urls"] = ["/reference/$",
"/reference/index.html",
"/articles/$",
"/articles/index.html"]
config["custom_settings"] = {
"separatorsToIndex": "_",
"attributesToRetrieve": ["hierarchy",
"content", "anchor", "url",
"url_without_anchor"]
}
config["min_indexed_level"] = 2
return config
def to_vuepress_config(config):
config["selectors"]["lvl0"] = OrderedDict((
("selector", "p.sidebar-heading.open"),
("global", True),
("default_value", "Documentation")
))
config["custom_settings"] = {"attributesForFaceting": ["lang"]
}
config["selectors"]["lvl1"] = ".content h1"
config["selectors"]["lvl2"] = ".content h2"
config["selectors"]["lvl3"] = ".content h3"
config["selectors"]["lvl4"] = ".content h4"
config["selectors"]["lvl5"] = ".content h5"
config["selectors"]["text"] = ".content p, .content li"
config["selectors"]["lang"] = OrderedDict((
("selector", "/html/@lang"),
("type", "xpath"),
("global", True),
("default_value", "en-US")
))
config["scrap_start_urls"] = False
config["strip_chars"] = " .,;:#"
return config
def to_larecipe_config(config, urls=None):
if urls:
config["sitemap_urls"] = [
extract_root_from_input(urls[0]) + "sitemap.xml"]
config["selectors"]["lvl0"] = OrderedDict((
("selector",
"//div[contains(@class, 'sidebar')]//li/a[text()=//div[contains(@class, 'article')]//h1[1]/text()]"),
("global", True),
("type", "xpath"),
("default_value", "Documentation")
))
config["selectors"]["lvl1"] = "div.article h1"
config["selectors"]["lvl2"] = "div.article h2"
config["selectors"]["lvl3"] = "div.article h3"
config["selectors"]["lvl4"] = "div.article h4"
config["selectors"]["lvl5"] = "div.article h5"
config["selectors"]["text"] = "div.article p, div.article li"
return config
def to_publii_config(config, urls=None):
if urls:
config["sitemap_urls"] = [
extract_root_from_input(urls[0]) + "sitemap.xml"]
config["selectors"]["lvl0"] = OrderedDict((
("selector", ".active-parent > span"),
("global", True),
("default_value", "Documentation")
))
config["selectors"]["lvl1"] = ".content h1"
config["selectors"]["lvl2"] = ".content h2"
config["selectors"]["lvl3"] = ".content h3"
config["selectors"]["lvl4"] = ".content h4"
config["selectors"]["lvl5"] = ".content h5"
config["selectors"]["text"] = ".content p, .content li"
config["only_content_level"] = True
return config
def to_jsdoc_config(config, urls=None):
config["stop_urls"] = ["\\.js\\.html",
"/index\\.html$"]
config["selectors"]["lvl0"] = OrderedDict((
("selector", "#main .page-title"),
("global", True),
("default_value", "Documentation")
))
config["selectors"]["lvl1"] = "#main h3"
config["selectors"]["lvl2"] = "#main h4"
config["selectors"]["lvl3"] = "#main h5"
config["selectors"]["lvl4"] = "#main h6, #main td.name"
del config["selectors"]["lvl5"]
config["selectors"]["text"] = "#main p, #main li"
config["selectors_exclude"] = [".signature",
".type-signature",
".details"]
return config
def create_config(u=None):
config = OrderedDict((
("index_name", ""),
("start_urls", []),
("stop_urls", []),
("selectors", OrderedDict((
("lvl0", "FIXME h1"),
("lvl1", "FIXME h2"),
("lvl2", "FIXME h3"),
("lvl3", "FIXME h4"),
("lvl4", "FIXME h5"),
("lvl5", "FIXME h6"),
("text", "FIXME p, FIXME li")
)))
))
if u is None:
u = helpers.get_user_value("start url: ")
urls = [u]
if helpdesk_helper.is_helpdesk_url(u):
cuid = helpdesk_helper.get_conversation_ID_from_url(u)
conversation = helpdesk_helper.get_conversation(cuid)
url_from_conversation = helpdesk_helper.get_start_url_from_conversation(
conversation)
urls = [url_from_conversation]
u = url_from_conversation
if helpdesk_helper.is_docusaurus_conversation(conversation):
config = to_docusaurus_config(config, urls)
elif helpdesk_helper.is_gitbook_conversation(conversation):
config = to_gitbook_config(config)
elif helpdesk_helper.is_pkgdown_conversation(conversation):
config = to_pkgdown_config(config, urls)
elif helpdesk_helper.is_vuepress_conversation(conversation):
config = to_vuepress_config(config)
elif helpdesk_helper.is_larecipe_conversation(conversation):
config = to_larecipe_config(config, urls)
elif helpdesk_helper.is_publii_conversation(conversation):
config = to_publii_config(config, urls)
elif helpdesk_helper.is_jsdoc_conversation(conversation):
config = to_jsdoc_config(config, urls)
config["conversation_id"] = [cuid]
if '.html' in u:
urls.append(u.rsplit('/', 1)[0])
# Use subdomain for github website https://<subdomain>.github.io/
config['index_name'] = tldextract.extract(
u).subdomain if tldextract.extract(
u).domain == 'github' else tldextract.extract(u).domain
if len(config['start_urls']) == 0:
config['start_urls'] = urls
user_index_name = helpers.get_user_value(
'index_name is \033[1;33m{}\033[0m [enter to confirm]: '.format(config[
"index_name"]))
if user_index_name != "":
config['index_name'] = user_index_name
print('index_name is now \033[1;33m{}\033[0m'.format(config[
"index_name"]))
return config
| 34.552147 | 110 | 0.532404 | from collections import OrderedDict
import tldextract
import re
from . import helpers
from . import helpdesk_helper
from urllib.parse import urlparse
def extract_root_from_input(input_string):
if input_string.endswith('/'):
return input_string
domain = re.match(".+?([^/]/(?!/))",
input_string)
try:
url_parsed = urlparse(input_string)
url_parsed._replace(params='', query='', fragment='')
path_splited = url_parsed.path.split('/')
if ('html' in path_splited[-1]):
url_parsed = url_parsed._replace(path='/'.join(path_splited[: -1]))
else:
pass
return url_parsed.geturl() + '/'
except ValueError:
return domain.group() if domain else input_string
def to_docusaurus_config(config, urls=None):
if urls:
config["sitemap_urls"] = [
extract_root_from_input(urls[0]) + "sitemap.xml"]
config["sitemap_alternate_links"] = True
config["custom_settings"] = {"attributesForFaceting": ["language",
"version"]
}
config["selectors"]["lvl0"] = OrderedDict((
("selector",
"//*[contains(@class,'navGroups')]//*[contains(@class,'navListItemActive')]/preceding::h3[1]"),
("type", "xpath"),
("global", True),
("default_value", "Documentation")
))
config["selectors"]["lvl1"] = ".post h1"
config["selectors"]["lvl2"] = ".post h2"
config["selectors"]["lvl3"] = ".post h3"
config["selectors"]["lvl4"] = ".post h4"
config["selectors"]["lvl5"] = ".post h5"
config["selectors"]["text"] = ".post article p, .post article li"
config["selectors_exclude"] = [".hash-link"]
return config
def to_gitbook_config(config):
config["selectors"]["lvl0"] = ".markdown-section h1"
config["selectors"]["lvl1"] = ".markdown-section h2"
config["selectors"]["lvl2"] = ".markdown-section h3"
config["selectors"]["lvl3"] = ".markdown-section h4"
config["selectors"]["lvl4"] = ".markdown-section h4"
config["selectors"]["lvl5"] = ".markdown-section h5"
config["selectors"]["text"] = ".markdown-section p, .markdown-section li"
return config
def to_pkgdown_config(config, urls=None):
if urls:
root = extract_root_from_input(urls[0])
config["start_urls"] = [{
"url": root + "index.html",
"selectors_key": "homepage",
"tags": [
"homepage"
]
},
{
"url": root + "reference",
"selectors_key": "reference",
"tags": [
"reference"
]
},
{
"url": root + "articles",
"selectors_key": "articles",
"tags": [
"articles"
]
}]
config["sitemap_urls"] = [
root + "sitemap.xml"]
config["selectors"] = OrderedDict((
("homepage", OrderedDict((
("lvl0", OrderedDict((
("selector", ".contents h1"),
("default_value", "pkgdown Home page")
))),
("lvl1", ".contents h2"),
("lvl2", OrderedDict((
("selector", ".contents h3"),
("default_value", "Context")
))),
("lvl3", ".ref-arguments td, .ref-description"),
("text", ".contents p, .contents li, .contents .pre")
))),
("reference", OrderedDict((
("lvl0", ".contents h1"),
("lvl1", OrderedDict((
("selector", ".contents .name"),
("default_value", "Argument")
))),
("lvl2", OrderedDict((
("selector", ".ref-arguments th"),
("default_value", "Description")
))),
("lvl3", ".ref-arguments td, .ref-description"),
("text", ".contents p, .contents li")
))),
("articles", OrderedDict((
("lvl0", ".contents h1"),
("lvl1", ".contents .name"),
("lvl2", OrderedDict((
("selector", ".contents h2, .contents h3"),
("default_value", "Context")
))),
("text",
".contents p, .contents li")
))),
("default", OrderedDict((
("lvl1", ".contents h2"),
("lvl2", ".contents h3, .contents th"),
("lvl3", ".contents h4"),
("lvl4", ".contents h5"),
("text",
".contents p, .contents li, .usage, .template-article .contents .pre")
)))
))
config["selectors_exclude"] = [".dont-index"]
config["stop_urls"] = ["/reference/$",
"/reference/index.html",
"/articles/$",
"/articles/index.html"]
config["custom_settings"] = {
"separatorsToIndex": "_",
"attributesToRetrieve": ["hierarchy",
"content", "anchor", "url",
"url_without_anchor"]
}
config["min_indexed_level"] = 2
return config
def to_vuepress_config(config):
config["selectors"]["lvl0"] = OrderedDict((
("selector", "p.sidebar-heading.open"),
("global", True),
("default_value", "Documentation")
))
config["custom_settings"] = {"attributesForFaceting": ["lang"]
}
config["selectors"]["lvl1"] = ".content h1"
config["selectors"]["lvl2"] = ".content h2"
config["selectors"]["lvl3"] = ".content h3"
config["selectors"]["lvl4"] = ".content h4"
config["selectors"]["lvl5"] = ".content h5"
config["selectors"]["text"] = ".content p, .content li"
config["selectors"]["lang"] = OrderedDict((
("selector", "/html/@lang"),
("type", "xpath"),
("global", True),
("default_value", "en-US")
))
config["scrap_start_urls"] = False
config["strip_chars"] = " .,;:#"
return config
def to_larecipe_config(config, urls=None):
if urls:
config["sitemap_urls"] = [
extract_root_from_input(urls[0]) + "sitemap.xml"]
config["selectors"]["lvl0"] = OrderedDict((
("selector",
"//div[contains(@class, 'sidebar')]//li/a[text()=//div[contains(@class, 'article')]//h1[1]/text()]"),
("global", True),
("type", "xpath"),
("default_value", "Documentation")
))
config["selectors"]["lvl1"] = "div.article h1"
config["selectors"]["lvl2"] = "div.article h2"
config["selectors"]["lvl3"] = "div.article h3"
config["selectors"]["lvl4"] = "div.article h4"
config["selectors"]["lvl5"] = "div.article h5"
config["selectors"]["text"] = "div.article p, div.article li"
return config
def to_publii_config(config, urls=None):
if urls:
config["sitemap_urls"] = [
extract_root_from_input(urls[0]) + "sitemap.xml"]
config["selectors"]["lvl0"] = OrderedDict((
("selector", ".active-parent > span"),
("global", True),
("default_value", "Documentation")
))
config["selectors"]["lvl1"] = ".content h1"
config["selectors"]["lvl2"] = ".content h2"
config["selectors"]["lvl3"] = ".content h3"
config["selectors"]["lvl4"] = ".content h4"
config["selectors"]["lvl5"] = ".content h5"
config["selectors"]["text"] = ".content p, .content li"
config["only_content_level"] = True
return config
def to_jsdoc_config(config, urls=None):
config["stop_urls"] = ["\\.js\\.html",
"/index\\.html$"]
config["selectors"]["lvl0"] = OrderedDict((
("selector", "#main .page-title"),
("global", True),
("default_value", "Documentation")
))
config["selectors"]["lvl1"] = "#main h3"
config["selectors"]["lvl2"] = "#main h4"
config["selectors"]["lvl3"] = "#main h5"
config["selectors"]["lvl4"] = "#main h6, #main td.name"
del config["selectors"]["lvl5"]
config["selectors"]["text"] = "#main p, #main li"
config["selectors_exclude"] = [".signature",
".type-signature",
".details"]
return config
def create_config(u=None):
config = OrderedDict((
("index_name", ""),
("start_urls", []),
("stop_urls", []),
("selectors", OrderedDict((
("lvl0", "FIXME h1"),
("lvl1", "FIXME h2"),
("lvl2", "FIXME h3"),
("lvl3", "FIXME h4"),
("lvl4", "FIXME h5"),
("lvl5", "FIXME h6"),
("text", "FIXME p, FIXME li")
)))
))
if u is None:
u = helpers.get_user_value("start url: ")
urls = [u]
if helpdesk_helper.is_helpdesk_url(u):
cuid = helpdesk_helper.get_conversation_ID_from_url(u)
conversation = helpdesk_helper.get_conversation(cuid)
url_from_conversation = helpdesk_helper.get_start_url_from_conversation(
conversation)
urls = [url_from_conversation]
u = url_from_conversation
if helpdesk_helper.is_docusaurus_conversation(conversation):
config = to_docusaurus_config(config, urls)
elif helpdesk_helper.is_gitbook_conversation(conversation):
config = to_gitbook_config(config)
elif helpdesk_helper.is_pkgdown_conversation(conversation):
config = to_pkgdown_config(config, urls)
elif helpdesk_helper.is_vuepress_conversation(conversation):
config = to_vuepress_config(config)
elif helpdesk_helper.is_larecipe_conversation(conversation):
config = to_larecipe_config(config, urls)
elif helpdesk_helper.is_publii_conversation(conversation):
config = to_publii_config(config, urls)
elif helpdesk_helper.is_jsdoc_conversation(conversation):
config = to_jsdoc_config(config, urls)
config["conversation_id"] = [cuid]
if '.html' in u:
urls.append(u.rsplit('/', 1)[0])
config['index_name'] = tldextract.extract(
u).subdomain if tldextract.extract(
u).domain == 'github' else tldextract.extract(u).domain
if len(config['start_urls']) == 0:
config['start_urls'] = urls
user_index_name = helpers.get_user_value(
'index_name is \033[1;33m{}\033[0m [enter to confirm]: '.format(config[
"index_name"]))
if user_index_name != "":
config['index_name'] = user_index_name
print('index_name is now \033[1;33m{}\033[0m'.format(config[
"index_name"]))
return config
| true | true |
f725b3e77a5288c1d9314ff987b54ca1c859daff | 46,076 | py | Python | tests/plugins/test_reactor_config.py | MartinBasti/atomic-reactor | 4431225c5a474c7f88c63ec1f25216d4b84a0f1d | [
"BSD-3-Clause"
] | null | null | null | tests/plugins/test_reactor_config.py | MartinBasti/atomic-reactor | 4431225c5a474c7f88c63ec1f25216d4b84a0f1d | [
"BSD-3-Clause"
] | null | null | null | tests/plugins/test_reactor_config.py | MartinBasti/atomic-reactor | 4431225c5a474c7f88c63ec1f25216d4b84a0f1d | [
"BSD-3-Clause"
] | null | null | null | """
Copyright (c) 2017 Red Hat, Inc
All rights reserved.
This software may be modified and distributed under the terms
of the BSD license. See the LICENSE file for details.
"""
from __future__ import unicode_literals
from jsonschema import ValidationError
import io
import logging
import os
import pkg_resources
import pytest
from textwrap import dedent
import re
import yaml
import smtplib
from copy import deepcopy
import atomic_reactor
import koji
from atomic_reactor.core import DockerTasker
from atomic_reactor.inner import DockerBuildWorkflow
from atomic_reactor.util import read_yaml
import atomic_reactor.koji_util
import atomic_reactor.pulp_util
import atomic_reactor.odcs_util
import osbs.conf
import osbs.api
from osbs.utils import RegistryURI
from atomic_reactor.plugins.pre_reactor_config import (ReactorConfig,
ReactorConfigPlugin,
get_config, WORKSPACE_CONF_KEY,
get_koji_session,
get_koji_path_info,
get_pulp_session,
get_odcs_session,
get_smtp_session,
get_openshift_session,
get_clusters_client_config_path,
get_docker_registry,
get_platform_to_goarch_mapping,
get_goarch_to_platform_mapping,
get_default_image_build_method,
get_flatpak_base_image,
CONTAINER_DEFAULT_BUILD_METHOD,
get_build_image_override,
NO_FALLBACK)
from tests.constants import TEST_IMAGE, REACTOR_CONFIG_MAP
from tests.docker_mock import mock_docker
from tests.fixtures import reactor_config_map # noqa
from flexmock import flexmock
class TestReactorConfigPlugin(object):
def prepare(self):
mock_docker()
tasker = DockerTasker()
workflow = DockerBuildWorkflow({'provider': 'git', 'uri': 'asd'},
TEST_IMAGE)
return tasker, workflow
@pytest.mark.parametrize(('fallback'), [
False,
True
])
@pytest.mark.parametrize(('config', 'valid'), [
("""\
version: 1
registries:
- url: https://container-registry.example.com/v2
auth:
cfg_path: /var/run/secrets/atomic-reactor/v2-registry-dockercfg
""",
True),
("""\
version: 1
registries:
- url: https://old-container-registry.example.com/v1
auth:
cfg_path: /var/run/secrets/atomic-reactor/v1-registry-dockercfg
- url: https://container-registry.example.com/v2
auth:
cfg_path: /var/run/secrets/atomic-reactor/v2-registry-dockercfg
""",
True),
("""\
version: 1
registries:
- url: https://old-container-registry.example.com/v1
auth:
cfg_path: /var/run/secrets/atomic-reactor/v1-registry-dockercfg
""",
False),
])
def test_get_docker_registry(self, config, fallback, valid):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
config_json = read_yaml(config, 'schemas/config.json')
docker_reg = {
'version': 'v2',
'insecure': False,
'secret': '/var/run/secrets/atomic-reactor/v2-registry-dockercfg',
'url': 'https://container-registry.example.com/v2',
}
if fallback:
if valid:
docker_fallback = docker_reg
expected = docker_reg
else:
docker_fallback = NO_FALLBACK
else:
docker_fallback = {}
expected = {
'url': 'https://container-registry.example.com',
'insecure': False,
'secret': '/var/run/secrets/atomic-reactor/v2-registry-dockercfg'
}
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\
ReactorConfig(config_json)
if valid:
docker_registry = get_docker_registry(workflow, docker_fallback)
assert docker_registry == expected
else:
if fallback:
with pytest.raises(KeyError):
get_docker_registry(workflow, docker_fallback)
else:
with pytest.raises(RuntimeError):
get_docker_registry(workflow, docker_fallback)
def test_no_config(self):
tasker, workflow = self.prepare()
conf = get_config(workflow)
assert isinstance(conf, ReactorConfig)
same_conf = get_config(workflow)
assert conf is same_conf
@pytest.mark.parametrize('basename', ['reactor-config.yaml', None])
def test_filename(self, tmpdir, basename):
filename = os.path.join(str(tmpdir), basename or 'config.yaml')
with open(filename, 'w'):
pass
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow,
config_path=str(tmpdir),
basename=filename)
assert plugin.run() is None
def test_filename_not_found(self):
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path='/not-found')
with pytest.raises(Exception):
plugin.run()
def test_no_schema_resource(self, tmpdir, caplog):
class FakeProvider(object):
def get_resource_stream(self, pkg, rsc):
raise IOError
# pkg_resources.resource_stream() cannot be mocked directly
# Instead mock the module-level function it calls.
(flexmock(pkg_resources)
.should_receive('get_provider')
.and_return(FakeProvider()))
filename = os.path.join(str(tmpdir), 'config.yaml')
with open(filename, 'w'):
pass
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
with caplog.atLevel(logging.ERROR), pytest.raises(Exception):
plugin.run()
captured_errs = [x.message for x in caplog.records()]
assert "unable to extract JSON schema, cannot validate" in captured_errs
@pytest.mark.parametrize('schema', [
# Invalid JSON
'{',
# Invalid schema
'{"properties": {"any": null}}',
])
def test_invalid_schema_resource(self, tmpdir, caplog, schema):
class FakeProvider(object):
def get_resource_stream(self, pkg, rsc):
return io.BufferedReader(io.BytesIO(schema))
# pkg_resources.resource_stream() cannot be mocked directly
# Instead mock the module-level function it calls.
(flexmock(pkg_resources)
.should_receive('get_provider')
.and_return(FakeProvider()))
filename = os.path.join(str(tmpdir), 'config.yaml')
with open(filename, 'w'):
pass
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
with caplog.atLevel(logging.ERROR), pytest.raises(Exception):
plugin.run()
captured_errs = [x.message for x in caplog.records()]
assert any("cannot validate" in x for x in captured_errs)
@pytest.mark.parametrize(('config', 'errors'), [ # noqa:F811
("""\
clusters:
foo:
- name: bar
max_concurrent_builds: 1
""", [
"validation error (at top level): "
"%r is a required property" % u'version',
]),
("""\
version: 1
clusters:
foo:
bar: 1
plat/form:
- name: foo
max_concurrent_builds: 1
""", [
"validation error (clusters.foo): None is not of type %r" % u'array',
"validation error (clusters.bar): 1 is not of type %r" % u'array',
re.compile(r"validation error \(clusters\): .*'plat/form'"),
]),
("""\
version: 1
clusters:
foo:
- name: 1
max_concurrent_builds: 1
- name: blah
max_concurrent_builds: one
- name: "2" # quoting prevents error
max_concurrent_builds: 2
- name: negative
max_concurrent_builds: -1
""", [
"validation error (clusters.foo[0].name): "
"1 is not of type %r" % u'string',
"validation error (clusters.foo[1].max_concurrent_builds): "
"'one' is not of type %r" % u'integer',
re.compile(r"validation error \(clusters\.foo\[3\]\.max_concurrent_builds\): -1(\.0)?"
r" is less than the minimum of 0"),
]),
("""\
version: 1
clusters:
foo:
- name: blah
max_concurrent_builds: 1
enabled: never
""", [
"validation error (clusters.foo[0].enabled): "
"'never' is not of type %r" % u'boolean',
]),
("""\
version: 1
clusters:
foo:
# missing name
- nam: bar
max_concurrent_builds: 1
# missing max_concurrent_builds
- name: baz
max_concurrrent_builds: 2
- name: bar
max_concurrent_builds: 4
extra: false
""", [
"validation error (clusters.foo[0]): "
"%r is a required property" % u'name',
"validation error (clusters.foo[1]): "
"%r is a required property" % u'max_concurrent_builds',
"validation error (clusters.foo[2]): "
"Additional properties are not allowed ('extra' was unexpected)",
])
])
def test_bad_cluster_config(self, tmpdir, caplog, reactor_config_map,
config, errors):
if reactor_config_map:
os.environ['REACTOR_CONFIG'] = dedent(config)
else:
filename = os.path.join(str(tmpdir), 'config.yaml')
with open(filename, 'w') as fp:
fp.write(dedent(config))
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
with caplog.atLevel(logging.ERROR), pytest.raises(ValidationError):
plugin.run()
os.environ.pop('REACTOR_CONFIG', None)
captured_errs = [x.message for x in caplog.records()]
for error in errors:
try:
# Match regexp
assert any(filter(error.match, captured_errs))
except AttributeError:
# String comparison
assert error in captured_errs
def test_bad_version(self, tmpdir):
filename = os.path.join(str(tmpdir), 'config.yaml')
with open(filename, 'w') as fp:
fp.write("version: 2")
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
with pytest.raises(ValueError):
plugin.run()
@pytest.mark.parametrize(('config', 'clusters'), [ # noqa:F811
# Empty config
("", []),
# Built-in default config
(yaml.dump(ReactorConfig.DEFAULT_CONFIG), []),
# Unknown key
("""\
version: 1
special: foo
""", []),
("""\
version: 1
clusters:
ignored:
- name: foo
max_concurrent_builds: 2
platform:
- name: one
max_concurrent_builds: 4
- name: two
max_concurrent_builds: 8
enabled: true
- name: three
max_concurrent_builds: 16
enabled: false
""", [
('one', 4),
('two', 8),
]),
])
def test_good_cluster_config(self, tmpdir, reactor_config_map, config, clusters):
if reactor_config_map and config:
os.environ['REACTOR_CONFIG'] = dedent(config)
else:
filename = os.path.join(str(tmpdir), 'config.yaml')
with open(filename, 'w') as fp:
fp.write(dedent(config))
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
assert plugin.run() is None
os.environ.pop('REACTOR_CONFIG', None)
conf = get_config(workflow)
enabled = conf.get_enabled_clusters_for_platform('platform')
assert set([(x.name, x.max_concurrent_builds)
for x in enabled]) == set(clusters)
@pytest.mark.parametrize(('extra_config', 'fallback', 'error'), [ # noqa:F811
('clusters_client_config_dir: /the/path', None, None),
('clusters_client_config_dir: /the/path', '/unused/path', None),
(None, '/the/path', None),
(None, NO_FALLBACK, KeyError),
])
def test_cluster_client_config_path(self, tmpdir, reactor_config_map, extra_config, fallback,
error):
config = 'version: 1'
if extra_config:
config += '\n' + extra_config
if reactor_config_map and config:
os.environ['REACTOR_CONFIG'] = config
else:
filename = os.path.join(str(tmpdir), 'config.yaml')
with open(filename, 'w') as fp:
fp.write(config)
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
assert plugin.run() is None
os.environ.pop('REACTOR_CONFIG', None)
if error:
with pytest.raises(error):
get_clusters_client_config_path(workflow, fallback)
else:
path = get_clusters_client_config_path(workflow, fallback)
assert path == '/the/path/osbs.conf'
@pytest.mark.parametrize('default', (
'release',
'beta',
'unsigned',
))
def test_odcs_config(self, tmpdir, default):
filename = str(tmpdir.join('config.yaml'))
with open(filename, 'w') as fp:
fp.write(dedent("""\
version: 1
odcs:
signing_intents:
- name: release
keys: [R123, R234]
- name: beta
keys: [R123, B456, B457]
- name: unsigned
keys: []
default_signing_intent: {default}
api_url: http://odcs.example.com
auth:
ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret
""".format(default=default)))
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
assert plugin.run() is None
odcs_config = get_config(workflow).get_odcs_config()
assert odcs_config.default_signing_intent == default
unsigned_intent = {'name': 'unsigned', 'keys': [], 'restrictiveness': 0}
beta_intent = {'name': 'beta', 'keys': ['R123', 'B456', 'B457'], 'restrictiveness': 1}
release_intent = {'name': 'release', 'keys': ['R123', 'R234'], 'restrictiveness': 2}
assert odcs_config.signing_intents == [
unsigned_intent, beta_intent, release_intent
]
assert odcs_config.get_signing_intent_by_name('release') == release_intent
assert odcs_config.get_signing_intent_by_name('beta') == beta_intent
assert odcs_config.get_signing_intent_by_name('unsigned') == unsigned_intent
with pytest.raises(ValueError):
odcs_config.get_signing_intent_by_name('missing')
assert odcs_config.get_signing_intent_by_keys(['R123', 'R234'])['name'] == 'release'
assert odcs_config.get_signing_intent_by_keys('R123 R234')['name'] == 'release'
assert odcs_config.get_signing_intent_by_keys(['R123'])['name'] == 'release'
assert odcs_config.get_signing_intent_by_keys('R123')['name'] == 'release'
assert odcs_config.get_signing_intent_by_keys(['R123', 'B456'])['name'] == 'beta'
assert odcs_config.get_signing_intent_by_keys(['B456', 'R123'])['name'] == 'beta'
assert odcs_config.get_signing_intent_by_keys('B456 R123')['name'] == 'beta'
assert odcs_config.get_signing_intent_by_keys('R123 B456 ')['name'] == 'beta'
assert odcs_config.get_signing_intent_by_keys(['B456'])['name'] == 'beta'
assert odcs_config.get_signing_intent_by_keys('B456')['name'] == 'beta'
assert odcs_config.get_signing_intent_by_keys([])['name'] == 'unsigned'
assert odcs_config.get_signing_intent_by_keys('')['name'] == 'unsigned'
with pytest.raises(ValueError):
assert odcs_config.get_signing_intent_by_keys(['missing'])
with pytest.raises(ValueError):
assert odcs_config.get_signing_intent_by_keys(['R123', 'R234', 'B457'])
def test_odcs_config_invalid_default_signing_intent(self, tmpdir):
filename = str(tmpdir.join('config.yaml'))
with open(filename, 'w') as fp:
fp.write(dedent("""\
version: 1
odcs:
signing_intents:
- name: release
keys: [R123]
- name: beta
keys: [R123, B456]
- name: unsigned
keys: []
default_signing_intent: spam
api_url: http://odcs.example.com
auth:
ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret
"""))
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
assert plugin.run() is None
with pytest.raises(ValueError) as exc_info:
get_config(workflow).get_odcs_config()
message = str(exc_info.value)
assert message == dedent("""\
unknown signing intent name "spam", valid names: unsigned, beta, release
""".rstrip())
@pytest.mark.parametrize('fallback', (True, False, None))
@pytest.mark.parametrize('method', [
'koji', 'pulp', 'odcs', 'smtp', 'arrangement_version',
'artifacts_allowed_domains', 'image_labels',
'image_label_info_url_format', 'image_equal_labels',
'openshift', 'group_manifests', 'platform_descriptors', 'prefer_schema1_digest',
'content_versions', 'registries', 'yum_proxy', 'source_registry', 'sources_command',
'required_secrets', 'worker_token_secrets', 'clusters',
])
def test_get_methods(self, fallback, method):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
if fallback is False:
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] = \
ReactorConfig(yaml.safe_load(REACTOR_CONFIG_MAP))
else:
if fallback:
fall_source = ReactorConfig(yaml.safe_load(REACTOR_CONFIG_MAP))
else:
fall_source = ReactorConfig(yaml.safe_load("version: 1"))
method_name = 'get_' + method
real_method = getattr(atomic_reactor.plugins.pre_reactor_config, method_name)
if fallback is True:
output = real_method(workflow, fall_source.conf[method])
else:
if fallback is False:
output = real_method(workflow)
else:
with pytest.raises(KeyError):
real_method(workflow)
return
expected = yaml.safe_load(REACTOR_CONFIG_MAP)[method]
if method == 'registries':
registries_cm = {}
for registry in expected:
reguri = RegistryURI(registry.get('url'))
regdict = {}
regdict['version'] = reguri.version
if registry.get('auth'):
regdict['secret'] = registry['auth']['cfg_path']
regdict['insecure'] = registry.get('insecure', False)
regdict['expected_media_types'] = registry.get('expected_media_types', [])
registries_cm[reguri.docker_uri] = regdict
if fallback:
output = real_method(workflow, registries_cm)
assert output == registries_cm
return
if method == 'source_registry':
expect = {
'uri': RegistryURI(expected['url']),
'insecure': expected.get('insecure', False)
}
if fallback:
output = real_method(workflow, expect)
assert output['insecure'] == expect['insecure']
assert output['uri'].uri == expect['uri'].uri
return
assert output == expected
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize(('config', 'expect'), [
("""\
version: 1
platform_descriptors:
- platform: x86_64
architecture: amd64
""",
{'x86_64': 'amd64',
'ppc64le': 'ppc64le'}),
])
def test_get_platform_to_goarch_mapping(self, fallback, config, expect):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
config_json = read_yaml(config, 'schemas/config.json')
workspace = workflow.plugin_workspace[ReactorConfigPlugin.key]
workspace[WORKSPACE_CONF_KEY] = ReactorConfig(config_json)
kwargs = {}
if fallback:
kwargs['descriptors_fallback'] = {'x86_64': 'amd64'}
platform_to_goarch = get_platform_to_goarch_mapping(workflow, **kwargs)
goarch_to_platform = get_goarch_to_platform_mapping(workflow, **kwargs)
for plat, goarch in expect.items():
assert platform_to_goarch[plat] == goarch
assert goarch_to_platform[goarch] == plat
@pytest.mark.parametrize(('config', 'expect'), [
("""\
version: 1
default_image_build_method: imagebuilder
""",
"imagebuilder"),
("""\
version: 1
""",
CONTAINER_DEFAULT_BUILD_METHOD),
])
def test_get_default_image_build_method(self, config, expect):
config_json = read_yaml(config, 'schemas/config.json')
_, workflow = self.prepare()
workspace = workflow.plugin_workspace.setdefault(ReactorConfigPlugin.key, {})
workspace[WORKSPACE_CONF_KEY] = ReactorConfig(config_json)
method = get_default_image_build_method(workflow)
assert method == expect
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize(('config', 'expect'), [
("""\
version: 1
build_image_override:
ppc64le: registry.example.com/buildroot-ppc64le:latest
arm: registry.example.com/buildroot-arm:latest
""",
{'ppc64le': 'registry.example.com/buildroot-ppc64le:latest',
'arm': 'registry.example.com/buildroot-arm:latest'}),
])
def test_get_build_image_override(self, fallback, config, expect):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
config_json = read_yaml(config, 'schemas/config.json')
workspace = workflow.plugin_workspace[ReactorConfigPlugin.key]
workspace[WORKSPACE_CONF_KEY] = ReactorConfig(config_json)
kwargs = {}
if fallback:
kwargs['fallback'] = expect
build_image_override = get_build_image_override(workflow, **kwargs)
assert build_image_override == expect
@pytest.mark.parametrize(('config', 'fallback', 'expect'), [
("""\
version: 1
flatpak:
base_image: fedora:latest
""",
"x", "fedora:latest"),
("""\
version: 1
flatpak: {}
""",
"x", "x"),
("""\
version: 1
""",
"x", "x"),
("""\
version: 1
""",
None, None),
("""\
version: 1
flatpak: {}
""",
None, None),
])
def test_get_flatpak_base_image(self, config, fallback, expect):
config_json = read_yaml(config, 'schemas/config.json')
_, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {
WORKSPACE_CONF_KEY: ReactorConfig(config_json)
}
kwargs = {}
if fallback:
kwargs['fallback'] = fallback
if expect:
base_image = get_flatpak_base_image(workflow, **kwargs)
assert base_image == expect
else:
with pytest.raises(KeyError):
get_flatpak_base_image(workflow, **kwargs)
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize(('config', 'raise_error'), [
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
krb_principal: krb_principal
krb_keytab_path: /tmp/krb_keytab
""", False),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
ssl_certs_dir: /var/certs
""", False),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
""", False),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
""", True),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
krb_principal: krb_principal
krb_keytab_path: /tmp/krb_keytab
ssl_certs_dir: /var/certs
""", True),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
krb_keytab_path: /tmp/krb_keytab
""", True),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
krb_principal: krb_principal
""", True),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
krb_principal: krb_principal
ssl_certs_dir: /var/certs
""", True),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
krb_keytab_path: /tmp/krb_keytab
ssl_certs_dir: /var/certs
""", True),
])
def test_get_koji_session(self, fallback, config, raise_error):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
if raise_error:
with pytest.raises(Exception):
read_yaml(config, 'schemas/config.json')
return
config_json = read_yaml(config, 'schemas/config.json')
auth_info = {
"proxyuser": config_json['koji']['auth'].get('proxyuser'),
"ssl_certs_dir": config_json['koji']['auth'].get('ssl_certs_dir'),
"krb_principal": config_json['koji']['auth'].get('krb_principal'),
"krb_keytab": config_json['koji']['auth'].get('krb_keytab_path')
}
fallback_map = {}
if fallback:
fallback_map = {'auth': deepcopy(auth_info), 'hub_url': config_json['koji']['hub_url']}
fallback_map['auth']['krb_keytab_path'] = fallback_map['auth'].pop('krb_keytab')
else:
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] = \
ReactorConfig(config_json)
(flexmock(atomic_reactor.koji_util)
.should_receive('create_koji_session')
.with_args(config_json['koji']['hub_url'], auth_info)
.once()
.and_return(True))
get_koji_session(workflow, fallback_map)
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize('root_url', (
'https://koji.example.com/root',
'https://koji.example.com/root/',
None
))
def test_get_koji_path_info(self, fallback, root_url):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
config = {
'version': 1,
'koji': {
'hub_url': 'https://koji.example.com/hub',
'auth': {
'ssl_certs_dir': '/var/certs'
}
}
}
expected_root_url = 'https://koji.example.com/root'
if root_url:
config['koji']['root_url'] = root_url
config_yaml = yaml.safe_dump(config)
expect_error = not root_url
if expect_error:
with pytest.raises(Exception):
read_yaml(config_yaml, 'schemas/config.json')
return
parsed_config = read_yaml(config_yaml, 'schemas/config.json')
fallback_map = {}
if fallback:
fallback_map = deepcopy(config['koji'])
else:
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] = \
ReactorConfig(parsed_config)
(flexmock(koji.PathInfo)
.should_receive('__init__')
.with_args(topdir=expected_root_url)
.once())
get_koji_path_info(workflow, fallback_map)
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize(('config', 'raise_error'), [
("""\
version: 1
pulp:
name: my-pulp
auth:
password: testpasswd
username: testuser
""", False),
("""\
version: 1
pulp:
name: my-pulp
auth:
ssl_certs_dir: /var/certs
""", False),
("""\
version: 1
pulp:
name: my-pulp
auth:
ssl_certs_dir: /var/certs
password: testpasswd
username: testuser
""", True),
("""\
version: 1
pulp:
name: my-pulp
auth:
ssl_certs_dir: /var/certs
password: testpasswd
""", True),
("""\
version: 1
pulp:
name: my-pulp
auth:
ssl_certs_dir: /var/certs
username: testuser
""", True),
("""\
version: 1
pulp:
name: my-pulp
auth:
username: testuser
""", True),
("""\
version: 1
pulp:
name: my-pulp
auth:
password: testpasswd
""", True),
])
def test_get_pulp_session(self, fallback, config, raise_error):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
if raise_error:
with pytest.raises(Exception):
read_yaml(config, 'schemas/config.json')
return
config_json = read_yaml(config, 'schemas/config.json')
auth_info = {
"pulp_secret_path": config_json['pulp']['auth'].get('ssl_certs_dir'),
"username": config_json['pulp']['auth'].get('username'),
"password": config_json['pulp']['auth'].get('password'),
"dockpulp_loglevel": None
}
fallback_map = {}
if fallback:
fallback_map = {'auth': deepcopy(auth_info), 'name': config_json['pulp']['name']}
fallback_map['auth']['ssl_certs_dir'] = fallback_map['auth'].pop('pulp_secret_path')
else:
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\
ReactorConfig(config_json)
(flexmock(atomic_reactor.pulp_util.PulpHandler)
.should_receive('__init__')
.with_args(workflow, config_json['pulp']['name'], 'logger', **auth_info)
.once()
.and_return(None))
get_pulp_session(workflow, 'logger', fallback_map)
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize(('config', 'raise_error'), [
("""\
version: 1
odcs:
api_url: https://odcs.example.com/api/1
auth:
ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret
signing_intents:
- name: release
keys: [R123]
default_signing_intent: default
""", False),
("""\
version: 1
odcs:
api_url: https://odcs.example.com/api/1
auth:
ssl_certs_dir: nonexistent
signing_intents:
- name: release
keys: [R123]
default_signing_intent: default
""", False),
("""\
version: 1
odcs:
api_url: https://odcs.example.com/api/1
auth:
openidc_dir: /var/run/open_idc
signing_intents:
- name: release
keys: [R123]
default_signing_intent: default
""", False),
("""\
version: 1
odcs:
api_url: https://odcs.example.com/api/1
auth:
openidc_dir: /var/run/open_idc
ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret
signing_intents:
- name: release
keys: [R123]
default_signing_intent: default
""", True),
("""\
version: 1
odcs:
api_url: https://odcs.example.com/api/1
auth:
openidc_dir: /var/run/open_idc
signing_intents:
- name: release
keys: [R123]
""", True),
("""\
version: 1
odcs:
api_url: https://odcs.example.com/api/1
auth:
openidc_dir: /var/run/open_idc
default_signing_intent: default
""", True),
("""\
version: 1
odcs:
auth:
openidc_dir: /var/run/open_idc
signing_intents:
- name: release
keys: [R123]
default_signing_intent: default
""", True),
])
def test_get_odcs_session(self, tmpdir, fallback, config, raise_error):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
if raise_error:
with pytest.raises(Exception):
read_yaml(config, 'schemas/config.json')
return
config_json = read_yaml(config, 'schemas/config.json')
auth_info = {'insecure': config_json['odcs'].get('insecure', False)}
if 'openidc_dir' in config_json['odcs']['auth']:
config_json['odcs']['auth']['openidc_dir'] = str(tmpdir)
filename = str(tmpdir.join('token'))
with open(filename, 'w') as fp:
fp.write("my_token")
auth_info['token'] = "my_token"
ssl_dir_raise = False
if 'ssl_certs_dir' in config_json['odcs']['auth']:
if config_json['odcs']['auth']['ssl_certs_dir'] != "nonexistent":
config_json['odcs']['auth']['ssl_certs_dir'] = str(tmpdir)
filename = str(tmpdir.join('cert'))
with open(filename, 'w') as fp:
fp.write("my_cert")
auth_info['cert'] = filename
else:
ssl_dir_raise = True
fallback_map = {}
if fallback:
fallback_map = {'auth': deepcopy(auth_info),
'api_url': config_json['odcs']['api_url']}
fallback_map['auth']['ssl_certs_dir'] = config_json['odcs']['auth'].get('ssl_certs_dir')
fallback_map['auth']['openidc_dir'] = config_json['odcs']['auth'].get('openidc_dir')
else:
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\
ReactorConfig(config_json)
if not ssl_dir_raise:
(flexmock(atomic_reactor.odcs_util.ODCSClient)
.should_receive('__init__')
.with_args(config_json['odcs']['api_url'], **auth_info)
.once()
.and_return(None))
get_odcs_session(workflow, fallback_map)
else:
with pytest.raises(KeyError):
get_odcs_session(workflow, fallback_map)
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize(('config', 'raise_error'), [
("""\
version: 1
smtp:
host: smtp.example.com
from_address: osbs@example.com
""", False),
("""\
version: 1
smtp:
from_address: osbs@example.com
""", True),
("""\
version: 1
smtp:
host: smtp.example.com
""", True),
("""\
version: 1
smtp:
""", True),
])
def test_get_smtp_session(self, fallback, config, raise_error):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
if raise_error:
with pytest.raises(Exception):
read_yaml(config, 'schemas/config.json')
return
config_json = read_yaml(config, 'schemas/config.json')
fallback_map = {}
if fallback:
fallback_map['host'] = config_json['smtp']['host']
else:
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\
ReactorConfig(config_json)
(flexmock(smtplib.SMTP)
.should_receive('__init__')
.with_args(config_json['smtp']['host'])
.once()
.and_return(None))
get_smtp_session(workflow, fallback_map)
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize('build_json_dir', [
None, "/tmp/build_json_dir",
])
@pytest.mark.parametrize(('config', 'raise_error'), [
("""\
version: 1
openshift:
url: https://openshift.example.com
auth:
ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret
""", False),
("""\
version: 1
openshift:
url: https://openshift.example.com
""", False),
("""\
version: 1
openshift:
url: https://openshift.example.com
auth:
krb_principal: principal
krb_keytab_path: /var/keytab
""", False),
("""\
version: 1
openshift:
url: https://openshift.example.com
auth:
krb_principal: principal
krb_keytab_path: /var/keytab
krb_cache_path: /var/krb/cache
""", False),
("""\
version: 1
openshift:
url: https://openshift.example.com
auth:
enable: True
""", False),
("""\
version: 1
openshift:
url: https://openshift.example.com
auth:
krb_keytab_path: /var/keytab
""", True),
("""\
version: 1
openshift:
url: https://openshift.example.com
auth:
krb_principal: principal
""", True),
("""\
version: 1
openshift:
auth:
ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret
""", True),
("""\
version: 1
openshift:
auth:
krb_principal: principal
krb_keytab_path: /var/keytab
""", True),
("""\
version: 1
openshift:
url: https://openshift.example.com
auth:
""", True),
("""\
version: 1
openshift:
auth:
ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret
""", True),
])
def test_get_openshift_session(self, fallback, build_json_dir, config, raise_error):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
if build_json_dir:
config += " build_json_dir: " + build_json_dir
if raise_error:
with pytest.raises(Exception):
read_yaml(config, 'schemas/config.json')
return
config_json = read_yaml(config, 'schemas/config.json')
auth_info = {
'openshift_url': config_json['openshift']['url'],
'verify_ssl': not config_json['openshift'].get('insecure', False),
'use_auth': False,
'conf_file': None,
'namespace': 'namespace',
'build_json_dir': build_json_dir
}
if config_json['openshift'].get('auth'):
if config_json['openshift']['auth'].get('krb_keytab_path'):
auth_info['kerberos_keytab'] =\
config_json['openshift']['auth'].get('krb_keytab_path')
if config_json['openshift']['auth'].get('krb_principal'):
auth_info['kerberos_principal'] =\
config_json['openshift']['auth'].get('krb_principal')
if config_json['openshift']['auth'].get('krb_cache_path'):
auth_info['kerberos_ccache'] =\
config_json['openshift']['auth'].get('krb_cache_path')
if config_json['openshift']['auth'].get('ssl_certs_dir'):
auth_info['client_cert'] =\
os.path.join(config_json['openshift']['auth'].get('ssl_certs_dir'), 'cert')
auth_info['client_key'] =\
os.path.join(config_json['openshift']['auth'].get('ssl_certs_dir'), 'key')
auth_info['use_auth'] = config_json['openshift']['auth'].get('enable', False)
fallback_map = {}
if fallback:
fallback_map = {'url': config_json['openshift']['url'],
'insecure': config_json['openshift'].get('insecure', False),
'build_json_dir': build_json_dir}
if config_json['openshift'].get('auth'):
fallback_map['auth'] = {}
fallback_map['auth']['krb_keytab_path'] =\
config_json['openshift']['auth'].get('krb_keytab_path')
fallback_map['auth']['krb_principal'] =\
config_json['openshift']['auth'].get('krb_principal')
fallback_map['auth']['enable'] =\
config_json['openshift']['auth'].get('enable', False)
fallback_map['auth']['krb_cache_path'] =\
config_json['openshift']['auth'].get('krb_cache_path')
fallback_map['auth']['ssl_certs_dir'] =\
config_json['openshift']['auth'].get('ssl_certs_dir')
else:
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\
ReactorConfig(config_json)
(flexmock(osbs.conf.Configuration)
.should_call('__init__')
.with_args(**auth_info)
.once())
(flexmock(osbs.api.OSBS)
.should_call('__init__')
.once())
flexmock(os, environ={'BUILD': '{"metadata": {"namespace": "namespace"}}'})
get_openshift_session(workflow, fallback_map)
| 35.092155 | 100 | 0.532924 | from __future__ import unicode_literals
from jsonschema import ValidationError
import io
import logging
import os
import pkg_resources
import pytest
from textwrap import dedent
import re
import yaml
import smtplib
from copy import deepcopy
import atomic_reactor
import koji
from atomic_reactor.core import DockerTasker
from atomic_reactor.inner import DockerBuildWorkflow
from atomic_reactor.util import read_yaml
import atomic_reactor.koji_util
import atomic_reactor.pulp_util
import atomic_reactor.odcs_util
import osbs.conf
import osbs.api
from osbs.utils import RegistryURI
from atomic_reactor.plugins.pre_reactor_config import (ReactorConfig,
ReactorConfigPlugin,
get_config, WORKSPACE_CONF_KEY,
get_koji_session,
get_koji_path_info,
get_pulp_session,
get_odcs_session,
get_smtp_session,
get_openshift_session,
get_clusters_client_config_path,
get_docker_registry,
get_platform_to_goarch_mapping,
get_goarch_to_platform_mapping,
get_default_image_build_method,
get_flatpak_base_image,
CONTAINER_DEFAULT_BUILD_METHOD,
get_build_image_override,
NO_FALLBACK)
from tests.constants import TEST_IMAGE, REACTOR_CONFIG_MAP
from tests.docker_mock import mock_docker
from tests.fixtures import reactor_config_map
from flexmock import flexmock
class TestReactorConfigPlugin(object):
def prepare(self):
mock_docker()
tasker = DockerTasker()
workflow = DockerBuildWorkflow({'provider': 'git', 'uri': 'asd'},
TEST_IMAGE)
return tasker, workflow
@pytest.mark.parametrize(('fallback'), [
False,
True
])
@pytest.mark.parametrize(('config', 'valid'), [
("""\
version: 1
registries:
- url: https://container-registry.example.com/v2
auth:
cfg_path: /var/run/secrets/atomic-reactor/v2-registry-dockercfg
""",
True),
("""\
version: 1
registries:
- url: https://old-container-registry.example.com/v1
auth:
cfg_path: /var/run/secrets/atomic-reactor/v1-registry-dockercfg
- url: https://container-registry.example.com/v2
auth:
cfg_path: /var/run/secrets/atomic-reactor/v2-registry-dockercfg
""",
True),
("""\
version: 1
registries:
- url: https://old-container-registry.example.com/v1
auth:
cfg_path: /var/run/secrets/atomic-reactor/v1-registry-dockercfg
""",
False),
])
def test_get_docker_registry(self, config, fallback, valid):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
config_json = read_yaml(config, 'schemas/config.json')
docker_reg = {
'version': 'v2',
'insecure': False,
'secret': '/var/run/secrets/atomic-reactor/v2-registry-dockercfg',
'url': 'https://container-registry.example.com/v2',
}
if fallback:
if valid:
docker_fallback = docker_reg
expected = docker_reg
else:
docker_fallback = NO_FALLBACK
else:
docker_fallback = {}
expected = {
'url': 'https://container-registry.example.com',
'insecure': False,
'secret': '/var/run/secrets/atomic-reactor/v2-registry-dockercfg'
}
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\
ReactorConfig(config_json)
if valid:
docker_registry = get_docker_registry(workflow, docker_fallback)
assert docker_registry == expected
else:
if fallback:
with pytest.raises(KeyError):
get_docker_registry(workflow, docker_fallback)
else:
with pytest.raises(RuntimeError):
get_docker_registry(workflow, docker_fallback)
def test_no_config(self):
tasker, workflow = self.prepare()
conf = get_config(workflow)
assert isinstance(conf, ReactorConfig)
same_conf = get_config(workflow)
assert conf is same_conf
@pytest.mark.parametrize('basename', ['reactor-config.yaml', None])
def test_filename(self, tmpdir, basename):
filename = os.path.join(str(tmpdir), basename or 'config.yaml')
with open(filename, 'w'):
pass
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow,
config_path=str(tmpdir),
basename=filename)
assert plugin.run() is None
def test_filename_not_found(self):
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path='/not-found')
with pytest.raises(Exception):
plugin.run()
def test_no_schema_resource(self, tmpdir, caplog):
class FakeProvider(object):
def get_resource_stream(self, pkg, rsc):
raise IOError
(flexmock(pkg_resources)
.should_receive('get_provider')
.and_return(FakeProvider()))
filename = os.path.join(str(tmpdir), 'config.yaml')
with open(filename, 'w'):
pass
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
with caplog.atLevel(logging.ERROR), pytest.raises(Exception):
plugin.run()
captured_errs = [x.message for x in caplog.records()]
assert "unable to extract JSON schema, cannot validate" in captured_errs
@pytest.mark.parametrize('schema', [
'{',
'{"properties": {"any": null}}',
])
def test_invalid_schema_resource(self, tmpdir, caplog, schema):
class FakeProvider(object):
def get_resource_stream(self, pkg, rsc):
return io.BufferedReader(io.BytesIO(schema))
(flexmock(pkg_resources)
.should_receive('get_provider')
.and_return(FakeProvider()))
filename = os.path.join(str(tmpdir), 'config.yaml')
with open(filename, 'w'):
pass
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
with caplog.atLevel(logging.ERROR), pytest.raises(Exception):
plugin.run()
captured_errs = [x.message for x in caplog.records()]
assert any("cannot validate" in x for x in captured_errs)
@pytest.mark.parametrize(('config', 'errors'), [
("""\
clusters:
foo:
- name: bar
max_concurrent_builds: 1
""", [
"validation error (at top level): "
"%r is a required property" % u'version',
]),
("""\
version: 1
clusters:
foo:
bar: 1
plat/form:
- name: foo
max_concurrent_builds: 1
""", [
"validation error (clusters.foo): None is not of type %r" % u'array',
"validation error (clusters.bar): 1 is not of type %r" % u'array',
re.compile(r"validation error \(clusters\): .*'plat/form'"),
]),
("""\
version: 1
clusters:
foo:
- name: 1
max_concurrent_builds: 1
- name: blah
max_concurrent_builds: one
- name: "2" # quoting prevents error
max_concurrent_builds: 2
- name: negative
max_concurrent_builds: -1
""", [
"validation error (clusters.foo[0].name): "
"1 is not of type %r" % u'string',
"validation error (clusters.foo[1].max_concurrent_builds): "
"'one' is not of type %r" % u'integer',
re.compile(r"validation error \(clusters\.foo\[3\]\.max_concurrent_builds\): -1(\.0)?"
r" is less than the minimum of 0"),
]),
("""\
version: 1
clusters:
foo:
- name: blah
max_concurrent_builds: 1
enabled: never
""", [
"validation error (clusters.foo[0].enabled): "
"'never' is not of type %r" % u'boolean',
]),
("""\
version: 1
clusters:
foo:
# missing name
- nam: bar
max_concurrent_builds: 1
# missing max_concurrent_builds
- name: baz
max_concurrrent_builds: 2
- name: bar
max_concurrent_builds: 4
extra: false
""", [
"validation error (clusters.foo[0]): "
"%r is a required property" % u'name',
"validation error (clusters.foo[1]): "
"%r is a required property" % u'max_concurrent_builds',
"validation error (clusters.foo[2]): "
"Additional properties are not allowed ('extra' was unexpected)",
])
])
def test_bad_cluster_config(self, tmpdir, caplog, reactor_config_map,
config, errors):
if reactor_config_map:
os.environ['REACTOR_CONFIG'] = dedent(config)
else:
filename = os.path.join(str(tmpdir), 'config.yaml')
with open(filename, 'w') as fp:
fp.write(dedent(config))
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
with caplog.atLevel(logging.ERROR), pytest.raises(ValidationError):
plugin.run()
os.environ.pop('REACTOR_CONFIG', None)
captured_errs = [x.message for x in caplog.records()]
for error in errors:
try:
assert any(filter(error.match, captured_errs))
except AttributeError:
assert error in captured_errs
def test_bad_version(self, tmpdir):
filename = os.path.join(str(tmpdir), 'config.yaml')
with open(filename, 'w') as fp:
fp.write("version: 2")
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
with pytest.raises(ValueError):
plugin.run()
@pytest.mark.parametrize(('config', 'clusters'), [
("", []),
(yaml.dump(ReactorConfig.DEFAULT_CONFIG), []),
("""\
version: 1
special: foo
""", []),
("""\
version: 1
clusters:
ignored:
- name: foo
max_concurrent_builds: 2
platform:
- name: one
max_concurrent_builds: 4
- name: two
max_concurrent_builds: 8
enabled: true
- name: three
max_concurrent_builds: 16
enabled: false
""", [
('one', 4),
('two', 8),
]),
])
def test_good_cluster_config(self, tmpdir, reactor_config_map, config, clusters):
if reactor_config_map and config:
os.environ['REACTOR_CONFIG'] = dedent(config)
else:
filename = os.path.join(str(tmpdir), 'config.yaml')
with open(filename, 'w') as fp:
fp.write(dedent(config))
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
assert plugin.run() is None
os.environ.pop('REACTOR_CONFIG', None)
conf = get_config(workflow)
enabled = conf.get_enabled_clusters_for_platform('platform')
assert set([(x.name, x.max_concurrent_builds)
for x in enabled]) == set(clusters)
@pytest.mark.parametrize(('extra_config', 'fallback', 'error'), [
('clusters_client_config_dir: /the/path', None, None),
('clusters_client_config_dir: /the/path', '/unused/path', None),
(None, '/the/path', None),
(None, NO_FALLBACK, KeyError),
])
def test_cluster_client_config_path(self, tmpdir, reactor_config_map, extra_config, fallback,
error):
config = 'version: 1'
if extra_config:
config += '\n' + extra_config
if reactor_config_map and config:
os.environ['REACTOR_CONFIG'] = config
else:
filename = os.path.join(str(tmpdir), 'config.yaml')
with open(filename, 'w') as fp:
fp.write(config)
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
assert plugin.run() is None
os.environ.pop('REACTOR_CONFIG', None)
if error:
with pytest.raises(error):
get_clusters_client_config_path(workflow, fallback)
else:
path = get_clusters_client_config_path(workflow, fallback)
assert path == '/the/path/osbs.conf'
@pytest.mark.parametrize('default', (
'release',
'beta',
'unsigned',
))
def test_odcs_config(self, tmpdir, default):
filename = str(tmpdir.join('config.yaml'))
with open(filename, 'w') as fp:
fp.write(dedent("""\
version: 1
odcs:
signing_intents:
- name: release
keys: [R123, R234]
- name: beta
keys: [R123, B456, B457]
- name: unsigned
keys: []
default_signing_intent: {default}
api_url: http://odcs.example.com
auth:
ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret
""".format(default=default)))
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
assert plugin.run() is None
odcs_config = get_config(workflow).get_odcs_config()
assert odcs_config.default_signing_intent == default
unsigned_intent = {'name': 'unsigned', 'keys': [], 'restrictiveness': 0}
beta_intent = {'name': 'beta', 'keys': ['R123', 'B456', 'B457'], 'restrictiveness': 1}
release_intent = {'name': 'release', 'keys': ['R123', 'R234'], 'restrictiveness': 2}
assert odcs_config.signing_intents == [
unsigned_intent, beta_intent, release_intent
]
assert odcs_config.get_signing_intent_by_name('release') == release_intent
assert odcs_config.get_signing_intent_by_name('beta') == beta_intent
assert odcs_config.get_signing_intent_by_name('unsigned') == unsigned_intent
with pytest.raises(ValueError):
odcs_config.get_signing_intent_by_name('missing')
assert odcs_config.get_signing_intent_by_keys(['R123', 'R234'])['name'] == 'release'
assert odcs_config.get_signing_intent_by_keys('R123 R234')['name'] == 'release'
assert odcs_config.get_signing_intent_by_keys(['R123'])['name'] == 'release'
assert odcs_config.get_signing_intent_by_keys('R123')['name'] == 'release'
assert odcs_config.get_signing_intent_by_keys(['R123', 'B456'])['name'] == 'beta'
assert odcs_config.get_signing_intent_by_keys(['B456', 'R123'])['name'] == 'beta'
assert odcs_config.get_signing_intent_by_keys('B456 R123')['name'] == 'beta'
assert odcs_config.get_signing_intent_by_keys('R123 B456 ')['name'] == 'beta'
assert odcs_config.get_signing_intent_by_keys(['B456'])['name'] == 'beta'
assert odcs_config.get_signing_intent_by_keys('B456')['name'] == 'beta'
assert odcs_config.get_signing_intent_by_keys([])['name'] == 'unsigned'
assert odcs_config.get_signing_intent_by_keys('')['name'] == 'unsigned'
with pytest.raises(ValueError):
assert odcs_config.get_signing_intent_by_keys(['missing'])
with pytest.raises(ValueError):
assert odcs_config.get_signing_intent_by_keys(['R123', 'R234', 'B457'])
def test_odcs_config_invalid_default_signing_intent(self, tmpdir):
filename = str(tmpdir.join('config.yaml'))
with open(filename, 'w') as fp:
fp.write(dedent("""\
version: 1
odcs:
signing_intents:
- name: release
keys: [R123]
- name: beta
keys: [R123, B456]
- name: unsigned
keys: []
default_signing_intent: spam
api_url: http://odcs.example.com
auth:
ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret
"""))
tasker, workflow = self.prepare()
plugin = ReactorConfigPlugin(tasker, workflow, config_path=str(tmpdir))
assert plugin.run() is None
with pytest.raises(ValueError) as exc_info:
get_config(workflow).get_odcs_config()
message = str(exc_info.value)
assert message == dedent("""\
unknown signing intent name "spam", valid names: unsigned, beta, release
""".rstrip())
@pytest.mark.parametrize('fallback', (True, False, None))
@pytest.mark.parametrize('method', [
'koji', 'pulp', 'odcs', 'smtp', 'arrangement_version',
'artifacts_allowed_domains', 'image_labels',
'image_label_info_url_format', 'image_equal_labels',
'openshift', 'group_manifests', 'platform_descriptors', 'prefer_schema1_digest',
'content_versions', 'registries', 'yum_proxy', 'source_registry', 'sources_command',
'required_secrets', 'worker_token_secrets', 'clusters',
])
def test_get_methods(self, fallback, method):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
if fallback is False:
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] = \
ReactorConfig(yaml.safe_load(REACTOR_CONFIG_MAP))
else:
if fallback:
fall_source = ReactorConfig(yaml.safe_load(REACTOR_CONFIG_MAP))
else:
fall_source = ReactorConfig(yaml.safe_load("version: 1"))
method_name = 'get_' + method
real_method = getattr(atomic_reactor.plugins.pre_reactor_config, method_name)
if fallback is True:
output = real_method(workflow, fall_source.conf[method])
else:
if fallback is False:
output = real_method(workflow)
else:
with pytest.raises(KeyError):
real_method(workflow)
return
expected = yaml.safe_load(REACTOR_CONFIG_MAP)[method]
if method == 'registries':
registries_cm = {}
for registry in expected:
reguri = RegistryURI(registry.get('url'))
regdict = {}
regdict['version'] = reguri.version
if registry.get('auth'):
regdict['secret'] = registry['auth']['cfg_path']
regdict['insecure'] = registry.get('insecure', False)
regdict['expected_media_types'] = registry.get('expected_media_types', [])
registries_cm[reguri.docker_uri] = regdict
if fallback:
output = real_method(workflow, registries_cm)
assert output == registries_cm
return
if method == 'source_registry':
expect = {
'uri': RegistryURI(expected['url']),
'insecure': expected.get('insecure', False)
}
if fallback:
output = real_method(workflow, expect)
assert output['insecure'] == expect['insecure']
assert output['uri'].uri == expect['uri'].uri
return
assert output == expected
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize(('config', 'expect'), [
("""\
version: 1
platform_descriptors:
- platform: x86_64
architecture: amd64
""",
{'x86_64': 'amd64',
'ppc64le': 'ppc64le'}),
])
def test_get_platform_to_goarch_mapping(self, fallback, config, expect):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
config_json = read_yaml(config, 'schemas/config.json')
workspace = workflow.plugin_workspace[ReactorConfigPlugin.key]
workspace[WORKSPACE_CONF_KEY] = ReactorConfig(config_json)
kwargs = {}
if fallback:
kwargs['descriptors_fallback'] = {'x86_64': 'amd64'}
platform_to_goarch = get_platform_to_goarch_mapping(workflow, **kwargs)
goarch_to_platform = get_goarch_to_platform_mapping(workflow, **kwargs)
for plat, goarch in expect.items():
assert platform_to_goarch[plat] == goarch
assert goarch_to_platform[goarch] == plat
@pytest.mark.parametrize(('config', 'expect'), [
("""\
version: 1
default_image_build_method: imagebuilder
""",
"imagebuilder"),
("""\
version: 1
""",
CONTAINER_DEFAULT_BUILD_METHOD),
])
def test_get_default_image_build_method(self, config, expect):
config_json = read_yaml(config, 'schemas/config.json')
_, workflow = self.prepare()
workspace = workflow.plugin_workspace.setdefault(ReactorConfigPlugin.key, {})
workspace[WORKSPACE_CONF_KEY] = ReactorConfig(config_json)
method = get_default_image_build_method(workflow)
assert method == expect
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize(('config', 'expect'), [
("""\
version: 1
build_image_override:
ppc64le: registry.example.com/buildroot-ppc64le:latest
arm: registry.example.com/buildroot-arm:latest
""",
{'ppc64le': 'registry.example.com/buildroot-ppc64le:latest',
'arm': 'registry.example.com/buildroot-arm:latest'}),
])
def test_get_build_image_override(self, fallback, config, expect):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
config_json = read_yaml(config, 'schemas/config.json')
workspace = workflow.plugin_workspace[ReactorConfigPlugin.key]
workspace[WORKSPACE_CONF_KEY] = ReactorConfig(config_json)
kwargs = {}
if fallback:
kwargs['fallback'] = expect
build_image_override = get_build_image_override(workflow, **kwargs)
assert build_image_override == expect
@pytest.mark.parametrize(('config', 'fallback', 'expect'), [
("""\
version: 1
flatpak:
base_image: fedora:latest
""",
"x", "fedora:latest"),
("""\
version: 1
flatpak: {}
""",
"x", "x"),
("""\
version: 1
""",
"x", "x"),
("""\
version: 1
""",
None, None),
("""\
version: 1
flatpak: {}
""",
None, None),
])
def test_get_flatpak_base_image(self, config, fallback, expect):
config_json = read_yaml(config, 'schemas/config.json')
_, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {
WORKSPACE_CONF_KEY: ReactorConfig(config_json)
}
kwargs = {}
if fallback:
kwargs['fallback'] = fallback
if expect:
base_image = get_flatpak_base_image(workflow, **kwargs)
assert base_image == expect
else:
with pytest.raises(KeyError):
get_flatpak_base_image(workflow, **kwargs)
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize(('config', 'raise_error'), [
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
krb_principal: krb_principal
krb_keytab_path: /tmp/krb_keytab
""", False),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
ssl_certs_dir: /var/certs
""", False),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
""", False),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
""", True),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
krb_principal: krb_principal
krb_keytab_path: /tmp/krb_keytab
ssl_certs_dir: /var/certs
""", True),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
krb_keytab_path: /tmp/krb_keytab
""", True),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
krb_principal: krb_principal
""", True),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
krb_principal: krb_principal
ssl_certs_dir: /var/certs
""", True),
("""\
version: 1
koji:
hub_url: https://koji.example.com/hub
root_url: https://koji.example.com/root
auth:
proxyuser: proxyuser
krb_keytab_path: /tmp/krb_keytab
ssl_certs_dir: /var/certs
""", True),
])
def test_get_koji_session(self, fallback, config, raise_error):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
if raise_error:
with pytest.raises(Exception):
read_yaml(config, 'schemas/config.json')
return
config_json = read_yaml(config, 'schemas/config.json')
auth_info = {
"proxyuser": config_json['koji']['auth'].get('proxyuser'),
"ssl_certs_dir": config_json['koji']['auth'].get('ssl_certs_dir'),
"krb_principal": config_json['koji']['auth'].get('krb_principal'),
"krb_keytab": config_json['koji']['auth'].get('krb_keytab_path')
}
fallback_map = {}
if fallback:
fallback_map = {'auth': deepcopy(auth_info), 'hub_url': config_json['koji']['hub_url']}
fallback_map['auth']['krb_keytab_path'] = fallback_map['auth'].pop('krb_keytab')
else:
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] = \
ReactorConfig(config_json)
(flexmock(atomic_reactor.koji_util)
.should_receive('create_koji_session')
.with_args(config_json['koji']['hub_url'], auth_info)
.once()
.and_return(True))
get_koji_session(workflow, fallback_map)
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize('root_url', (
'https://koji.example.com/root',
'https://koji.example.com/root/',
None
))
def test_get_koji_path_info(self, fallback, root_url):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
config = {
'version': 1,
'koji': {
'hub_url': 'https://koji.example.com/hub',
'auth': {
'ssl_certs_dir': '/var/certs'
}
}
}
expected_root_url = 'https://koji.example.com/root'
if root_url:
config['koji']['root_url'] = root_url
config_yaml = yaml.safe_dump(config)
expect_error = not root_url
if expect_error:
with pytest.raises(Exception):
read_yaml(config_yaml, 'schemas/config.json')
return
parsed_config = read_yaml(config_yaml, 'schemas/config.json')
fallback_map = {}
if fallback:
fallback_map = deepcopy(config['koji'])
else:
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] = \
ReactorConfig(parsed_config)
(flexmock(koji.PathInfo)
.should_receive('__init__')
.with_args(topdir=expected_root_url)
.once())
get_koji_path_info(workflow, fallback_map)
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize(('config', 'raise_error'), [
("""\
version: 1
pulp:
name: my-pulp
auth:
password: testpasswd
username: testuser
""", False),
("""\
version: 1
pulp:
name: my-pulp
auth:
ssl_certs_dir: /var/certs
""", False),
("""\
version: 1
pulp:
name: my-pulp
auth:
ssl_certs_dir: /var/certs
password: testpasswd
username: testuser
""", True),
("""\
version: 1
pulp:
name: my-pulp
auth:
ssl_certs_dir: /var/certs
password: testpasswd
""", True),
("""\
version: 1
pulp:
name: my-pulp
auth:
ssl_certs_dir: /var/certs
username: testuser
""", True),
("""\
version: 1
pulp:
name: my-pulp
auth:
username: testuser
""", True),
("""\
version: 1
pulp:
name: my-pulp
auth:
password: testpasswd
""", True),
])
def test_get_pulp_session(self, fallback, config, raise_error):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
if raise_error:
with pytest.raises(Exception):
read_yaml(config, 'schemas/config.json')
return
config_json = read_yaml(config, 'schemas/config.json')
auth_info = {
"pulp_secret_path": config_json['pulp']['auth'].get('ssl_certs_dir'),
"username": config_json['pulp']['auth'].get('username'),
"password": config_json['pulp']['auth'].get('password'),
"dockpulp_loglevel": None
}
fallback_map = {}
if fallback:
fallback_map = {'auth': deepcopy(auth_info), 'name': config_json['pulp']['name']}
fallback_map['auth']['ssl_certs_dir'] = fallback_map['auth'].pop('pulp_secret_path')
else:
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\
ReactorConfig(config_json)
(flexmock(atomic_reactor.pulp_util.PulpHandler)
.should_receive('__init__')
.with_args(workflow, config_json['pulp']['name'], 'logger', **auth_info)
.once()
.and_return(None))
get_pulp_session(workflow, 'logger', fallback_map)
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize(('config', 'raise_error'), [
("""\
version: 1
odcs:
api_url: https://odcs.example.com/api/1
auth:
ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret
signing_intents:
- name: release
keys: [R123]
default_signing_intent: default
""", False),
("""\
version: 1
odcs:
api_url: https://odcs.example.com/api/1
auth:
ssl_certs_dir: nonexistent
signing_intents:
- name: release
keys: [R123]
default_signing_intent: default
""", False),
("""\
version: 1
odcs:
api_url: https://odcs.example.com/api/1
auth:
openidc_dir: /var/run/open_idc
signing_intents:
- name: release
keys: [R123]
default_signing_intent: default
""", False),
("""\
version: 1
odcs:
api_url: https://odcs.example.com/api/1
auth:
openidc_dir: /var/run/open_idc
ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret
signing_intents:
- name: release
keys: [R123]
default_signing_intent: default
""", True),
("""\
version: 1
odcs:
api_url: https://odcs.example.com/api/1
auth:
openidc_dir: /var/run/open_idc
signing_intents:
- name: release
keys: [R123]
""", True),
("""\
version: 1
odcs:
api_url: https://odcs.example.com/api/1
auth:
openidc_dir: /var/run/open_idc
default_signing_intent: default
""", True),
("""\
version: 1
odcs:
auth:
openidc_dir: /var/run/open_idc
signing_intents:
- name: release
keys: [R123]
default_signing_intent: default
""", True),
])
def test_get_odcs_session(self, tmpdir, fallback, config, raise_error):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
if raise_error:
with pytest.raises(Exception):
read_yaml(config, 'schemas/config.json')
return
config_json = read_yaml(config, 'schemas/config.json')
auth_info = {'insecure': config_json['odcs'].get('insecure', False)}
if 'openidc_dir' in config_json['odcs']['auth']:
config_json['odcs']['auth']['openidc_dir'] = str(tmpdir)
filename = str(tmpdir.join('token'))
with open(filename, 'w') as fp:
fp.write("my_token")
auth_info['token'] = "my_token"
ssl_dir_raise = False
if 'ssl_certs_dir' in config_json['odcs']['auth']:
if config_json['odcs']['auth']['ssl_certs_dir'] != "nonexistent":
config_json['odcs']['auth']['ssl_certs_dir'] = str(tmpdir)
filename = str(tmpdir.join('cert'))
with open(filename, 'w') as fp:
fp.write("my_cert")
auth_info['cert'] = filename
else:
ssl_dir_raise = True
fallback_map = {}
if fallback:
fallback_map = {'auth': deepcopy(auth_info),
'api_url': config_json['odcs']['api_url']}
fallback_map['auth']['ssl_certs_dir'] = config_json['odcs']['auth'].get('ssl_certs_dir')
fallback_map['auth']['openidc_dir'] = config_json['odcs']['auth'].get('openidc_dir')
else:
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\
ReactorConfig(config_json)
if not ssl_dir_raise:
(flexmock(atomic_reactor.odcs_util.ODCSClient)
.should_receive('__init__')
.with_args(config_json['odcs']['api_url'], **auth_info)
.once()
.and_return(None))
get_odcs_session(workflow, fallback_map)
else:
with pytest.raises(KeyError):
get_odcs_session(workflow, fallback_map)
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize(('config', 'raise_error'), [
("""\
version: 1
smtp:
host: smtp.example.com
from_address: osbs@example.com
""", False),
("""\
version: 1
smtp:
from_address: osbs@example.com
""", True),
("""\
version: 1
smtp:
host: smtp.example.com
""", True),
("""\
version: 1
smtp:
""", True),
])
def test_get_smtp_session(self, fallback, config, raise_error):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
if raise_error:
with pytest.raises(Exception):
read_yaml(config, 'schemas/config.json')
return
config_json = read_yaml(config, 'schemas/config.json')
fallback_map = {}
if fallback:
fallback_map['host'] = config_json['smtp']['host']
else:
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\
ReactorConfig(config_json)
(flexmock(smtplib.SMTP)
.should_receive('__init__')
.with_args(config_json['smtp']['host'])
.once()
.and_return(None))
get_smtp_session(workflow, fallback_map)
@pytest.mark.parametrize('fallback', (True, False))
@pytest.mark.parametrize('build_json_dir', [
None, "/tmp/build_json_dir",
])
@pytest.mark.parametrize(('config', 'raise_error'), [
("""\
version: 1
openshift:
url: https://openshift.example.com
auth:
ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret
""", False),
("""\
version: 1
openshift:
url: https://openshift.example.com
""", False),
("""\
version: 1
openshift:
url: https://openshift.example.com
auth:
krb_principal: principal
krb_keytab_path: /var/keytab
""", False),
("""\
version: 1
openshift:
url: https://openshift.example.com
auth:
krb_principal: principal
krb_keytab_path: /var/keytab
krb_cache_path: /var/krb/cache
""", False),
("""\
version: 1
openshift:
url: https://openshift.example.com
auth:
enable: True
""", False),
("""\
version: 1
openshift:
url: https://openshift.example.com
auth:
krb_keytab_path: /var/keytab
""", True),
("""\
version: 1
openshift:
url: https://openshift.example.com
auth:
krb_principal: principal
""", True),
("""\
version: 1
openshift:
auth:
ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret
""", True),
("""\
version: 1
openshift:
auth:
krb_principal: principal
krb_keytab_path: /var/keytab
""", True),
("""\
version: 1
openshift:
url: https://openshift.example.com
auth:
""", True),
("""\
version: 1
openshift:
auth:
ssl_certs_dir: /var/run/secrets/atomic-reactor/odcssecret
""", True),
])
def test_get_openshift_session(self, fallback, build_json_dir, config, raise_error):
tasker, workflow = self.prepare()
workflow.plugin_workspace[ReactorConfigPlugin.key] = {}
if build_json_dir:
config += " build_json_dir: " + build_json_dir
if raise_error:
with pytest.raises(Exception):
read_yaml(config, 'schemas/config.json')
return
config_json = read_yaml(config, 'schemas/config.json')
auth_info = {
'openshift_url': config_json['openshift']['url'],
'verify_ssl': not config_json['openshift'].get('insecure', False),
'use_auth': False,
'conf_file': None,
'namespace': 'namespace',
'build_json_dir': build_json_dir
}
if config_json['openshift'].get('auth'):
if config_json['openshift']['auth'].get('krb_keytab_path'):
auth_info['kerberos_keytab'] =\
config_json['openshift']['auth'].get('krb_keytab_path')
if config_json['openshift']['auth'].get('krb_principal'):
auth_info['kerberos_principal'] =\
config_json['openshift']['auth'].get('krb_principal')
if config_json['openshift']['auth'].get('krb_cache_path'):
auth_info['kerberos_ccache'] =\
config_json['openshift']['auth'].get('krb_cache_path')
if config_json['openshift']['auth'].get('ssl_certs_dir'):
auth_info['client_cert'] =\
os.path.join(config_json['openshift']['auth'].get('ssl_certs_dir'), 'cert')
auth_info['client_key'] =\
os.path.join(config_json['openshift']['auth'].get('ssl_certs_dir'), 'key')
auth_info['use_auth'] = config_json['openshift']['auth'].get('enable', False)
fallback_map = {}
if fallback:
fallback_map = {'url': config_json['openshift']['url'],
'insecure': config_json['openshift'].get('insecure', False),
'build_json_dir': build_json_dir}
if config_json['openshift'].get('auth'):
fallback_map['auth'] = {}
fallback_map['auth']['krb_keytab_path'] =\
config_json['openshift']['auth'].get('krb_keytab_path')
fallback_map['auth']['krb_principal'] =\
config_json['openshift']['auth'].get('krb_principal')
fallback_map['auth']['enable'] =\
config_json['openshift']['auth'].get('enable', False)
fallback_map['auth']['krb_cache_path'] =\
config_json['openshift']['auth'].get('krb_cache_path')
fallback_map['auth']['ssl_certs_dir'] =\
config_json['openshift']['auth'].get('ssl_certs_dir')
else:
workflow.plugin_workspace[ReactorConfigPlugin.key][WORKSPACE_CONF_KEY] =\
ReactorConfig(config_json)
(flexmock(osbs.conf.Configuration)
.should_call('__init__')
.with_args(**auth_info)
.once())
(flexmock(osbs.api.OSBS)
.should_call('__init__')
.once())
flexmock(os, environ={'BUILD': '{"metadata": {"namespace": "namespace"}}'})
get_openshift_session(workflow, fallback_map)
| true | true |
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