hexsha stringlengths 40 40 | repo stringlengths 7 114 | path stringlengths 4 124 | license listlengths 1 9 | language stringclasses 1
value | identifier stringlengths 1 71 | return_type stringlengths 1 749 ⌀ | original_string stringlengths 76 22.7k | original_docstring stringlengths 16 7.61k | docstring stringlengths 16 2.47k | docstring_tokens listlengths 6 477 | code stringlengths 14 10.2k | code_tokens listlengths 6 996 | short_docstring stringlengths 2 644 | short_docstring_tokens listlengths 1 116 | comment listlengths 1 89 | parameters listlengths 0 64 | docstring_params dict |
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4719cbed1deadfcac7293b691f197ccf777cdc13 | BdVade/Voting | Rewards2/Dhanny_reward2/run.py | [
"Apache-2.0"
] | Python | check_exists | <not_specific> | def check_exists(self, row, xpath):
"""
Checks if an xpath exists
`Required`
:param row: row to find path on
:param str xpath: xpath to check that exists
"""
try:
row.find_element_by_xpath(xpath)
return True
except NoSuch... |
Checks if an xpath exists
`Required`
:param row: row to find path on
:param str xpath: xpath to check that exists
| Checks if an xpath exists
`Required` | [
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try:
row.find_element_by_xpath(xpath)
return True
except NoSuchElementException:
return False | [
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4719cbed1deadfcac7293b691f197ccf777cdc13 | BdVade/Voting | Rewards2/Dhanny_reward2/run.py | [
"Apache-2.0"
] | Python | _get_total_pages | null | def _get_total_pages(self):
"""
Gets the the total page that exists.
"""
total_pages = self.browser.find_element_by_xpath('//*[@id="__next"]/div/div[1]/div[1]/div/div[2]/div/div/div/div/div[1]/div[2]').text
self.total_pages = int(total_pages.split(' ')[1]) |
Gets the the total page that exists.
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total_pages = self.browser.find_element_by_xpath('//*[@id="__next"]/div/div[1]/div[1]/div/div[2]/div/div/div/div/div[1]/div[2]').text
self.total_pages = int(total_pages.split(' ')[1]) | [
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4719cbed1deadfcac7293b691f197ccf777cdc13 | BdVade/Voting | Rewards2/Dhanny_reward2/run.py | [
"Apache-2.0"
] | Python | scrape_rows | null | def scrape_rows(self):
"""
Scrapes the data on a page
"""
rows = self.browser.find_elements_by_css_selector('div.master > div.root > div > div:nth-child(3) > div > div > div > div > div.table-responsive > table > tbody > tr.row.styles_item__L_bVx')
for row in rows:
fr... |
Scrapes the data on a page
| Scrapes the data on a page | [
"Scrapes",
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] | def scrape_rows(self):
rows = self.browser.find_elements_by_css_selector('div.master > div.root > div > div:nth-child(3) > div > div > div > div > div.table-responsive > table > tbody > tr.row.styles_item__L_bVx')
for row in rows:
fromAddress = row.find_element_by_xpath('.//td[5]/span') if s... | [
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4719cbed1deadfcac7293b691f197ccf777cdc13 | BdVade/Voting | Rewards2/Dhanny_reward2/run.py | [
"Apache-2.0"
] | Python | main | None | async def main(self) -> None:
"""
Creates the ClientSession and loops through the list of urls to extract data from
"""
tasks = []
async with aiohttp.ClientSession(headers=self.headers) as session:
for url in self.urls:
log(f"Making connections to url:... |
Creates the ClientSession and loops through the list of urls to extract data from
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tasks = []
async with aiohttp.ClientSession(headers=self.headers) as session:
for url in self.urls:
log(f"Making connections to url: {url}...")
tasks.append(self.fetch(session, url))
htmls = await asyncio.gather(*tasks... | [
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4719cbed1deadfcac7293b691f197ccf777cdc13 | BdVade/Voting | Rewards2/Dhanny_reward2/run.py | [
"Apache-2.0"
] | Python | log | null | def log(msg: str, level='debug'):
"""
Log output to the console
`Optional`
:param str level: Level of verbosity
`Required`
:param str msg: Data to display on console
"""
logging.basicConfig(level=logging.DEBUG if globals()['_debug'] else logging.INFO, handlers=[logging.StreamHand... |
Log output to the console
`Optional`
:param str level: Level of verbosity
`Required`
:param str msg: Data to display on console
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`Optional` | [
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logging.basicConfig(level=logging.DEBUG if globals()['_debug'] else logging.INFO, handlers=[logging.StreamHandler()])
logger = logging.getLogger()
getattr(logger, level if hasattr(logger, level) else 'debug')(str(msg)) | [
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4719cbed1deadfcac7293b691f197ccf777cdc13 | BdVade/Voting | Rewards2/Dhanny_reward2/run.py | [
"Apache-2.0"
] | Python | create_urls | <not_specific> | def create_urls(address):
"""
Creates the list of url for the different pages on the api
`Required`
:param str address: Address to use for the creation of the url
"""
urls = []
url = string.Template("https://indexer.algoexplorerapi.io/rl/v1/transactions?page=${page}&&limit=10&&address=$... |
Creates the list of url for the different pages on the api
`Required`
:param str address: Address to use for the creation of the url
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] | def create_urls(address):
urls = []
url = string.Template("https://indexer.algoexplorerapi.io/rl/v1/transactions?page=${page}&&limit=10&&address=${address}")
log(f"Getting number of pages for {address}...", "info")
response = requests.get(url.substitute(page=1, address=address))
if response.status_c... | [
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4719cbed1deadfcac7293b691f197ccf777cdc13 | BdVade/Voting | Rewards2/Dhanny_reward2/run.py | [
"Apache-2.0"
] | Python | write_csv | null | def write_csv(all_data, wallet, type):
"""
Writes to a csv file the data received with a filename of the walletaddress and the scantype (algoexplorer or algoscan)
`Required`
:param list all_data: A list of data containing `from`,`to` and `amount`
:param str wallet: The wallet address
:pa... |
Writes to a csv file the data received with a filename of the walletaddress and the scantype (algoexplorer or algoscan)
`Required`
:param list all_data: A list of data containing `from`,`to` and `amount`
:param str wallet: The wallet address
:param str type: Scan type (algoexplorer/a... | Writes to a csv file the data received with a filename of the walletaddress and the scantype (algoexplorer or algoscan)
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log("Writing to csv file", "info")
header = ["from", "to","amount"]
filename = f"{wallet}_{type}.csv"
with open(filename, "w", encoding="UTF-8") as f:
writer = csv.DictWriter(f, fieldnames=header)
writer.writeheader()
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becf0ccc61e757a90d3f3350119a8385d52eab65 | BdVade/Voting | Bronze_Badges/seun-Choice-Coin-Python/helpers.py | [
"Apache-2.0"
] | Python | validate_escrow_wallet | bool | def validate_escrow_wallet(address: str, _mnemonic: str, client) -> bool:
"""Validate an escrow wallet and check it has sufficient funds and is opted in to Choice Coin."""
if not is_valid_address(address):
return False
# compare the address to the address gotten from the mnemonic
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if not is_valid_address(address):
return False
if account.address_from_private_key(mnemonic.to_private_key(_mnemonic)) != address:
return False
if not contains_choice_coin(address, client):
return False
if ... | [
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becf0ccc61e757a90d3f3350119a8385d52eab65 | BdVade/Voting | Bronze_Badges/seun-Choice-Coin-Python/helpers.py | [
"Apache-2.0"
] | Python | contains_choice_coin | bool | def contains_choice_coin(address: str, client) -> bool:
"""Checks if the address is opted into Vote Coin."""
account = client.account_info(address)
contains_choice = False
for asset in account["assets"]:
if asset["asset-id"] == ASSET_ID:
contains_vote = True
break
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account = client.account_info(address)
contains_choice = False
for asset in account["assets"]:
if asset["asset-id"] == ASSET_ID:
contains_vote = True
break
return contains_vote | [
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becf0ccc61e757a90d3f3350119a8385d52eab65 | BdVade/Voting | Bronze_Badges/seun-Choice-Coin-Python/helpers.py | [
"Apache-2.0"
] | Python | create_option_account | str | def create_option_account(escrow_private_key: str, escrow_address: str, client) -> str:
"""Creates an account for option."""
# This is the amount of Algo to fund the account with. The unit is microAlgos.
AMOUNT = 1000000
private_key, address = account.generate_account()
is_successful = fund_addres... | Creates an account for option. | Creates an account for option. | [
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AMOUNT = 1000000
private_key, address = account.generate_account()
is_successful = fund_address(AMOUNT, address, escrow_address, escrow_private_key, client)
if not is_successful:
raise Exception("Funding Fail... | [
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{
"identifier": "escrow_address",
"type": "str",
"docstring": null,
... |
becf0ccc61e757a90d3f3350119a8385d52eab65 | BdVade/Voting | Bronze_Badges/seun-Choice-Coin-Python/helpers.py | [
"Apache-2.0"
] | Python | fund_address | bool | def fund_address(
amount: int, recipient_address: str, escrow_address: str, escrow_private_key: str, client
) -> bool:
"""Fund an account with Algo."""
suggested_params = client.suggested_params()
unsigned_transaction = PaymentTxn(
escrow_address,
suggested_params,
recipient_addr... | Fund an account with Algo. | Fund an account with Algo. | [
"Fund",
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"Algo",
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] | def fund_address(
amount: int, recipient_address: str, escrow_address: str, escrow_private_key: str, client
) -> bool:
suggested_params = client.suggested_params()
unsigned_transaction = PaymentTxn(
escrow_address,
suggested_params,
recipient_address,
amount,
note="In... | [
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becf0ccc61e757a90d3f3350119a8385d52eab65 | BdVade/Voting | Bronze_Badges/seun-Choice-Coin-Python/helpers.py | [
"Apache-2.0"
] | Python | opt_in_to_vote | bool | def opt_in_to_vote(private_key: str, address: str, client) -> bool:
"""Opt in a wallet to vote Coin."""
suggested_params = client.suggested_params()
if not contains_choice_coin(address, client):
unsigned_transaction = AssetTransferTxn(
address, suggested_params, address, 0, ASSET_ID
... | Opt in a wallet to vote Coin. | Opt in a wallet to vote Coin. | [
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] | def opt_in_to_vote(private_key: str, address: str, client) -> bool:
suggested_params = client.suggested_params()
if not contains_choice_coin(address, client):
unsigned_transaction = AssetTransferTxn(
address, suggested_params, address, 0, ASSET_ID
)
signed_transaction = unsig... | [
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becf0ccc61e757a90d3f3350119a8385d52eab65 | BdVade/Voting | Bronze_Badges/seun-Choice-Coin-Python/helpers.py | [
"Apache-2.0"
] | Python | vote | null | def vote(escrow_private_key, escrow_address, option_zero_address, option_one_address, client):
"""Places a vote based on the input of the user."""
voter = int(input("Vote 0 for zero and vote 1 for one: "))
if voter == 1:
make_vote(
escrow_address,
escrow_private_key,
... | Places a vote based on the input of the user. | Places a vote based on the input of the user. | [
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] | def vote(escrow_private_key, escrow_address, option_zero_address, option_one_address, client):
voter = int(input("Vote 0 for zero and vote 1 for one: "))
if voter == 1:
make_vote(
escrow_address,
escrow_private_key,
option_one_address,
100,
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... |
becf0ccc61e757a90d3f3350119a8385d52eab65 | BdVade/Voting | Bronze_Badges/seun-Choice-Coin-Python/helpers.py | [
"Apache-2.0"
] | Python | calculate_votes | <not_specific> | def calculate_votes(addresses: list, client):
"""Calculate the result of a voting process."""
results = []
for addr in addresses:
account_info = client.account_info(addr)
assets = account_info.get("assets")
for _asset in assets:
if _asset["asset-id"] == ASSET_ID:
... | Calculate the result of a voting process. | Calculate the result of a voting process. | [
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] | def calculate_votes(addresses: list, client):
results = []
for addr in addresses:
account_info = client.account_info(addr)
assets = account_info.get("assets")
for _asset in assets:
if _asset["asset-id"] == ASSET_ID:
amount = _asset.get("amount")
... | [
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becf0ccc61e757a90d3f3350119a8385d52eab65 | BdVade/Voting | Bronze_Badges/seun-Choice-Coin-Python/helpers.py | [
"Apache-2.0"
] | Python | wait_for_x_secs | None | def wait_for_x_secs(delay: float) -> None:
"""Specify the number of seconds the program should delay for."""
# A block takes approximately four(4) seconds to be added to the blockchain on Algorand
print(f"Waiting for {delay} second(s) for blockchain to sync...")
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] | def wait_for_x_secs(delay: float) -> None:
print(f"Waiting for {delay} second(s) for blockchain to sync...")
time.sleep(delay) | [
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}
],
"outlier_params": [],
"others": []
} |
94a01d37ccc1992e81f937888ea8226fcc3ef7bd | testsibirtsv/opncrt_my_version | elements/addressbook.py | [
"Apache-2.0"
] | Python | records_count | <not_specific> | def records_count(self):
"""
Count the number of address records on the Address Book page.
"""
return len(self.edit_btn_list) |
Count the number of address records on the Address Book page.
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} |
94a01d37ccc1992e81f937888ea8226fcc3ef7bd | testsibirtsv/opncrt_my_version | elements/addressbook.py | [
"Apache-2.0"
] | Python | open_edit_page_by_position | null | def open_edit_page_by_position(self, position):
"""
Edit address book entry from the Address Book page
by it's positional index.
:param position: positional index of address entry
in list of addresses on the Address Book page.
"""
self.edit_btn_list[position].cli... |
Edit address book entry from the Address Book page
by it's positional index.
:param position: positional index of address entry
in list of addresses on the Address Book page.
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94a01d37ccc1992e81f937888ea8226fcc3ef7bd | testsibirtsv/opncrt_my_version | elements/addressbook.py | [
"Apache-2.0"
] | Python | delete_record_by_index | null | def delete_record_by_index(self, index):
"""
Delete Address Book entry from Address Book page
by it's positional index.
:param index: positional index of address entry
in list of addresses on Address Book page.
"""
self.delete_btn_list[index].click() |
Delete Address Book entry from Address Book page
by it's positional index.
:param index: positional index of address entry
in list of addresses on Address Book page.
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94a01d37ccc1992e81f937888ea8226fcc3ef7bd | testsibirtsv/opncrt_my_version | elements/addressbook.py | [
"Apache-2.0"
] | Python | open_form_page | null | def open_form_page(self):
"""
Open Add Address form o
n Address Book page.
"""
self.new_address_btn.click() |
Open Add Address form o
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} |
0bfae455cf931809b3c6750a51baf273e3dd315c | testsibirtsv/opncrt_my_version | elements/addaddress.py | [
"Apache-2.0"
] | Python | fill_address_form | null | def fill_address_form(self, address):
"""
Fill fields with data in Add Address form.
:param address: object with parameters for fields.
"""
self.change_text_field_data(self.firstname_field, address.first_name)
self.change_text_field_data(self.lastname_field, address.last... |
Fill fields with data in Add Address form.
:param address: object with parameters for fields.
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] | def fill_address_form(self, address):
self.change_text_field_data(self.firstname_field, address.first_name)
self.change_text_field_data(self.lastname_field, address.last_name)
self.change_text_field_data(self.company_field, address.company)
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0bfae455cf931809b3c6750a51baf273e3dd315c | testsibirtsv/opncrt_my_version | elements/addaddress.py | [
"Apache-2.0"
] | Python | change_drop_list_data | null | def change_drop_list_data(ddlist_option, value):
"""
Select option in dropdown list in Add Address form.
:param ddlist_option: option's id in dropdown list.
:param value: option's text in dropdown list.
"""
if value is not None:
data_select = Select(ddlist_op... |
Select option in dropdown list in Add Address form.
:param ddlist_option: option's id in dropdown list.
:param value: option's text in dropdown list.
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] | def change_drop_list_data(ddlist_option, value):
if value is not None:
data_select = Select(ddlist_option)
data_select.select_by_visible_text(value) | [
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0bfae455cf931809b3c6750a51baf273e3dd315c | testsibirtsv/opncrt_my_version | elements/addaddress.py | [
"Apache-2.0"
] | Python | change_text_field_data | null | def change_text_field_data(field_name, value):
"""
Set text into Add Address form field.
:param field_name: field's id in Add Address form.
:param value: field's text in Add Address form.
"""
if value is not None:
field_name.click()
field_name.cle... |
Set text into Add Address form field.
:param field_name: field's id in Add Address form.
:param value: field's text in Add Address form.
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if value is not None:
field_name.click()
field_name.clear()
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c3b4405b276e7fee1cc8ee9ce88df10ddeab5ad0 | testsibirtsv/opncrt_my_version | pages/addressbook.py | [
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Open Address Book page, then open and fill Add Address form
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c3b4405b276e7fee1cc8ee9ce88df10ddeab5ad0 | testsibirtsv/opncrt_my_version | pages/addressbook.py | [
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8045cc2050e8062660de69a260292e9376bfaf4b | testsibirtsv/opncrt_my_version | elements/editaccount.py | [
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Enter the data in the textfield.
:param form_field: textfield's id.
:param data: data entered in the textfield.
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8045cc2050e8062660de69a260292e9376bfaf4b | testsibirtsv/opncrt_my_version | elements/editaccount.py | [
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Change user data in the Edit Account form.
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56d16616a07d1329bfa565a3bbed2e15c3297b66 | 0xbs0d/figures | figures/sites.py | [
"MIT"
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"""
Helper to cast site or site id to id
This is helpful for celery tasks that require primitives for
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"""
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56d16616a07d1329bfa565a3bbed2e15c3297b66 | 0xbs0d/figures | figures/sites.py | [
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Convenience method to iterate over site or site id iterables.
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56d16616a07d1329bfa565a3bbed2e15c3297b66 | 0xbs0d/figures | figures/sites.py | [
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Tech debt note: Open edX monkeypatches django.contrib.sites to override
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56d16616a07d1329bfa565a3bbed2e15c3297b66 | 0xbs0d/figures | figures/sites.py | [
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56d16616a07d1329bfa565a3bbed2e15c3297b66 | 0xbs0d/figures | figures/sites.py | [
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bfa16ba5a32981109998657f90e7b693fc27ac9c | 0xbs0d/figures | figures/tasks.py | [
"MIT"
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bfa16ba5a32981109998657f90e7b693fc27ac9c | 0xbs0d/figures | figures/tasks.py | [
"MIT"
] | Python | populate_daily_metrics | null | def populate_daily_metrics(date_for=None, force_update=False):
'''Populates the daily metrics models for the given date
This method populates CourseDailyMetrics for all the courses in the site,
then populates SiteDailyMetrics
It calls the individual tasks, ``populate_single_cdm`` and
``populate_si... | Populates the daily metrics models for the given date
This method populates CourseDailyMetrics for all the courses in the site,
then populates SiteDailyMetrics
It calls the individual tasks, ``populate_single_cdm`` and
``populate_site_daily_metrics`` as immediate calls so that no courses are
misse... | Populates the daily metrics models for the given date
This method populates CourseDailyMetrics for all the courses in the site,
then populates SiteDailyMetrics
We have an experimental task that runs the course populators in
parallel, then when they are all done, populates the site metrics. See the
function ``experi... | [
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if date_for:
date_for = as_date(date_for)
else:
date_for = datetime.datetime.utcnow().replace(tzinfo=utc).date()
logger.info('Starting task "figures.populate_daily_metrics" for date "{}"'.format(
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bfa16ba5a32981109998657f90e7b693fc27ac9c | 0xbs0d/figures | figures/tasks.py | [
"MIT"
] | Python | experimental_populate_daily_metrics | <not_specific> | def experimental_populate_daily_metrics(date_for=None, force_update=False):
'''Experimental task to populate daily metrics
WARNING: In Ginkgo devstack, this task tends to gets stuck in the middle of
processing course metrics. Not all the courses get processed and the site
metrics doesn't get called.
... | Experimental task to populate daily metrics
WARNING: In Ginkgo devstack, this task tends to gets stuck in the middle of
processing course metrics. Not all the courses get processed and the site
metrics doesn't get called.
We're keeping it in the tasks so that we can continue to debug this.
Enablin... | Experimental task to populate daily metrics
WARNING: In Ginkgo devstack, this task tends to gets stuck in the middle of
processing course metrics. Not all the courses get processed and the site
metrics doesn't get called.
We're keeping it in the tasks so that we can continue to debug this.
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def include_course(course_overview, threshold=50):
count = CourseEnrollment.objects.filter(course_id=course_overview.id).count()
return False if count > threshold else True
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bfa16ba5a32981109998657f90e7b693fc27ac9c | 0xbs0d/figures | figures/tasks.py | [
"MIT"
] | Python | include_course | <not_specific> | def include_course(course_overview, threshold=50):
'''This function let's us skip over courses with many enrollments, speeding
up testing. Do not use for production
'''
count = CourseEnrollment.objects.filter(course_id=course_overview.id).count()
return False if count > threshold... | This function let's us skip over courses with many enrollments, speeding
up testing. Do not use for production
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bfa16ba5a32981109998657f90e7b693fc27ac9c | 0xbs0d/figures | figures/tasks.py | [
"MIT"
] | Python | populate_course_mau | null | def populate_course_mau(site_id, course_id, month_for=None, force_update=False):
"""Populates the MAU for the given site, course, and month
"""
if month_for:
month_for = as_date(month_for)
else:
month_for = datetime.datetime.utcnow().date()
site = Site.objects.get(id=site_id)
sta... | Populates the MAU for the given site, course, and month
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if month_for:
month_for = as_date(month_for)
else:
month_for = datetime.datetime.utcnow().date()
site = Site.objects.get(id=site_id)
start_time = time.time()
obj, _created = collect_course_mau(site=site,... | [
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bfa16ba5a32981109998657f90e7b693fc27ac9c | 0xbs0d/figures | figures/tasks.py | [
"MIT"
] | Python | populate_mau_metrics_for_site | null | def populate_mau_metrics_for_site(site_id, month_for=None, force_update=False):
"""
Collect (save) MAU metrics for the specified site
Iterates over all courses in the site to collect MAU counts
TODO: Decide how sites would be excluded and create filter
TODO: Check results of 'store_mau_metrics' to ... |
Collect (save) MAU metrics for the specified site
Iterates over all courses in the site to collect MAU counts
TODO: Decide how sites would be excluded and create filter
TODO: Check results of 'store_mau_metrics' to log unexpected results
| Collect (save) MAU metrics for the specified site
Iterates over all courses in the site to collect MAU counts
TODO: Decide how sites would be excluded and create filter
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site = Site.objects.get(id=site_id)
msg = 'Starting figures'
logger.info(msg)
for course_key in figures.sites.get_course_keys_for_site(site):
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bfa16ba5a32981109998657f90e7b693fc27ac9c | 0xbs0d/figures | figures/tasks.py | [
"MIT"
] | Python | populate_all_mau | null | def populate_all_mau():
"""
Top level task to kick off MAU collection
Initially, run it every day to observe monthly active user accumulation for
the month and evaluate the results
"""
for site in Site.objects.all():
populate_mau_metrics_for_site(site_id=site.id, force_update=False) |
Top level task to kick off MAU collection
Initially, run it every day to observe monthly active user accumulation for
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Initially, run it every day to observe monthly active user accumulation for
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bfa16ba5a32981109998657f90e7b693fc27ac9c | 0xbs0d/figures | figures/tasks.py | [
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] | Python | run_figures_monthly_metrics | null | def run_figures_monthly_metrics():
"""
TODO: only run for active sites. Requires knowing which sites we can skip
"""
logger.info('Starting figures.tasks.run_figures_monthly_metrics...')
for site in Site.objects.all():
populate_monthly_metrics_for_site.delay(site_id=site.id) |
TODO: only run for active sites. Requires knowing which sites we can skip
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aac684e37c8b10ebc504d86848f64855ed9e56a2 | 0xbs0d/figures | figures/views.py | [
"MIT"
] | Python | completed | <not_specific> | def completed(self, request):
"""Experimental endpoint to return completed LCGM records
This is the same as `/figures/api/enrollment-metrics/?only_completed=True
Return matching LearnerCourseGradeMetric rows that have completed
enrollments
"""
site = django.contrib.site... | Experimental endpoint to return completed LCGM records
This is the same as `/figures/api/enrollment-metrics/?only_completed=True
Return matching LearnerCourseGradeMetric rows that have completed
enrollments
| Experimental endpoint to return completed LCGM records
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qs = self.model.objects.completed_for_site(site=site)
page = self.paginate_queryset(qs)
if page is not None:
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aac684e37c8b10ebc504d86848f64855ed9e56a2 | 0xbs0d/figures | figures/views.py | [
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] | Python | list | <not_specific> | def list(self, request):
"""
Returns site metrics data for current month
TODO: NEXT Add query params to get data from previous months
TODO: Add paginagation
"""
site = django.contrib.sites.shortcuts.get_current_site(request)
course_keys = figures.sites.get_course... |
Returns site metrics data for current month
TODO: NEXT Add query params to get data from previous months
TODO: Add paginagation
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aac684e37c8b10ebc504d86848f64855ed9e56a2 | 0xbs0d/figures | figures/views.py | [
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Returns site metrics data for current month
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aac684e37c8b10ebc504d86848f64855ed9e56a2 | 0xbs0d/figures | figures/views.py | [
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TODO: Rename the metrics module function to "new_users" to match this
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058ff30b2aef3298cf9698a8d4607656a3eb32d1 | rfzeg/pp_unit3_exercises | unit5_pp/scripts/gradient_descent_solution.py | [
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058ff30b2aef3298cf9698a8d4607656a3eb32d1 | rfzeg/pp_unit3_exercises | unit5_pp/scripts/gradient_descent_solution.py | [
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058ff30b2aef3298cf9698a8d4607656a3eb32d1 | rfzeg/pp_unit3_exercises | unit5_pp/scripts/gradient_descent_solution.py | [
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c14ab152e7a6fa2004bbe1f137adcd7c35fc9df1 | rfzeg/pp_unit3_exercises | unit4_pp/scripts/unit4_exercise.py | [
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c14ab152e7a6fa2004bbe1f137adcd7c35fc9df1 | rfzeg/pp_unit3_exercises | unit4_pp/scripts/unit4_exercise.py | [
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c14ab152e7a6fa2004bbe1f137adcd7c35fc9df1 | rfzeg/pp_unit3_exercises | unit4_pp/scripts/unit4_exercise.py | [
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] | Python | find_closest_node | null | def find_closest_node(random_pt, node_list):
"""
Finds the closest node in the tree
random_pt: a [x,y] point (as a list)
node_list: list that keeps all nodes in the tree
returns: Node instance that is the closest node in the tree
"""
## Add your code ##
pass |
Finds the closest node in the tree
random_pt: a [x,y] point (as a list)
node_list: list that keeps all nodes in the tree
returns: Node instance that is the closest node in the tree
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c14ab152e7a6fa2004bbe1f137adcd7c35fc9df1 | rfzeg/pp_unit3_exercises | unit4_pp/scripts/unit4_exercise.py | [
"BSD-3-Clause"
] | Python | create_new_branch_point | null | def create_new_branch_point(p1, p2, max_distance):
"""
Creates a new point at the max_distance towards a second point
p1: the point to go from (as a list containing x and y values)
p2: the point to go to (as a list containing x and y values)
max_distance: the expand distance (in grid cells)
returns: new poi... |
Creates a new point at the max_distance towards a second point
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p2: the point to go to (as a list containing x and y values)
max_distance: the expand distance (in grid cells)
returns: new point as a list containing x and y values
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p1: the point to go from (as a list containing x and y values)
p2: the point to go to (as a list containing x and y values)
max_distance: the expand distance (in grid cells)
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c14ab152e7a6fa2004bbe1f137adcd7c35fc9df1 | rfzeg/pp_unit3_exercises | unit4_pp/scripts/unit4_exercise.py | [
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] | Python | rrt | null | def rrt(initial_position, target_position, width, height, map, map_resolution, map_origin, tree_viz):
'''
Performs Rapidly-Exploring Random Trees (RRT) algorithm on a costmap with a given start and goal node
'''
## Add your code ##
pass |
Performs Rapidly-Exploring Random Trees (RRT) algorithm on a costmap with a given start and goal node
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e41a847415557fa2440bb7b39dc8cff07d08e112 | rfzeg/pp_unit3_exercises | unit5_pp/scripts/unit5_exercise.py | [
"BSD-3-Clause"
] | Python | random_force | <not_specific> | def random_force(K = 1.0):
"""
Toy function that calculates a random force for one grid cell
returns: a random force value scaled by the constant K
"""
return K * random.uniform(0, 100) |
Toy function that calculates a random force for one grid cell
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e41a847415557fa2440bb7b39dc8cff07d08e112 | rfzeg/pp_unit3_exercises | unit5_pp/scripts/unit5_exercise.py | [
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] | Python | populate_attractive_field | null | def populate_attractive_field(height, width, goal_xy):
"""
Creates an attractive field
returns: 1D flat grid map
"""
pass |
Creates an attractive field
returns: 1D flat grid map
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e41a847415557fa2440bb7b39dc8cff07d08e112 | rfzeg/pp_unit3_exercises | unit5_pp/scripts/unit5_exercise.py | [
"BSD-3-Clause"
] | Python | rescale_to_max_value | <not_specific> | def rescale_to_max_value(max_value, input_list):
""" Rescale each value inside input_list into a target range of 0 to max_value """
scale_factor = max_value / float(max(input_list))
# Multiply each item by the scale_factor
input_list_rescaled = [int(x * scale_factor) for x in input_list]
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scale_factor = max_value / float(max(input_list))
input_list_rescaled = [int(x * scale_factor) for x in input_list]
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e41a847415557fa2440bb7b39dc8cff07d08e112 | rfzeg/pp_unit3_exercises | unit5_pp/scripts/unit5_exercise.py | [
"BSD-3-Clause"
] | Python | world_to_grid_map | <not_specific> | def world_to_grid_map(world_pose):
""" Convert Pose() message in world frame of reference to grid map cell/pixel coordinates """
grid_cell_x = int((world_pose.position.x - attractive_field.info.origin.position.x) / attractive_field.info.resolution)
grid_cell_y = int((world_pose.position.y - attractive_field... | Convert Pose() message in world frame of reference to grid map cell/pixel coordinates | Convert Pose() message in world frame of reference to grid map cell/pixel coordinates | [
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grid_cell_x = int((world_pose.position.x - attractive_field.info.origin.position.x) / attractive_field.info.resolution)
grid_cell_y = int((world_pose.position.y - attractive_field.info.origin.position.y) / attractive_field.info.resolution)
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f651bc23499d8eec0741f83ad2802d8bdd6f2452 | rfzeg/pp_unit3_exercises | unit2_pp/scripts/unit2_solution_server.py | [
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] | Python | make_plan | <not_specific> | def make_plan(req):
'''
Callback function used by the service server to process
requests from clients. It returns a msg of type PathPlanningPluginResponse
'''
# costmap as 1-D array representation
costmap = req.costmap_ros
# number of columns in the occupancy grid
width = req.width
# number of rows ... |
Callback function used by the service server to process
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costmap = req.costmap_ros
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height = req.height
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resolution = 0.2
origin = [-7.4, -7.4, 0]
viz = GridViz(costmap, resolution, origin, start_index, goal_index, width)
start_time = rospy.Time.now()
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6dd31e4e55cc740be4b16f0ca285414a2fd1cac7 | rfzeg/pp_unit3_exercises | unit5_pp/scripts/gradient_descent.py | [
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] | Python | gradient_descent | null | def gradient_descent(start_index, goal_index, width, height, potential_field_data, descent_viz):
'''
Performs gradient descent on an artificial potential field
with a given start, goal node and a total potential field
'''
pass |
Performs gradient descent on an artificial potential field
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51e390807dd3601f364492ecc606070ea12af6c6 | rfzeg/pp_unit3_exercises | unit3_pp/scripts/unit3_gbfs_exercise_server.py | [
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] | Python | make_plan | <not_specific> | def make_plan(req):
'''
Callback function used by the service server to process
requests from clients. It returns a msg of type PathPlanningPluginResponse
'''
# costmap as 1-D array representation
costmap = req.costmap_ros
# number of columns in the occupancy grid
width = req.width
# number of rows ... |
Callback function used by the service server to process
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costmap = req.costmap_ros
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goal_index = req.goal
resolution = 0.2
origin = [-7.4, -7.4, 0]
viz = GridViz(costmap, resolution, origin, start_index, goal_index, width)
start_time = rospy.Time.now()
path = greedy_bfs(start... | [
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78d13e65958bdc0e38670c0f8fbe78836e056725 | rfzeg/pp_unit3_exercises | unit2_pp/scripts/unit2_solution.py | [
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] | Python | find_neighbors | <not_specific> | def find_neighbors(index, width, height, costmap, orthogonal_step_cost):
"""
Identifies neighbor nodes inspecting the 8 adjacent neighbors
Checks if neighbor is inside the map boundaries and if is not an obstacle according to a threshold
Returns a list with valid neighbour nodes as [index, step_cost] pairs
""... |
Identifies neighbor nodes inspecting the 8 adjacent neighbors
Checks if neighbor is inside the map boundaries and if is not an obstacle according to a threshold
Returns a list with valid neighbour nodes as [index, step_cost] pairs
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Checks if neighbor is inside the map boundaries and if is not an obstacle according to a threshold
Returns a list with valid neighbour nodes as [index, step_cost] pairs | [
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neighbors = []
diagonal_step_cost = orthogonal_step_cost * 1.41421
lethal_cost = 1
upper = index - width
if upper > 0:
if costmap[upper] < lethal_cost:
step_cost = orthogonal_step_cost + costmap[upper]/255
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78d13e65958bdc0e38670c0f8fbe78836e056725 | rfzeg/pp_unit3_exercises | unit2_pp/scripts/unit2_solution.py | [
"BSD-3-Clause"
] | Python | dijkstra | <not_specific> | def dijkstra(start_index, goal_index, width, height, costmap, resolution, origin, grid_viz):
'''
Performs Dijkstra's shortes path algorithm search on a costmap with a given start and goal node
'''
# create an open_list
open_list = []
# set to hold already processed nodes
closed_list = set()
# dict f... |
Performs Dijkstra's shortes path algorithm search on a costmap with a given start and goal node
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open_list = []
closed_list = set()
parents = dict()
g_costs = dict()
g_costs[start_index] = 0
open_list.append([start_index, 0])
shortest_path = []
path_found = False
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5cc88305e0cb26bcae8d0eca3a9ff52805989579 | rfzeg/pp_unit3_exercises | unit5_pp/scripts/unit5_pp_server.py | [
"BSD-3-Clause"
] | Python | make_plan | <not_specific> | def make_plan(req):
'''
Callback function used by the service server to process
requests from clients. It returns a msg of type PathPlanningPluginResponse
'''
global got_potential_field_update
# costmap as 1-D array representation
costmap = req.costmap_ros
# number of columns in the occupancy grid
wi... |
Callback function used by the service server to process
requests from clients. It returns a msg of type PathPlanningPluginResponse
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global got_potential_field_update
costmap = req.costmap_ros
width = req.width
height = req.height
start_index = req.start
goal_index = req.goal
resolution = 0.2
origin = [-7.4, -7.4, 0]
got_potential_field_update = False
while not got_potential_field_update:
pass
viz = Desc... | [
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3f8e1f8bef34916bf1eee2f9dda967d2b4adfcb6 | rfzeg/pp_unit3_exercises | unit4_pp/scripts/unit4_exercise_solution.py | [
"BSD-3-Clause"
] | Python | find_closest_node | <not_specific> | def find_closest_node(random_pt, node_list):
"""
Finds the closest node in the tree
random_pt: a [x,y] point (as a list)
node_list: list that keeps all nodes in the tree
returns: Node instance that is the closest node in the tree
"""
nearest_distance = float('inf')
for n in node_list:
current_distan... |
Finds the closest node in the tree
random_pt: a [x,y] point (as a list)
node_list: list that keeps all nodes in the tree
returns: Node instance that is the closest node in the tree
| Finds the closest node in the tree
random_pt: a [x,y] point (as a list)
node_list: list that keeps all nodes in the tree
returns: Node instance that is the closest node in the tree | [
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nearest_distance = float('inf')
for n in node_list:
current_distance = calculate_distance(random_pt, n.coordinates)
if current_distance < nearest_distance:
nearest_node = n
nearest_distance = current_distance
return nearest_node | [
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3f8e1f8bef34916bf1eee2f9dda967d2b4adfcb6 | rfzeg/pp_unit3_exercises | unit4_pp/scripts/unit4_exercise_solution.py | [
"BSD-3-Clause"
] | Python | create_new_branch_point | <not_specific> | def create_new_branch_point(p1, p2, max_distance):
"""
Creates a new point at the max_distance towards a second point
p1: the point to go from (as a list containing x and y values)
p2: the point to go to (as a list containing x and y values)
max_distance: the expand distance (in grid cells)
returns: new poi... |
Creates a new point at the max_distance towards a second point
p1: the point to go from (as a list containing x and y values)
p2: the point to go to (as a list containing x and y values)
max_distance: the expand distance (in grid cells)
returns: new point as a list containing x and y values
| Creates a new point at the max_distance towards a second point
p1: the point to go from (as a list containing x and y values)
p2: the point to go to (as a list containing x and y values)
max_distance: the expand distance (in grid cells)
returns: new point as a list containing x and y values | [
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new_point = list(p1)
d = calculate_distance(new_point, p2)
theta = calculate_angle(new_point, p2)
if max_distance > d:
max_distance = d
new_point[0] += int(max_distance * cos(theta))
new_point[1] += int(max_distance * sin(theta))
return new_point | [
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3f8e1f8bef34916bf1eee2f9dda967d2b4adfcb6 | rfzeg/pp_unit3_exercises | unit4_pp/scripts/unit4_exercise_solution.py | [
"BSD-3-Clause"
] | Python | rrt | <not_specific> | def rrt(initial_position, target_position, width, height, map, map_resolution, map_origin, tree_viz):
'''
Performs Rapidly-Exploring Random Trees (RRT) algorithm on a costmap with a given start and goal node
'''
# Create the root node
root_node = Node(initial_position)
# A list to keep all nodes in the tre... |
Performs Rapidly-Exploring Random Trees (RRT) algorithm on a costmap with a given start and goal node
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root_node = Node(initial_position)
nodes = [root_node]
interations = 0
max_iterations = 10000
max_branch_lenght = 20
goal_tolerance = 20
path = []
while True:
interations += 1
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8a4c4fad7f26d4d1dd67f7b88494a5812c150c0c | rfzeg/pp_unit3_exercises | unit3_pp/scripts/unit3_astar_solution.py | [
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] | Python | indexToWorld | <not_specific> | def indexToWorld(flatmap_index, map_width, map_resolution, map_origin = [0,0]):
"""
Converts a flatmap index value to world coordinates (meters)
flatmap_index: a linear index value, specifying a cell/pixel in an 1-D array
map_width: number of columns in the occupancy grid
map_resolution: side lenght... |
Converts a flatmap index value to world coordinates (meters)
flatmap_index: a linear index value, specifying a cell/pixel in an 1-D array
map_width: number of columns in the occupancy grid
map_resolution: side lenght of each grid map cell in meters
map_origin: the x,y position in grid cell coordina... | Converts a flatmap index value to world coordinates (meters)
flatmap_index: a linear index value, specifying a cell/pixel in an 1-D array
map_width: number of columns in the occupancy grid
map_resolution: side lenght of each grid map cell in meters
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grid_cell_map_x = flatmap_index % map_width
grid_cell_map_y = flatmap_index // map_width
x = map_resolution * grid_cell_map_x + map_origin[0]
y = map_resolution * grid_cell_map_y + map_origin[1]
return [x,y] | [
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8a4c4fad7f26d4d1dd67f7b88494a5812c150c0c | rfzeg/pp_unit3_exercises | unit3_pp/scripts/unit3_astar_solution.py | [
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] | Python | a_star | <not_specific> | def a_star(start_index, goal_index, width, height, costmap, resolution, origin, grid_viz):
'''
Performs A-star's shortes path algorithm search on a costmap with a given start and goal node
'''
# create an open_list
open_list = []
# set to hold already processed nodes
closed_list = set()
# dict for m... |
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7d73397a011e7053212201c7b8f805b9877be267 | rfzeg/pp_unit3_exercises | unit4_pp/scripts/unit4_exercise_server.py | [
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] | Python | make_plan | <not_specific> | def make_plan(req):
'''
Callback function used by the service server to process requests.
It returns a PathPlanningPluginResponse
'''
# Convert costmap from a 1-D tuple flat map representation
map = list(req.costmap_ros)
# Change values on the map from unknown to free space
map[map==255] = 1
width = ... |
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start_index = req.start
goal_index = req.goal
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7d73397a011e7053212201c7b8f805b9877be267 | rfzeg/pp_unit3_exercises | unit4_pp/scripts/unit4_exercise_server.py | [
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] | Python | indexToGridCell | <not_specific> | def indexToGridCell(flat_map_index, map_width):
"""
Converts a linear index of a flat map to grid cell coordinate values
flat_map_index: a linear index value, specifying a cell/pixel in an 1-D array
map_width: the map's width
returns: list with [x,y] grid cell coordinates
"""
grid_cell_map_x = flat_map_i... |
Converts a linear index of a flat map to grid cell coordinate values
flat_map_index: a linear index value, specifying a cell/pixel in an 1-D array
map_width: the map's width
returns: list with [x,y] grid cell coordinates
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grid_cell_map_x = flat_map_index % map_width
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c9deb650839257dd6c511ae0ee8bb6b306ff1de9 | rfzeg/pp_unit3_exercises | unit6_pp/scripts/planner_solution.py | [
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""" Plan the shortest path between a start and a destination geopoint.
The 'geo_path' method can receive GeoPoints out of the graph, upon such a case, the nearest segments on the OSM map are detected,
and the planning is carried out.
:pram req: The requ... | Plan the shortest path between a start and a destination geopoint.
The 'geo_path' method can receive GeoPoints out of the graph, upon such a case, the nearest segments on the OSM map are detected,
and the planning is carried out.
:pram req: The request message.
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c9deb650839257dd6c511ae0ee8bb6b306ff1de9 | rfzeg/pp_unit3_exercises | unit6_pp/scripts/planner_solution.py | [
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] | Python | dijkstra_geo_planner | <not_specific> | def dijkstra_geo_planner(self, start_segment, goal_segment):
"""
Plan route from start to goal. The actual search algorithm to find a path is executed here.
start_segment: The nearest segment to the start point, as type 'geographic_msgs/RouteSegment'
goal_segment: The nearest segment... |
Plan route from start to goal. The actual search algorithm to find a path is executed here.
start_segment: The nearest segment to the start point, as type 'geographic_msgs/RouteSegment'
goal_segment: The nearest segment to the goal point, as type 'geographic_msgs/RouteSegment'
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start_idx = self.points.index(start_segment.start.uuid)
goal_idx = self.points.index(goal_segment.start.uuid)
plan = RoutePath(network=self.graph.id)
open_list = []
open_list.append([0, start_idx])
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c9deb650839257dd6c511ae0ee8bb6b306ff1de9 | rfzeg/pp_unit3_exercises | unit6_pp/scripts/planner_solution.py | [
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""" Chooses between the orthogonal projection, and the start and end points
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If the given orthogonal projection lies out of the segment, the whether the start
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... | Chooses between the orthogonal projection, and the start and end points
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If the given orthogonal projection lies out of the segment, the whether the start
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c9deb650839257dd6c511ae0ee8bb6b306ff1de9 | rfzeg/pp_unit3_exercises | unit6_pp/scripts/planner_solution.py | [
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""" Get the segment that corresponds to the given ID.
:param uuid: The id of the segment.
:type uuid: uuid_msgs/UniqueID
:return: The segment for the given uuid.
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c9deb650839257dd6c511ae0ee8bb6b306ff1de9 | rfzeg/pp_unit3_exercises | unit6_pp/scripts/planner_solution.py | [
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""" Searches the segment with given id with given start point in a pre-cached list
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:param start_point_id: The id of start point of the segment.
:type start_point_id: uuid_msgs/UniqueID
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:type start_point_id: uuid_msgs/UniqueID
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c9deb650839257dd6c511ae0ee8bb6b306ff1de9 | rfzeg/pp_unit3_exercises | unit6_pp/scripts/planner_solution.py | [
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""" Compute 2D Euclidean distance between two utm points.
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92126ac18a5a89ebf72e7e97e5c03c10630565b4 | rfzeg/pp_unit3_exercises | unit5_pp/scripts/descentviz.py | [
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0f3410ec5a2795a303ea9e8ac165d482bc4d618c | rfzeg/pp_unit3_exercises | unit3_pp/scripts/unit3_dijkstra.py | [
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'''
Callback function used by the service server to process
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'''
# costmap as 1-D array representation
costmap = req.costmap_ros
# number of columns in the occupancy grid
width = req.width
# number of rows ... |
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df694000084b816f85fe01f6ce6f8add562ed937 | rfzeg/pp_unit3_exercises | unit3_pp/scripts/unit3_gbfs_exercise.py | [
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] | Python | greedy_bfs | <not_specific> | def greedy_bfs(start_index, goal_index, width, height, costmap, resolution, origin, grid_viz):
'''
Performs Greedy Best-First Search on a costmap with a given start and goal node
'''
# create an open_list
open_list = []
# set to hold already processed nodes
closed_list = set()
# dict for mapping chi... |
Performs Greedy Best-First Search on a costmap with a given start and goal node
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g_costs[start_index] = 0
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d481528b18818ea262c20e7e449b36d1d6fefc73 | rfzeg/pp_unit3_exercises | unit2_pp/scripts/unit2_exercise.py | [
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'''
Performs Dijkstra's shortes path algorithm search on a costmap with a given start and goal node
'''
#### To-do: complete all exercises below ####
pass |
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d5e0cbd29c239e41b881c07902797dadc185f3ab | rfzeg/pp_unit3_exercises | route_network/tests/test_planner.py | [
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r.points.append(makeWayPoint('812f1c08-a34b-5a21-92b9-18b2b0cf4950',
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d5e0cbd29c239e41b881c07902797dadc185f3ab | rfzeg/pp_unit3_exercises | route_network/tests/test_planner.py | [
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d5e0cbd29c239e41b881c07902797dadc185f3ab | rfzeg/pp_unit3_exercises | route_network/tests/test_planner.py | [
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d5e0cbd29c239e41b881c07902797dadc185f3ab | rfzeg/pp_unit3_exercises | route_network/tests/test_planner.py | [
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:param min_lon: Initial longitude [degrees].
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:param min_lon: Initial longitude [degrees].
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:param max_lon: Longitude limit [degrees].
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r = RouteNetwork(id=nid)
prev_row = None
for latitude in float_range(min_lat, max_lat, step):
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983edccbd0effb2fe190c3b23525e13a6259a22d | TomCallingham/Agama_Cautun_Contraction | contraction_agama.py | [
"MIT"
] | Python | contract_agama_potential | <not_specific> | def contract_agama_potential(dm_pot, baryon_pot, fbar=0.157,rmax=500, rmin=0.1):
'''Given spherical DM and axisymmetric baryon agama potentials,
creates a contracted DM agama potential using procedure found in Cautun 20'''
r_space = np.geomspace(rmin,rmax,501)
xyz = np.stack((r_space,np.zeros_like(r_sp... | Given spherical DM and axisymmetric baryon agama potentials,
creates a contracted DM agama potential using procedure found in Cautun 20 | Given spherical DM and axisymmetric baryon agama potentials,
creates a contracted DM agama potential using procedure found in Cautun 20 | [
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] | def contract_agama_potential(dm_pot, baryon_pot, fbar=0.157,rmax=500, rmin=0.1):
r_space = np.geomspace(rmin,rmax,501)
xyz = np.stack((r_space,np.zeros_like(r_space),np.zeros_like(r_space))).transpose()
dens_dm = dm_pot.density(xyz)
try:
Mcum_dm = np.array([dm_pot.totalMass(r) for r in r_space]... | [
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983edccbd0effb2fe190c3b23525e13a6259a22d | TomCallingham/Agama_Cautun_Contraction | contraction_agama.py | [
"MIT"
] | Python | contract_density_fit | <not_specific> | def contract_density_fit( density_DM, density_bar, mass_DM, mass_bar, f_bar=0.157 ):
""" Returns the contracted DM density profile given the 'uncontracted' density and that of the baryonic distribution.
It uses the differential (d/dr) form of Eq. (11) from Cautun et al (2020).
WARNING: This function is... | Returns the contracted DM density profile given the 'uncontracted' density and that of the baryonic distribution.
It uses the differential (d/dr) form of Eq. (11) from Cautun et al (2020).
WARNING: This function is based on a fit to the iterative calculated result of the 'contract_enclosed_mass' and works... | Returns the contracted DM density profile given the 'uncontracted' density and that of the baryonic distribution.
It uses the differential (d/dr) form of Eq.
This function is based on a fit to the iterative calculated result of the 'contract_enclosed_mass' and works best when the
baryonic fraction is close to the Plan... | [
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eta_bar = mass_bar / mass_DM * (1.-f_bar) / f_bar
first_factor = 0.45 + 0.41 * (eta_bar + 0.98)**0.53
temp = density_bar - eta_bar * density_DM * f_bar / (1.-f_bar)
const_term = 0.41 * 0.53 * (eta_bar + 0.98... | [
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983edccbd0effb2fe190c3b23525e13a6259a22d | TomCallingham/Agama_Cautun_Contraction | contraction_agama.py | [
"MIT"
] | Python | contract_enclosed_mass | <not_specific> | def contract_enclosed_mass( mass_DM, mass_bar, f_bar=0.157 ):
""" Returns the contracted DM enclosed mass given the 'uncontracted' profile and that of the baryonic distribution.
It uses Eq. (10) from Cautun et al (2020).
Args:
mass_DM : enclosed mass in the DM component in the absence of bary... | Returns the contracted DM enclosed mass given the 'uncontracted' profile and that of the baryonic distribution.
It uses Eq. (10) from Cautun et al (2020).
Args:
mass_DM : enclosed mass in the DM component in the absence of baryons.
It corresponds to '(1-baryon_fraction... | Returns the contracted DM enclosed mass given the 'uncontracted' profile and that of the baryonic distribution.
It uses Eq. | [
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func_M_DM_contract = lambda M, M_DMO, M_bar: 1.023 * (M_DMO/(1.-f_bar)/(M+M_bar))**-0.540 * M_DMO
return optimize.fixed_point( func_M_DM_contract, mass_DM.copy(), args=(mass_DM,mass_bar), xtol=1.e-6 ) | [
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983edccbd0effb2fe190c3b23525e13a6259a22d | TomCallingham/Agama_Cautun_Contraction | contraction_agama.py | [
"MIT"
] | Python | density_from_enclosed_mass | <not_specific> | def density_from_enclosed_mass( r_bins, enclosed_mass, out_r_bins ):
""" Converts an array of enclosed masses to 3D densities.
Args:
r_bins : the radial bins at which the enclosed mass is defined.
enclosed_mass : array of enclosed masses.
out_r_bins : array of radia... | Converts an array of enclosed masses to 3D densities.
Args:
r_bins : the radial bins at which the enclosed mass is defined.
enclosed_mass : array of enclosed masses.
out_r_bins : array of radial distances at which the density will be interpolated.
Returns:
Arr... | Converts an array of enclosed masses to 3D densities. | [
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bin_mass = enclosed_mass[1:] - enclosed_mass[:-1]
shell_vol= 4.*np.pi/3. * (r_bins[1:]**3 - r_bins[:-1]**3)
bin_dens = bin_mass / shell_vol
r_vals = np.sqrt( r_bins[1:] * r_bins[:-1] )
return np.interp( out_r_bins, r_vals, bin_dens... | [
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604f4b41a897d956a6dbb83fb5388fe3c97768a8 | crgalley/romspline | romspline/helpers.py | [
"MIT"
] | Python | partitions | <not_specific> | def partitions(n, K):
"""Split array with n samples into K (nearly) equal partitions
Input
-----
n -- number of elements to partition into K subsets
K -- number of partitions
Output
------
K arrays whose elements are contiguous intervals of an array of
sequential integers with length n.
""... | Split array with n samples into K (nearly) equal partitions
Input
-----
n -- number of elements to partition into K subsets
K -- number of partitions
Output
------
K arrays whose elements are contiguous intervals of an array of
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| Split array with n samples into K (nearly) equal partitions
Input
- number of elements to partition into K subsets
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Output
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assert n >= K, "Number of partitions must not exceed number of samples."
return np.asarray( np.array_split(np.arange(n), K) ) | [
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... |
604f4b41a897d956a6dbb83fb5388fe3c97768a8 | crgalley/romspline | romspline/helpers.py | [
"MIT"
] | Python | random_partitions | <not_specific> | def random_partitions(n, K):
"""Split array with n samples into K (nearly) equal
partitions of non-overlapping random subsets.
Input
-----
n -- number of elements to partition into K subsets
K -- number of partitions
Output
------
K arrays whose elements are randomly drawn from an array of ... | Split array with n samples into K (nearly) equal
partitions of non-overlapping random subsets.
Input
-----
n -- number of elements to partition into K subsets
K -- number of partitions
Output
------
K arrays whose elements are randomly drawn from an array of
sequential integers with length... | Split array with n samples into K (nearly) equal
partitions of non-overlapping random subsets.
Input
- number of elements to partition into K subsets
K -- number of partitions
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assert n >= K, "Number of partitions must not exceed number of samples."
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return [np.sort(rand[pp]) for pp in partitions(n, K)] | [
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604f4b41a897d956a6dbb83fb5388fe3c97768a8 | crgalley/romspline | romspline/helpers.py | [
"MIT"
] | Python | partitions_to_sets | <not_specific> | def partitions_to_sets(p_label, partitions, sort=True):
"""Split partitions into a validation set (indexed by p_label)
and the rest into a training set
Input
-----
p_label -- index of partition to use
as a validation set
partitions -- set of partitions containing
... | Split partitions into a validation set (indexed by p_label)
and the rest into a training set
Input
-----
p_label -- index of partition to use
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partitions -- set of partitions containing
array indices for validation
and training... | Split partitions into a validation set (indexed by p_label)
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validation = partitions[p_label]
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for ii, pp in enumerate(partitions):
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training = np.hstack([training, pp])
training = np.sort(training)
return validation, np.asarray(training, dtype='int') | [
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604f4b41a897d956a6dbb83fb5388fe3c97768a8 | crgalley/romspline | romspline/helpers.py | [
"MIT"
] | Python | integrate | <not_specific> | def integrate(y, x, weight=None, partial=False):
"""Trapezoidal rule for integration.
Inputs
======
y -- data values or responses
x -- data samples
weight -- Weighting factor in the integrand
(default is None)
partial -- Output partial sums instead of the integral
... | Trapezoidal rule for integration.
Inputs
======
y -- data values or responses
x -- data samples
weight -- Weighting factor in the integrand
(default is None)
partial -- Output partial sums instead of the integral
(default is False)
Outputs
=======... | Trapezoidal rule for integration.
Inputs
Outputs
Scalar equal to the numerical integration of y
over x using the trapezoidal quadrature rule. If
partial is True then the output is an array of
partial sums for each element of y over the previous
x values.
Comments
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... | def integrate(y, x, weight=None, partial=False):
assert len(y) == len(x), "Expecting arrays `y` and `x` to have equal lengths."
dx = x[1]-x[0]
quad_weights = np.ones(len(x))*dx
quad_weights[0] *= 0.5
quad_weights[-1] *= 0.5
if weight is not None:
assert len(x) == len(weight), "Expecting `weight` option ... | [
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... |
604f4b41a897d956a6dbb83fb5388fe3c97768a8 | crgalley/romspline | romspline/helpers.py | [
"MIT"
] | Python | conditional_median | <not_specific> | def conditional_median(x, y, k=0, repeat=0):
"""Compute the median of y conditioned on x.
Input
-----
x -- samples
y -- distributed values, possibly at same x samples
k -- number of nearest neighbors to condition the median on
(default 0)
repeat -- iterates the resu... | Compute the median of y conditioned on x.
Input
-----
x -- samples
y -- distributed values, possibly at same x samples
k -- number of nearest neighbors to condition the median on
(default 0)
repeat -- iterates the result of conditional_median a "repeat"
... | Compute the median of y conditioned on x.
Input
Output
x_unique -- number of unique samples
y_medians -- conditional median for each unique sample
Comments
When k is not zero then the median is conditioned on the
k nearest neighbors for each sample value. A non-zero k
should be used when there are at most one y ... | [
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assert len(x) == len(y), "Expecting arrays x and y to have the same length."
x_unique = np.unique(x)
x_args = np.arange(len(x))
isort = np.argsort(x)
y_sorted = y[isort]
y_medians = np.zeros(len(x_unique), dtype='double')
for rr in range(repeat+1):
for ii i... | [
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... |
604f4b41a897d956a6dbb83fb5388fe3c97768a8 | crgalley/romspline | romspline/helpers.py | [
"MIT"
] | Python | conditional_mean | <not_specific> | def conditional_mean(x, y, k=0, repeat=0):
"""Compute the mean of y conditioned on x.
Input
-----
x -- samples
y -- distributed values, possibly at same x samples
k -- number of nearest neighbors to condition the mean on
(default 0)
repeat -- iterates the result of ... | Compute the mean of y conditioned on x.
Input
-----
x -- samples
y -- distributed values, possibly at same x samples
k -- number of nearest neighbors to condition the mean on
(default 0)
repeat -- iterates the result of conditional_mean a "repeat"
number... | Compute the mean of y conditioned on x.
Input
Output
x_unique -- number of unique samples
y_medians -- conditional mean for each unique sample
Comments
When k is not zero then the mean is conditioned on the
k nearest neighbors for each sample value. A non-zero k
should be used when there are at most one y values... | [
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assert len(x) == len(y), "Expecting arrays x and y to have the same length."
x_unique = np.unique(x)
x_args = np.arange(len(x))
isort = np.argsort(x)
y_sorted = y[isort]
y_means = np.zeros(len(x_unique), dtype='double')
for rr in range(repeat+1):
for ii in ra... | [
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... |
604f4b41a897d956a6dbb83fb5388fe3c97768a8 | crgalley/romspline | romspline/helpers.py | [
"MIT"
] | Python | total_compression_from_sizes | <not_specific> | def total_compression_from_sizes(full_sizes, reduced_sizes):
"""Compute the total amount of compression achieved for
multiple datasets that may have possibly different numbers
of samples.
Input
-----
full_sizes - list or array containing sizes of
multiple full datasets
redu... | Compute the total amount of compression achieved for
multiple datasets that may have possibly different numbers
of samples.
Input
-----
full_sizes - list or array containing sizes of
multiple full datasets
reduced_sizes - list or array containing sizes of
... | Compute the total amount of compression achieved for
multiple datasets that may have possibly different numbers
of samples.
Input
full_sizes - list or array containing sizes of
multiple full datasets
reduced_sizes - list or array containing sizes of
corresponding reduced datasets
Output
The total compression fac... | [
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return float(np.sum(full_sizes)) / np.sum(reduced_sizes) | [
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604f4b41a897d956a6dbb83fb5388fe3c97768a8 | crgalley/romspline | romspline/helpers.py | [
"MIT"
] | Python | total_compression | <not_specific> | def total_compression(*compressions):
"""Compute the total amount of compression achieved for
multiple datasets that have the same number of samples.
Input
-----
compressions -- list or array containing the compression
factors for each dataset
Output
------
The total compre... | Compute the total amount of compression achieved for
multiple datasets that have the same number of samples.
Input
-----
compressions -- list or array containing the compression
factors for each dataset
Output
------
The total compression factor is defined as the sum of
th... | Compute the total amount of compression achieved for
multiple datasets that have the same number of samples.
Input
- list or array containing the compression
factors for each dataset
Output
The total compression factor is defined as the sum of
the full dataset sizes divided by the sum of the reduced
dataset sizes.
... | [
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_sum = 0.
for cc in compressions:
_sum += 1./cc
return len(compressions) / _sum | [
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} |
604f4b41a897d956a6dbb83fb5388fe3c97768a8 | crgalley/romspline | romspline/helpers.py | [
"MIT"
] | Python | D | <not_specific> | def D(y, x, dx=1, order=4):
"""Deriviative(s) of y with respect to x using finite differencing"""
# Look up finite differencing weights
h = x[1]-x[0]
weights = _FD_weights(dx, order, order/2., h)
# Compute finite difference derivatives
ans = np.zeros(y.size, dtype=y.dtype)
for ii, xx in enumerate(x):
... | Deriviative(s) of y with respect to x using finite differencing | Deriviative(s) of y with respect to x using finite differencing | [
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h = x[1]-x[0]
weights = _FD_weights(dx, order, order/2., h)
ans = np.zeros(y.size, dtype=y.dtype)
for ii, xx in enumerate(x):
ix_patch, _, ix = _make_patch(xx, x, order)
if float(ix) != order/2.:
weights = _FD_weights(dx, order, ix, h)
ans[ii] = np.dot(y[ix_patch], ... | [
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... |
604f4b41a897d956a6dbb83fb5388fe3c97768a8 | crgalley/romspline | romspline/helpers.py | [
"MIT"
] | Python | _make_patch | <not_specific> | def _make_patch(x, x_grid, dim):
"""Make patch with dim elements centered around x"""
# Find grid points closest to x
ix = _get_arg(x_grid, x)
# Find how many elements to take on either side of x
if dim % 2: # Check if dim is odd...
a = int((dim+1)/2.)
b = a
else: #...or even
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ix = _get_arg(x_grid, x)
if dim % 2:
a = int((dim+1)/2.)
b = a
else:
a = int(dim/2.)
b = a+1
ix = _check_patch(ix, a, b, x_grid.size)
ipatch = np.arange(ix-a, ix+b)
return ipatch, ix, _get_arg(ipatch, ix) | [
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604f4b41a897d956a6dbb83fb5388fe3c97768a8 | crgalley/romspline | romspline/helpers.py | [
"MIT"
] | Python | _check_patch | <not_specific> | def _check_patch(i, dileft, diright, dim):
"""Check if i is too close to interval endpoints and adjust if so."""
if i <= dileft:
ans = dileft
elif dim-i <= diright:
ans = dim-diright
else:
ans = i
return ans | Check if i is too close to interval endpoints and adjust if so. | Check if i is too close to interval endpoints and adjust if so. | [
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if i <= dileft:
ans = dileft
elif dim-i <= diright:
ans = dim-diright
else:
ans = i
return ans | [
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9e6765ad53ce1c9781af4ab255893c2136eb1437 | crgalley/romspline | romspline/build_spline.py | [
"MIT"
] | Python | build_spline | <not_specific> | def build_spline(x, y, tol=1e-6, deg=None, rel=False, seeds=None, small=True, cv=True, K=10, parallel=True, verbose=True):
"""Build a reduced-order spline interpolant with requested tolerance and degree.
Options include `small`, for randomly sampling the initial seed points to
find a smallest reduced data set, a... | Build a reduced-order spline interpolant with requested tolerance and degree.
Options include `small`, for randomly sampling the initial seed points to
find a smallest reduced data set, and `cv` for performing a Monte Carlo
K-fold cross-validation in order to estimate a global interpolation error.
Input
-... | Build a reduced-order spline interpolant with requested tolerance and degree.
Options include `small`, for randomly sampling the initial seed points to
find a smallest reduced data set, and `cv` for performing a Monte Carlo
K-fold cross-validation in order to estimate a global interpolation error.
Input
Output
C... | [
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if small is False:
if verbose:
print("\nBuilding the reduced-order spline...",)
if deg is None:
deg = 5
spline = greedy.ReducedOrderSpline(x, y, tol=tol, deg=deg, rel=rel,... | [
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"\"\"\"Build a reduced-order spline interpolant with requested tolerance and degree.\n Options include `small`, for randomly sampling the initial seed points to \n find a smallest reduced data set, and `cv` for performing a Monte Carlo\n K-fold cross-validation in order to estimate a global interpolation error.\... | [
{
"param": "x",
"type": null
},
{
"param": "y",
"type": null
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{
"param": "tol",
"type": null
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{
"param": "deg",
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{
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... | {
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"docstring_tokens": [],
... |
0d2939abfb34717f2d595a7d4da9bf3a39e52861 | crgalley/romspline | romspline/random_seeds.py | [
"MIT"
] | Python | _randomSeeds | <not_specific> | def _randomSeeds(x, y, seeds, tol=1e-6, rel=False, deg=5):
"""Build reduced-order splines from an arbitrary seed for the greedy algorithm.
See RandomSeeds for further details.
Input
-----
x -- samples
y -- data to be downsampled
seeds -- list of integers specifying the array indexes of
... | Build reduced-order splines from an arbitrary seed for the greedy algorithm.
See RandomSeeds for further details.
Input
-----
x -- samples
y -- data to be downsampled
seeds -- list of integers specifying the array indexes of
the samples used to seed the greedy algorithm
tol... | Build reduced-order splines from an arbitrary seed for the greedy algorithm.
See RandomSeeds for further details.
Input
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] | def _randomSeeds(x, y, seeds, tol=1e-6, rel=False, deg=5):
seeds = np.sort(seeds)
spline = greedy.ReducedOrderSpline(x, y, tol=tol, rel=rel, deg=deg, seeds=seeds)
return spline.errors[-1], spline.size, seeds, spline.X | [
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... |
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