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8e7b8edb646d4e8fadcdca86d701f8c615778097cb8ff4989f674fb4f03fd734 | @blocker.command('toggle', aliases=['設定', 't'])
@commands.has_guild_permissions(manage_messages=True)
@commands.cooldown(1, 10, commands.BucketType.guild)
@setting.Setting('guild', 'Emoji Blocker 0', HELP)
async def _toggle(self, ctx: commands.Context, *, mode: DataManager.Mode):
"!lang ja\n --------\n リアクションか絵文字またはスタンプのブロックの有効/無効の切り替えをします。 \n ですが、有効にしても対象のロールを追加しなければ意味がありませんので、削除対象のロールの設定を忘れずに。\n\n Parameters\n ----------\n mode : emoji または stamp または reaction\n 削除するものです。\n\n Aliases\n -------\n t, 設定\n\n !lang en\n --------\n Enable/disable the blocking of emoji or stamps or reaction. \n But even if you enable it, it will be useless if you don't add the target role, so don't forget to set the target role for deletion.\n\n Parameters\n ----------\n mode : emoji / stamp / reaction\n It is what RT will delete.\n\n Aliases\n -------\n t"
(await ctx.trigger_typing())
(await self.write(ctx.guild.id, mode))
(await ctx.reply('Ok')) | !lang ja
--------
リアクションか絵文字またはスタンプのブロックの有効/無効の切り替えをします。
ですが、有効にしても対象のロールを追加しなければ意味がありませんので、削除対象のロールの設定を忘れずに。
Parameters
----------
mode : emoji または stamp または reaction
削除するものです。
Aliases
-------
t, 設定
!lang en
--------
Enable/disable the blocking of emoji or stamps or reaction.
But even if you enable it, it will be useless if you don't add the target role, so don't forget to set the target role for deletion.
Parameters
----------
mode : emoji / stamp / reaction
It is what RT will delete.
Aliases
-------
t | cogs/blocker.py | _toggle | RT-Team/rt-bot | 26 | python | @blocker.command('toggle', aliases=['設定', 't'])
@commands.has_guild_permissions(manage_messages=True)
@commands.cooldown(1, 10, commands.BucketType.guild)
@setting.Setting('guild', 'Emoji Blocker 0', HELP)
async def _toggle(self, ctx: commands.Context, *, mode: DataManager.Mode):
"!lang ja\n --------\n リアクションか絵文字またはスタンプのブロックの有効/無効の切り替えをします。 \n ですが、有効にしても対象のロールを追加しなければ意味がありませんので、削除対象のロールの設定を忘れずに。\n\n Parameters\n ----------\n mode : emoji または stamp または reaction\n 削除するものです。\n\n Aliases\n -------\n t, 設定\n\n !lang en\n --------\n Enable/disable the blocking of emoji or stamps or reaction. \n But even if you enable it, it will be useless if you don't add the target role, so don't forget to set the target role for deletion.\n\n Parameters\n ----------\n mode : emoji / stamp / reaction\n It is what RT will delete.\n\n Aliases\n -------\n t"
(await ctx.trigger_typing())
(await self.write(ctx.guild.id, mode))
(await ctx.reply('Ok')) | @blocker.command('toggle', aliases=['設定', 't'])
@commands.has_guild_permissions(manage_messages=True)
@commands.cooldown(1, 10, commands.BucketType.guild)
@setting.Setting('guild', 'Emoji Blocker 0', HELP)
async def _toggle(self, ctx: commands.Context, *, mode: DataManager.Mode):
"!lang ja\n --------\n リアクションか絵文字またはスタンプのブロックの有効/無効の切り替えをします。 \n ですが、有効にしても対象のロールを追加しなければ意味がありませんので、削除対象のロールの設定を忘れずに。\n\n Parameters\n ----------\n mode : emoji または stamp または reaction\n 削除するものです。\n\n Aliases\n -------\n t, 設定\n\n !lang en\n --------\n Enable/disable the blocking of emoji or stamps or reaction. \n But even if you enable it, it will be useless if you don't add the target role, so don't forget to set the target role for deletion.\n\n Parameters\n ----------\n mode : emoji / stamp / reaction\n It is what RT will delete.\n\n Aliases\n -------\n t"
(await ctx.trigger_typing())
(await self.write(ctx.guild.id, mode))
(await ctx.reply('Ok'))<|docstring|>!lang ja
--------
リアクションか絵文字またはスタンプのブロックの有効/無効の切り替えをします。
ですが、有効にしても対象のロールを追加しなければ意味がありませんので、削除対象のロールの設定を忘れずに。
Parameters
----------
mode : emoji または stamp または reaction
削除するものです。
Aliases
-------
t, 設定
!lang en
--------
Enable/disable the blocking of emoji or stamps or reaction.
But even if you enable it, it will be useless if you don't add the target role, so don't forget to set the target role for deletion.
Parameters
----------
mode : emoji / stamp / reaction
It is what RT will delete.
Aliases
-------
t<|endoftext|> |
d88745ef7c7a3afc62241eabd99a27299fd40311c36f46cd7e6fde02834b2967 | @blocker.group(aliases=['ロール', '役職', 'r'], headding={'ja': '文字ブロックで削除対象とするロールの設定リストを表示します。', 'en': 'Displays the configuration list of roles to be deleted in the character block.'})
@setting.Setting('guild', 'Emoji Blocker 1', HELP)
async def role(self, ctx: commands.Context):
'!lang ja\n --------\n 削除対象とするロールを管理するコマンドです。 \n `rt!blocker role`と実行すると設定されているものの一覧が表示されます。 \n これで設定しても`rt!blocker toggle`を実行するまでは何も起きません。\n\n Aliases\n -------\n r, ロール, 役職\n\n !lang en\n --------\n This command is used to manage the roles to be deleted. \n If you run `rt!blocker role`, a list of the configured roles will be displayed. \n If you set it up this way, nothing will happen until you run `rt!blocker toggle`.\n\n Aliases\n -------\n r'
if (not ctx.invoked_subcommand):
if (ctx.guild.id in self.cache):
embed = discord.Embed(title=self.__cog_name__, color=self.bot.Colors.normal)
for (mode, roles) in list(self.cache[ctx.guild.id].items()):
if roles:
embed.add_field(name=mode, value='\n'.join((f'・<@&{role_id}>' for role_id in roles)))
(await ctx.reply(embed=embed)) | !lang ja
--------
削除対象とするロールを管理するコマンドです。
`rt!blocker role`と実行すると設定されているものの一覧が表示されます。
これで設定しても`rt!blocker toggle`を実行するまでは何も起きません。
Aliases
-------
r, ロール, 役職
!lang en
--------
This command is used to manage the roles to be deleted.
If you run `rt!blocker role`, a list of the configured roles will be displayed.
If you set it up this way, nothing will happen until you run `rt!blocker toggle`.
Aliases
-------
r | cogs/blocker.py | role | RT-Team/rt-bot | 26 | python | @blocker.group(aliases=['ロール', '役職', 'r'], headding={'ja': '文字ブロックで削除対象とするロールの設定リストを表示します。', 'en': 'Displays the configuration list of roles to be deleted in the character block.'})
@setting.Setting('guild', 'Emoji Blocker 1', HELP)
async def role(self, ctx: commands.Context):
'!lang ja\n --------\n 削除対象とするロールを管理するコマンドです。 \n `rt!blocker role`と実行すると設定されているものの一覧が表示されます。 \n これで設定しても`rt!blocker toggle`を実行するまでは何も起きません。\n\n Aliases\n -------\n r, ロール, 役職\n\n !lang en\n --------\n This command is used to manage the roles to be deleted. \n If you run `rt!blocker role`, a list of the configured roles will be displayed. \n If you set it up this way, nothing will happen until you run `rt!blocker toggle`.\n\n Aliases\n -------\n r'
if (not ctx.invoked_subcommand):
if (ctx.guild.id in self.cache):
embed = discord.Embed(title=self.__cog_name__, color=self.bot.Colors.normal)
for (mode, roles) in list(self.cache[ctx.guild.id].items()):
if roles:
embed.add_field(name=mode, value='\n'.join((f'・<@&{role_id}>' for role_id in roles)))
(await ctx.reply(embed=embed)) | @blocker.group(aliases=['ロール', '役職', 'r'], headding={'ja': '文字ブロックで削除対象とするロールの設定リストを表示します。', 'en': 'Displays the configuration list of roles to be deleted in the character block.'})
@setting.Setting('guild', 'Emoji Blocker 1', HELP)
async def role(self, ctx: commands.Context):
'!lang ja\n --------\n 削除対象とするロールを管理するコマンドです。 \n `rt!blocker role`と実行すると設定されているものの一覧が表示されます。 \n これで設定しても`rt!blocker toggle`を実行するまでは何も起きません。\n\n Aliases\n -------\n r, ロール, 役職\n\n !lang en\n --------\n This command is used to manage the roles to be deleted. \n If you run `rt!blocker role`, a list of the configured roles will be displayed. \n If you set it up this way, nothing will happen until you run `rt!blocker toggle`.\n\n Aliases\n -------\n r'
if (not ctx.invoked_subcommand):
if (ctx.guild.id in self.cache):
embed = discord.Embed(title=self.__cog_name__, color=self.bot.Colors.normal)
for (mode, roles) in list(self.cache[ctx.guild.id].items()):
if roles:
embed.add_field(name=mode, value='\n'.join((f'・<@&{role_id}>' for role_id in roles)))
(await ctx.reply(embed=embed))<|docstring|>!lang ja
--------
削除対象とするロールを管理するコマンドです。
`rt!blocker role`と実行すると設定されているものの一覧が表示されます。
これで設定しても`rt!blocker toggle`を実行するまでは何も起きません。
Aliases
-------
r, ロール, 役職
!lang en
--------
This command is used to manage the roles to be deleted.
If you run `rt!blocker role`, a list of the configured roles will be displayed.
If you set it up this way, nothing will happen until you run `rt!blocker toggle`.
Aliases
-------
r<|endoftext|> |
1c3d85fc3d0275292430393c7321ea3b1ef06e2f4abfed81b7d47eaa4a7ae87c | @role.command(aliases=['追加', 'a'])
@commands.cooldown(1, 8, commands.BucketType.guild)
@commands.has_guild_permissions(manage_messages=True)
@setting.Setting('guild', 'Emoji Blocker 2', HELP)
async def add(self, ctx: commands.Context, mode: DataManager.Mode, *, role: 'Role'):
"!lang ja\n --------\n 所有していると絵文字等を送信することができなくなるロールを設定します。\n\n Parameters\n ----------\n mode : emoji / stamp / reaction\n 絵文字かスタンプかリアクションどっちの時での設定かです。\n role : 役職名,IDまたはメンション\n 設定する役職です。\n\n Aliases\n -------\n a, ついか\n\n !lang en\n --------\n Set a role that, when owned, will not allow you to send emoji and other text.\n\n Parameters\n ----------\n mode : emoji / stamp / reaction\n Emoji or Stamp or reaction\n role : role's name, role's ID or role mention\n Target role\n\n Aliases\n -------\n a"
try:
(await self.add_role(ctx.guild.id, mode, role.id))
except AssertionError:
(await ctx.reply('これ以上設定できないまたはまだ設定が有効になっていません。'))
else:
(await ctx.reply('Ok')) | !lang ja
--------
所有していると絵文字等を送信することができなくなるロールを設定します。
Parameters
----------
mode : emoji / stamp / reaction
絵文字かスタンプかリアクションどっちの時での設定かです。
role : 役職名,IDまたはメンション
設定する役職です。
Aliases
-------
a, ついか
!lang en
--------
Set a role that, when owned, will not allow you to send emoji and other text.
Parameters
----------
mode : emoji / stamp / reaction
Emoji or Stamp or reaction
role : role's name, role's ID or role mention
Target role
Aliases
-------
a | cogs/blocker.py | add | RT-Team/rt-bot | 26 | python | @role.command(aliases=['追加', 'a'])
@commands.cooldown(1, 8, commands.BucketType.guild)
@commands.has_guild_permissions(manage_messages=True)
@setting.Setting('guild', 'Emoji Blocker 2', HELP)
async def add(self, ctx: commands.Context, mode: DataManager.Mode, *, role: 'Role'):
"!lang ja\n --------\n 所有していると絵文字等を送信することができなくなるロールを設定します。\n\n Parameters\n ----------\n mode : emoji / stamp / reaction\n 絵文字かスタンプかリアクションどっちの時での設定かです。\n role : 役職名,IDまたはメンション\n 設定する役職です。\n\n Aliases\n -------\n a, ついか\n\n !lang en\n --------\n Set a role that, when owned, will not allow you to send emoji and other text.\n\n Parameters\n ----------\n mode : emoji / stamp / reaction\n Emoji or Stamp or reaction\n role : role's name, role's ID or role mention\n Target role\n\n Aliases\n -------\n a"
try:
(await self.add_role(ctx.guild.id, mode, role.id))
except AssertionError:
(await ctx.reply('これ以上設定できないまたはまだ設定が有効になっていません。'))
else:
(await ctx.reply('Ok')) | @role.command(aliases=['追加', 'a'])
@commands.cooldown(1, 8, commands.BucketType.guild)
@commands.has_guild_permissions(manage_messages=True)
@setting.Setting('guild', 'Emoji Blocker 2', HELP)
async def add(self, ctx: commands.Context, mode: DataManager.Mode, *, role: 'Role'):
"!lang ja\n --------\n 所有していると絵文字等を送信することができなくなるロールを設定します。\n\n Parameters\n ----------\n mode : emoji / stamp / reaction\n 絵文字かスタンプかリアクションどっちの時での設定かです。\n role : 役職名,IDまたはメンション\n 設定する役職です。\n\n Aliases\n -------\n a, ついか\n\n !lang en\n --------\n Set a role that, when owned, will not allow you to send emoji and other text.\n\n Parameters\n ----------\n mode : emoji / stamp / reaction\n Emoji or Stamp or reaction\n role : role's name, role's ID or role mention\n Target role\n\n Aliases\n -------\n a"
try:
(await self.add_role(ctx.guild.id, mode, role.id))
except AssertionError:
(await ctx.reply('これ以上設定できないまたはまだ設定が有効になっていません。'))
else:
(await ctx.reply('Ok'))<|docstring|>!lang ja
--------
所有していると絵文字等を送信することができなくなるロールを設定します。
Parameters
----------
mode : emoji / stamp / reaction
絵文字かスタンプかリアクションどっちの時での設定かです。
role : 役職名,IDまたはメンション
設定する役職です。
Aliases
-------
a, ついか
!lang en
--------
Set a role that, when owned, will not allow you to send emoji and other text.
Parameters
----------
mode : emoji / stamp / reaction
Emoji or Stamp or reaction
role : role's name, role's ID or role mention
Target role
Aliases
-------
a<|endoftext|> |
c861e89efb10e5a90babc261d927e42b0942d6ff89a4b3339a97902bc2c25070 | @role.command(aliases=['削除', 'rm'])
@commands.cooldown(1, 8, commands.BucketType.guild)
@commands.has_guild_permissions(manage_messages=True)
@setting.Setting('guild', 'Emoji Blocker 3', HELP)
async def remove(self, ctx: commands.Context, mode: DataManager.Mode, *, role: 'Role'):
'!lang ja\n --------\n `add`の逆です。\n\n Aliases\n -------\n rm, 削除\n\n !lang en\n --------\n The opposite of `add`.\n\n Aliases\n -------\n rm'
(await self.remove_role(ctx.guild.id, mode, role.id))
(await ctx.reply('Ok')) | !lang ja
--------
`add`の逆です。
Aliases
-------
rm, 削除
!lang en
--------
The opposite of `add`.
Aliases
-------
rm | cogs/blocker.py | remove | RT-Team/rt-bot | 26 | python | @role.command(aliases=['削除', 'rm'])
@commands.cooldown(1, 8, commands.BucketType.guild)
@commands.has_guild_permissions(manage_messages=True)
@setting.Setting('guild', 'Emoji Blocker 3', HELP)
async def remove(self, ctx: commands.Context, mode: DataManager.Mode, *, role: 'Role'):
'!lang ja\n --------\n `add`の逆です。\n\n Aliases\n -------\n rm, 削除\n\n !lang en\n --------\n The opposite of `add`.\n\n Aliases\n -------\n rm'
(await self.remove_role(ctx.guild.id, mode, role.id))
(await ctx.reply('Ok')) | @role.command(aliases=['削除', 'rm'])
@commands.cooldown(1, 8, commands.BucketType.guild)
@commands.has_guild_permissions(manage_messages=True)
@setting.Setting('guild', 'Emoji Blocker 3', HELP)
async def remove(self, ctx: commands.Context, mode: DataManager.Mode, *, role: 'Role'):
'!lang ja\n --------\n `add`の逆です。\n\n Aliases\n -------\n rm, 削除\n\n !lang en\n --------\n The opposite of `add`.\n\n Aliases\n -------\n rm'
(await self.remove_role(ctx.guild.id, mode, role.id))
(await ctx.reply('Ok'))<|docstring|>!lang ja
--------
`add`の逆です。
Aliases
-------
rm, 削除
!lang en
--------
The opposite of `add`.
Aliases
-------
rm<|endoftext|> |
9a703cc6afe2bd5c6b20e110612a4c58c70b638da3e71dcff6b0e5702dc4a8b9 | def is_should_check(self, author: discord.Member) -> Iterator[str]:
'ブロックをすべきかを確かめます。'
for (mode, roles) in list(self.cache[author.guild.id].items()):
if any(((author.get_role(role_id) or (role_id == 0)) for role_id in roles)):
(yield mode) | ブロックをすべきかを確かめます。 | cogs/blocker.py | is_should_check | RT-Team/rt-bot | 26 | python | def is_should_check(self, author: discord.Member) -> Iterator[str]:
for (mode, roles) in list(self.cache[author.guild.id].items()):
if any(((author.get_role(role_id) or (role_id == 0)) for role_id in roles)):
(yield mode) | def is_should_check(self, author: discord.Member) -> Iterator[str]:
for (mode, roles) in list(self.cache[author.guild.id].items()):
if any(((author.get_role(role_id) or (role_id == 0)) for role_id in roles)):
(yield mode)<|docstring|>ブロックをすべきかを確かめます。<|endoftext|> |
4ff36342dd545775ff45d5c6643715fa9d218c71969e40f08cb3868b26909594 | @staticmethod
def precision_recall_f1(true: np.ndarray, pred: np.ndarray):
'Compute precision, recall and f1_score.'
num_predicted = np.unique(pred).size
num_intersect = np.intersect1d(pred, true).size
num_observed = np.unique(true).size
p = (num_intersect / num_predicted)
r = (num_intersect / num_observed)
f1 = ((((2 * p) * r) / (p + r)) if ((p != 0) or (r != 0)) else 0)
return (p, r, f1) | Compute precision, recall and f1_score. | deepr/examples/movielens/jobs/evaluate.py | precision_recall_f1 | drohde/deepr | 0 | python | @staticmethod
def precision_recall_f1(true: np.ndarray, pred: np.ndarray):
num_predicted = np.unique(pred).size
num_intersect = np.intersect1d(pred, true).size
num_observed = np.unique(true).size
p = (num_intersect / num_predicted)
r = (num_intersect / num_observed)
f1 = ((((2 * p) * r) / (p + r)) if ((p != 0) or (r != 0)) else 0)
return (p, r, f1) | @staticmethod
def precision_recall_f1(true: np.ndarray, pred: np.ndarray):
num_predicted = np.unique(pred).size
num_intersect = np.intersect1d(pred, true).size
num_observed = np.unique(true).size
p = (num_intersect / num_predicted)
r = (num_intersect / num_observed)
f1 = ((((2 * p) * r) / (p + r)) if ((p != 0) or (r != 0)) else 0)
return (p, r, f1)<|docstring|>Compute precision, recall and f1_score.<|endoftext|> |
4314a5d38535714b8dc5fe9b0e278e82946d579dc4e9b18f5e7983ecd5573bc6 | @abc.abstractmethod
def is_success(self):
'Predicate that return True if solve was successfull.'
pass | Predicate that return True if solve was successfull. | galini/solvers/solution.py | is_success | cog-imperial/galini | 14 | python | @abc.abstractmethod
def is_success(self):
pass | @abc.abstractmethod
def is_success(self):
pass<|docstring|>Predicate that return True if solve was successfull.<|endoftext|> |
0f4248aa1650b33c0ff6b8d0cb766af416f2ac2d5400f7b0bb27534a7a2ae70f | @abc.abstractmethod
def is_infeasible(self):
'Predicate that return True if problem is infeasible.'
pass | Predicate that return True if problem is infeasible. | galini/solvers/solution.py | is_infeasible | cog-imperial/galini | 14 | python | @abc.abstractmethod
def is_infeasible(self):
pass | @abc.abstractmethod
def is_infeasible(self):
pass<|docstring|>Predicate that return True if problem is infeasible.<|endoftext|> |
159ae7eedffb7e8189c9f92d24044ee98e3b205441cf939fac9d7f2b586cc002 | @abc.abstractmethod
def is_unbounded(self):
'Predicate that return True if problem is unbounded.'
pass | Predicate that return True if problem is unbounded. | galini/solvers/solution.py | is_unbounded | cog-imperial/galini | 14 | python | @abc.abstractmethod
def is_unbounded(self):
pass | @abc.abstractmethod
def is_unbounded(self):
pass<|docstring|>Predicate that return True if problem is unbounded.<|endoftext|> |
dde2454cfc853910af9a1713b902e77a409e3a6e11ac485339a6517943259a4d | @abc.abstractmethod
def description(self):
'Return status description.'
pass | Return status description. | galini/solvers/solution.py | description | cog-imperial/galini | 14 | python | @abc.abstractmethod
def description(self):
pass | @abc.abstractmethod
def description(self):
pass<|docstring|>Return status description.<|endoftext|> |
c73e5fccb35a1c0b4977dc47d391550f4a5d675006925302d2091f3117c75852 | def setUp(self):
'Create filespace '
self.gpfile.create_filespace('filespace_test_a')
super(SwitchCheckpointTestCase, self).setUp() | Create filespace | src/test/tinc/tincrepo/mpp/gpdb/tests/storage/pg_twophase/switch_checkpoint.py | setUp | lintzc/GPDB | 1 | python | def setUp(self):
' '
self.gpfile.create_filespace('filespace_test_a')
super(SwitchCheckpointTestCase, self).setUp() | def setUp(self):
' '
self.gpfile.create_filespace('filespace_test_a')
super(SwitchCheckpointTestCase, self).setUp()<|docstring|>Create filespace<|endoftext|> |
f76df1cc31464cdab6f6132abcc3fcfe015d5781728fbc6445e0792e6286b6b0 | def tearDown(self):
' Cleanup up the filespace created '
self.gpfile.drop_filespace('filespace_test_a')
super(SwitchCheckpointTestCase, self).tearDown() | Cleanup up the filespace created | src/test/tinc/tincrepo/mpp/gpdb/tests/storage/pg_twophase/switch_checkpoint.py | tearDown | lintzc/GPDB | 1 | python | def tearDown(self):
' '
self.gpfile.drop_filespace('filespace_test_a')
super(SwitchCheckpointTestCase, self).tearDown() | def tearDown(self):
' '
self.gpfile.drop_filespace('filespace_test_a')
super(SwitchCheckpointTestCase, self).tearDown()<|docstring|>Cleanup up the filespace created<|endoftext|> |
6480b5c9cccedc8b582bb1f961e03898cc7b9354a551d40015ea244e3a0c448f | def switch_checkpoint(self, cluster_state, fault_type, crash_type):
" \n @param skip_state : skip/noskip checkpoint\n @param cluster_state : sync/change_tracking/resync\n @param ddl_type : create/drop\n @fault_type : commit/abort . Uses the same parameter to pass in 'end_prepare_two_phase_sleep'\n @crash_type : gpstop_i/gpstop_a/failover_to_primary/failover_to_mirror\n @description: Test the state of transactions after fault on master at diff stages followed by crash-recovery. \n Faults are used to suspend the transactions at three stages\n 1. After prepare has been sent by master and acknowledge by all workers\n 2. After distributed commit has been flushed to xlog on master\n 3. After commit prepared has been sent out acknowledged before distributed forget\n Steps:\n 0. Check the state of the cluster before proceeding the test execution\n 1. Run pre_sqls\n 2. Run any faults required before the trigger_sqls based on the fault_type as well as cluster_state\n 3. Run switch_checkpoint loop. switch_xlog, checkpoint in case of 'dtm_broadcast_prepare' fault \n 4. Crash and recover. Resume suspended faults if needed\n 5. Run validate_loop\n 7. Recover the cluster in case if needed\n 8. Validate using gpcheckcat and gpcheckmirrorseg\n\n "
test_dir = {'dtm_broadcast_prepare': 'switch_ckpt_a,switch_ckpt_b', 'dtm_broadcast_commit_prepared': 'switch_ckpt_a,switch_ckpt_b', 'dtm_xlog_distributed_commit': 'switch_ckpt_c'}
test_case_list0 = []
test_case_list0.append('mpp.gpdb.tests.storage.lib.dbstate.DbStateClass.check_system')
self.test_case_scenario.append(test_case_list0)
test_case_list1 = []
if (fault_type == 'dtm_broadcast_commit_prepared'):
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.pre_sql.test_presqls.TestPreSQLClass')
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.pre_sql.test_presqls.TestPreSQLClass')
if (fault_type == 'dtm_broadcast_prepare'):
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.pre_sql.test_presqls.TestPreSQLClass')
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.checkpoint.test_checkpoint.TestCheckpointClass')
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.pre_sql.test_presqls.TestPreSQLClass')
if (fault_type == 'dtm_xlog_distributed_commit'):
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_c.pre_sql.test_presqls.TestPreSQLClass')
self.test_case_scenario.append(test_case_list1)
test_case_list2 = []
test_case_list2.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_faults_before_trigger', [cluster_state, fault_type]))
self.test_case_scenario.append(test_case_list2)
test_case_list3 = []
test_case_list3.append('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_switch_xlog')
self.test_case_scenario.append(test_case_list3)
test_case_list4 = []
if (fault_type == 'dtm_broadcast_commit_prepared'):
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.trigger_sql.test_triggersqls.TestTriggerSQLClass')
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.trigger_sql.test_triggersqls.TestTriggerSQLClass')
if (fault_type == 'dtm_broadcast_prepare'):
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.trigger_sql.test_triggersqls.TestTriggerSQLClass')
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.checkpoint.test_checkpoint.TestCheckpointClass')
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.trigger_sql.test_triggersqls.TestTriggerSQLClass')
if (fault_type == 'dtm_xlog_distributed_commit'):
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_c.trigger_sql.test_triggersqls.TestTriggerSQLClass')
test_case_list4.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_crash_and_recover', [crash_type, fault_type, test_dir[fault_type], cluster_state]))
self.test_case_scenario.append(test_case_list4)
test_case_list5 = []
test_case_list5.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.run_gprecover', [crash_type, cluster_state]))
self.test_case_scenario.append(test_case_list5)
test_case_list6 = []
if (fault_type == 'dtm_broadcast_commit_prepared'):
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.post_sql.test_postsqls.TestPostSQLClass')
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.post_sql.test_postsqls.TestPostSQLClass')
if ((fault_type == 'dtm_broadcast_prepare') and (crash_type not in 'gpstop_i')):
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.post_sql.test_postsqls.TestPostSQLClass')
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.checkpoint.test_checkpoint.TestCheckpointClass')
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.post_sql.test_postsqls.TestPostSQLClass')
if (fault_type == 'dtm_xlog_distributed_commit'):
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_c.post_sql.test_postsqls.TestPostSQLClass')
self.test_case_scenario.append(test_case_list6)
test_case_list7 = []
if (fault_type in ('dtm_broadcast_commit_prepared', 'dtm_broadcast_prepare')):
test_case_list7.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.cleanup_sql.test_cleanup.TestCleanupClass')
if (fault_type == 'dtm_xlog_distributed_commit'):
test_case_list7.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_c.cleanup_sql.test_cleanup.TestCleanupClass')
self.test_case_scenario.append(test_case_list7)
test_case_list8 = []
test_case_list8.append('mpp.gpdb.tests.storage.lib.dbstate.DbStateClass.run_validation')
self.test_case_scenario.append(test_case_list8)
test_case_list9 = []
test_case_list9.append('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.cleanup_dangling_processes')
self.test_case_scenario.append(test_case_list9) | @param skip_state : skip/noskip checkpoint
@param cluster_state : sync/change_tracking/resync
@param ddl_type : create/drop
@fault_type : commit/abort . Uses the same parameter to pass in 'end_prepare_two_phase_sleep'
@crash_type : gpstop_i/gpstop_a/failover_to_primary/failover_to_mirror
@description: Test the state of transactions after fault on master at diff stages followed by crash-recovery.
Faults are used to suspend the transactions at three stages
1. After prepare has been sent by master and acknowledge by all workers
2. After distributed commit has been flushed to xlog on master
3. After commit prepared has been sent out acknowledged before distributed forget
Steps:
0. Check the state of the cluster before proceeding the test execution
1. Run pre_sqls
2. Run any faults required before the trigger_sqls based on the fault_type as well as cluster_state
3. Run switch_checkpoint loop. switch_xlog, checkpoint in case of 'dtm_broadcast_prepare' fault
4. Crash and recover. Resume suspended faults if needed
5. Run validate_loop
7. Recover the cluster in case if needed
8. Validate using gpcheckcat and gpcheckmirrorseg | src/test/tinc/tincrepo/mpp/gpdb/tests/storage/pg_twophase/switch_checkpoint.py | switch_checkpoint | lintzc/GPDB | 1 | python | def switch_checkpoint(self, cluster_state, fault_type, crash_type):
" \n @param skip_state : skip/noskip checkpoint\n @param cluster_state : sync/change_tracking/resync\n @param ddl_type : create/drop\n @fault_type : commit/abort . Uses the same parameter to pass in 'end_prepare_two_phase_sleep'\n @crash_type : gpstop_i/gpstop_a/failover_to_primary/failover_to_mirror\n @description: Test the state of transactions after fault on master at diff stages followed by crash-recovery. \n Faults are used to suspend the transactions at three stages\n 1. After prepare has been sent by master and acknowledge by all workers\n 2. After distributed commit has been flushed to xlog on master\n 3. After commit prepared has been sent out acknowledged before distributed forget\n Steps:\n 0. Check the state of the cluster before proceeding the test execution\n 1. Run pre_sqls\n 2. Run any faults required before the trigger_sqls based on the fault_type as well as cluster_state\n 3. Run switch_checkpoint loop. switch_xlog, checkpoint in case of 'dtm_broadcast_prepare' fault \n 4. Crash and recover. Resume suspended faults if needed\n 5. Run validate_loop\n 7. Recover the cluster in case if needed\n 8. Validate using gpcheckcat and gpcheckmirrorseg\n\n "
test_dir = {'dtm_broadcast_prepare': 'switch_ckpt_a,switch_ckpt_b', 'dtm_broadcast_commit_prepared': 'switch_ckpt_a,switch_ckpt_b', 'dtm_xlog_distributed_commit': 'switch_ckpt_c'}
test_case_list0 = []
test_case_list0.append('mpp.gpdb.tests.storage.lib.dbstate.DbStateClass.check_system')
self.test_case_scenario.append(test_case_list0)
test_case_list1 = []
if (fault_type == 'dtm_broadcast_commit_prepared'):
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.pre_sql.test_presqls.TestPreSQLClass')
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.pre_sql.test_presqls.TestPreSQLClass')
if (fault_type == 'dtm_broadcast_prepare'):
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.pre_sql.test_presqls.TestPreSQLClass')
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.checkpoint.test_checkpoint.TestCheckpointClass')
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.pre_sql.test_presqls.TestPreSQLClass')
if (fault_type == 'dtm_xlog_distributed_commit'):
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_c.pre_sql.test_presqls.TestPreSQLClass')
self.test_case_scenario.append(test_case_list1)
test_case_list2 = []
test_case_list2.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_faults_before_trigger', [cluster_state, fault_type]))
self.test_case_scenario.append(test_case_list2)
test_case_list3 = []
test_case_list3.append('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_switch_xlog')
self.test_case_scenario.append(test_case_list3)
test_case_list4 = []
if (fault_type == 'dtm_broadcast_commit_prepared'):
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.trigger_sql.test_triggersqls.TestTriggerSQLClass')
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.trigger_sql.test_triggersqls.TestTriggerSQLClass')
if (fault_type == 'dtm_broadcast_prepare'):
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.trigger_sql.test_triggersqls.TestTriggerSQLClass')
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.checkpoint.test_checkpoint.TestCheckpointClass')
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.trigger_sql.test_triggersqls.TestTriggerSQLClass')
if (fault_type == 'dtm_xlog_distributed_commit'):
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_c.trigger_sql.test_triggersqls.TestTriggerSQLClass')
test_case_list4.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_crash_and_recover', [crash_type, fault_type, test_dir[fault_type], cluster_state]))
self.test_case_scenario.append(test_case_list4)
test_case_list5 = []
test_case_list5.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.run_gprecover', [crash_type, cluster_state]))
self.test_case_scenario.append(test_case_list5)
test_case_list6 = []
if (fault_type == 'dtm_broadcast_commit_prepared'):
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.post_sql.test_postsqls.TestPostSQLClass')
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.post_sql.test_postsqls.TestPostSQLClass')
if ((fault_type == 'dtm_broadcast_prepare') and (crash_type not in 'gpstop_i')):
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.post_sql.test_postsqls.TestPostSQLClass')
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.checkpoint.test_checkpoint.TestCheckpointClass')
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.post_sql.test_postsqls.TestPostSQLClass')
if (fault_type == 'dtm_xlog_distributed_commit'):
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_c.post_sql.test_postsqls.TestPostSQLClass')
self.test_case_scenario.append(test_case_list6)
test_case_list7 = []
if (fault_type in ('dtm_broadcast_commit_prepared', 'dtm_broadcast_prepare')):
test_case_list7.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.cleanup_sql.test_cleanup.TestCleanupClass')
if (fault_type == 'dtm_xlog_distributed_commit'):
test_case_list7.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_c.cleanup_sql.test_cleanup.TestCleanupClass')
self.test_case_scenario.append(test_case_list7)
test_case_list8 = []
test_case_list8.append('mpp.gpdb.tests.storage.lib.dbstate.DbStateClass.run_validation')
self.test_case_scenario.append(test_case_list8)
test_case_list9 = []
test_case_list9.append('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.cleanup_dangling_processes')
self.test_case_scenario.append(test_case_list9) | def switch_checkpoint(self, cluster_state, fault_type, crash_type):
" \n @param skip_state : skip/noskip checkpoint\n @param cluster_state : sync/change_tracking/resync\n @param ddl_type : create/drop\n @fault_type : commit/abort . Uses the same parameter to pass in 'end_prepare_two_phase_sleep'\n @crash_type : gpstop_i/gpstop_a/failover_to_primary/failover_to_mirror\n @description: Test the state of transactions after fault on master at diff stages followed by crash-recovery. \n Faults are used to suspend the transactions at three stages\n 1. After prepare has been sent by master and acknowledge by all workers\n 2. After distributed commit has been flushed to xlog on master\n 3. After commit prepared has been sent out acknowledged before distributed forget\n Steps:\n 0. Check the state of the cluster before proceeding the test execution\n 1. Run pre_sqls\n 2. Run any faults required before the trigger_sqls based on the fault_type as well as cluster_state\n 3. Run switch_checkpoint loop. switch_xlog, checkpoint in case of 'dtm_broadcast_prepare' fault \n 4. Crash and recover. Resume suspended faults if needed\n 5. Run validate_loop\n 7. Recover the cluster in case if needed\n 8. Validate using gpcheckcat and gpcheckmirrorseg\n\n "
test_dir = {'dtm_broadcast_prepare': 'switch_ckpt_a,switch_ckpt_b', 'dtm_broadcast_commit_prepared': 'switch_ckpt_a,switch_ckpt_b', 'dtm_xlog_distributed_commit': 'switch_ckpt_c'}
test_case_list0 = []
test_case_list0.append('mpp.gpdb.tests.storage.lib.dbstate.DbStateClass.check_system')
self.test_case_scenario.append(test_case_list0)
test_case_list1 = []
if (fault_type == 'dtm_broadcast_commit_prepared'):
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.pre_sql.test_presqls.TestPreSQLClass')
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.pre_sql.test_presqls.TestPreSQLClass')
if (fault_type == 'dtm_broadcast_prepare'):
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.pre_sql.test_presqls.TestPreSQLClass')
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.checkpoint.test_checkpoint.TestCheckpointClass')
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.pre_sql.test_presqls.TestPreSQLClass')
if (fault_type == 'dtm_xlog_distributed_commit'):
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_c.pre_sql.test_presqls.TestPreSQLClass')
self.test_case_scenario.append(test_case_list1)
test_case_list2 = []
test_case_list2.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_faults_before_trigger', [cluster_state, fault_type]))
self.test_case_scenario.append(test_case_list2)
test_case_list3 = []
test_case_list3.append('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_switch_xlog')
self.test_case_scenario.append(test_case_list3)
test_case_list4 = []
if (fault_type == 'dtm_broadcast_commit_prepared'):
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.trigger_sql.test_triggersqls.TestTriggerSQLClass')
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.trigger_sql.test_triggersqls.TestTriggerSQLClass')
if (fault_type == 'dtm_broadcast_prepare'):
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.trigger_sql.test_triggersqls.TestTriggerSQLClass')
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.checkpoint.test_checkpoint.TestCheckpointClass')
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.trigger_sql.test_triggersqls.TestTriggerSQLClass')
if (fault_type == 'dtm_xlog_distributed_commit'):
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_c.trigger_sql.test_triggersqls.TestTriggerSQLClass')
test_case_list4.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_crash_and_recover', [crash_type, fault_type, test_dir[fault_type], cluster_state]))
self.test_case_scenario.append(test_case_list4)
test_case_list5 = []
test_case_list5.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.run_gprecover', [crash_type, cluster_state]))
self.test_case_scenario.append(test_case_list5)
test_case_list6 = []
if (fault_type == 'dtm_broadcast_commit_prepared'):
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.post_sql.test_postsqls.TestPostSQLClass')
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.post_sql.test_postsqls.TestPostSQLClass')
if ((fault_type == 'dtm_broadcast_prepare') and (crash_type not in 'gpstop_i')):
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_a.post_sql.test_postsqls.TestPostSQLClass')
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.checkpoint.test_checkpoint.TestCheckpointClass')
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.post_sql.test_postsqls.TestPostSQLClass')
if (fault_type == 'dtm_xlog_distributed_commit'):
test_case_list6.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_c.post_sql.test_postsqls.TestPostSQLClass')
self.test_case_scenario.append(test_case_list6)
test_case_list7 = []
if (fault_type in ('dtm_broadcast_commit_prepared', 'dtm_broadcast_prepare')):
test_case_list7.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_b.cleanup_sql.test_cleanup.TestCleanupClass')
if (fault_type == 'dtm_xlog_distributed_commit'):
test_case_list7.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_c.cleanup_sql.test_cleanup.TestCleanupClass')
self.test_case_scenario.append(test_case_list7)
test_case_list8 = []
test_case_list8.append('mpp.gpdb.tests.storage.lib.dbstate.DbStateClass.run_validation')
self.test_case_scenario.append(test_case_list8)
test_case_list9 = []
test_case_list9.append('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.cleanup_dangling_processes')
self.test_case_scenario.append(test_case_list9)<|docstring|>@param skip_state : skip/noskip checkpoint
@param cluster_state : sync/change_tracking/resync
@param ddl_type : create/drop
@fault_type : commit/abort . Uses the same parameter to pass in 'end_prepare_two_phase_sleep'
@crash_type : gpstop_i/gpstop_a/failover_to_primary/failover_to_mirror
@description: Test the state of transactions after fault on master at diff stages followed by crash-recovery.
Faults are used to suspend the transactions at three stages
1. After prepare has been sent by master and acknowledge by all workers
2. After distributed commit has been flushed to xlog on master
3. After commit prepared has been sent out acknowledged before distributed forget
Steps:
0. Check the state of the cluster before proceeding the test execution
1. Run pre_sqls
2. Run any faults required before the trigger_sqls based on the fault_type as well as cluster_state
3. Run switch_checkpoint loop. switch_xlog, checkpoint in case of 'dtm_broadcast_prepare' fault
4. Crash and recover. Resume suspended faults if needed
5. Run validate_loop
7. Recover the cluster in case if needed
8. Validate using gpcheckcat and gpcheckmirrorseg<|endoftext|> |
e82215c222b253e794fe66701d9d18ebfc30d092c38152c9317e49594b8b7775 | def switch_checkpoint_serial(self, cluster_state, fault_type, crash_type='gpstop_i'):
'\n @description: Tests with more switch_xlogs before trigger sqls\n '
test_case_list0 = []
test_case_list0.append('mpp.gpdb.tests.storage.lib.dbstate.DbStateClass.check_system')
self.test_case_scenario.append(test_case_list0)
test_case_list1 = []
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_serial.pre_sql.test_presqls.TestPreSQLClass')
self.test_case_scenario.append(test_case_list1)
test_case_list2 = []
test_case_list2.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_faults_before_trigger', [cluster_state, fault_type]))
self.test_case_scenario.append(test_case_list2)
test_case_list3 = []
test_case_list3.append('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_switch_xlog')
self.test_case_scenario.append(test_case_list3)
test_dir = 'switch_ckpt_serial'
test_case_list4 = []
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_serial.trigger_sql.test_triggersqls.TestTriggerSQLClass')
test_case_list4.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_crash_and_recover', [crash_type, fault_type, test_dir, cluster_state]))
self.test_case_scenario.append(test_case_list4)
test_case_list5 = []
test_case_list5.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_checkpoint_loop', [fault_type]))
self.test_case_scenario.append(test_case_list5)
test_case_list6 = []
test_case_list6.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.run_gprecover', [crash_type, cluster_state]))
self.test_case_scenario.append(test_case_list6)
if (fault_type not in 'dtm_broadcast_prepare'):
test_case_list7 = []
test_case_list7.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_serial.post_sql.test_postsqls.TestPostSQLClass')
self.test_case_scenario.append(test_case_list7)
test_case_list8 = []
test_case_list8.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_serial.cleanup_sql.test_cleanup.TestCleanupClass')
self.test_case_scenario.append(test_case_list8)
test_case_list9 = []
test_case_list9.append('mpp.gpdb.tests.storage.lib.dbstate.DbStateClass.run_validation')
self.test_case_scenario.append(test_case_list9)
test_case_list10 = []
test_case_list10.append('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.cleanup_dangling_processes')
self.test_case_scenario.append(test_case_list10) | @description: Tests with more switch_xlogs before trigger sqls | src/test/tinc/tincrepo/mpp/gpdb/tests/storage/pg_twophase/switch_checkpoint.py | switch_checkpoint_serial | lintzc/GPDB | 1 | python | def switch_checkpoint_serial(self, cluster_state, fault_type, crash_type='gpstop_i'):
'\n \n '
test_case_list0 = []
test_case_list0.append('mpp.gpdb.tests.storage.lib.dbstate.DbStateClass.check_system')
self.test_case_scenario.append(test_case_list0)
test_case_list1 = []
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_serial.pre_sql.test_presqls.TestPreSQLClass')
self.test_case_scenario.append(test_case_list1)
test_case_list2 = []
test_case_list2.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_faults_before_trigger', [cluster_state, fault_type]))
self.test_case_scenario.append(test_case_list2)
test_case_list3 = []
test_case_list3.append('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_switch_xlog')
self.test_case_scenario.append(test_case_list3)
test_dir = 'switch_ckpt_serial'
test_case_list4 = []
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_serial.trigger_sql.test_triggersqls.TestTriggerSQLClass')
test_case_list4.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_crash_and_recover', [crash_type, fault_type, test_dir, cluster_state]))
self.test_case_scenario.append(test_case_list4)
test_case_list5 = []
test_case_list5.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_checkpoint_loop', [fault_type]))
self.test_case_scenario.append(test_case_list5)
test_case_list6 = []
test_case_list6.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.run_gprecover', [crash_type, cluster_state]))
self.test_case_scenario.append(test_case_list6)
if (fault_type not in 'dtm_broadcast_prepare'):
test_case_list7 = []
test_case_list7.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_serial.post_sql.test_postsqls.TestPostSQLClass')
self.test_case_scenario.append(test_case_list7)
test_case_list8 = []
test_case_list8.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_serial.cleanup_sql.test_cleanup.TestCleanupClass')
self.test_case_scenario.append(test_case_list8)
test_case_list9 = []
test_case_list9.append('mpp.gpdb.tests.storage.lib.dbstate.DbStateClass.run_validation')
self.test_case_scenario.append(test_case_list9)
test_case_list10 = []
test_case_list10.append('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.cleanup_dangling_processes')
self.test_case_scenario.append(test_case_list10) | def switch_checkpoint_serial(self, cluster_state, fault_type, crash_type='gpstop_i'):
'\n \n '
test_case_list0 = []
test_case_list0.append('mpp.gpdb.tests.storage.lib.dbstate.DbStateClass.check_system')
self.test_case_scenario.append(test_case_list0)
test_case_list1 = []
test_case_list1.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_serial.pre_sql.test_presqls.TestPreSQLClass')
self.test_case_scenario.append(test_case_list1)
test_case_list2 = []
test_case_list2.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_faults_before_trigger', [cluster_state, fault_type]))
self.test_case_scenario.append(test_case_list2)
test_case_list3 = []
test_case_list3.append('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_switch_xlog')
self.test_case_scenario.append(test_case_list3)
test_dir = 'switch_ckpt_serial'
test_case_list4 = []
test_case_list4.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_serial.trigger_sql.test_triggersqls.TestTriggerSQLClass')
test_case_list4.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_ckpt_crash_and_recover', [crash_type, fault_type, test_dir, cluster_state]))
self.test_case_scenario.append(test_case_list4)
test_case_list5 = []
test_case_list5.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.switch_checkpoint_loop', [fault_type]))
self.test_case_scenario.append(test_case_list5)
test_case_list6 = []
test_case_list6.append(('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.run_gprecover', [crash_type, cluster_state]))
self.test_case_scenario.append(test_case_list6)
if (fault_type not in 'dtm_broadcast_prepare'):
test_case_list7 = []
test_case_list7.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_serial.post_sql.test_postsqls.TestPostSQLClass')
self.test_case_scenario.append(test_case_list7)
test_case_list8 = []
test_case_list8.append('mpp.gpdb.tests.storage.pg_twophase.switch_ckpt_serial.cleanup_sql.test_cleanup.TestCleanupClass')
self.test_case_scenario.append(test_case_list8)
test_case_list9 = []
test_case_list9.append('mpp.gpdb.tests.storage.lib.dbstate.DbStateClass.run_validation')
self.test_case_scenario.append(test_case_list9)
test_case_list10 = []
test_case_list10.append('mpp.gpdb.tests.storage.pg_twophase.PgtwoPhaseClass.cleanup_dangling_processes')
self.test_case_scenario.append(test_case_list10)<|docstring|>@description: Tests with more switch_xlogs before trigger sqls<|endoftext|> |
75c0b3643a99936901d57fcab626c44ea420db2fe25438880048cec245f93dc0 | def path(self):
'Return the path to the file that backs this database.'
return self.path | Return the path to the file that backs this database. | parlai/agents/tfidf_retriever/doc_db.py | path | ShaojieJiang/FACE_orig | 1 | python | def path(self):
return self.path | def path(self):
return self.path<|docstring|>Return the path to the file that backs this database.<|endoftext|> |
9bb1b2a9436426ad9cd5fbfb93617806c0da10b343f8b8192425d649d0a9f597 | def close(self):
'Close the connection to the database.'
self.connection.close() | Close the connection to the database. | parlai/agents/tfidf_retriever/doc_db.py | close | ShaojieJiang/FACE_orig | 1 | python | def close(self):
self.connection.close() | def close(self):
self.connection.close()<|docstring|>Close the connection to the database.<|endoftext|> |
96aa5b4c40ba3ccfaca6e3d7ee9ff304e79610dac86e880e1543265d3e898922 | def get_doc_ids(self):
'Fetch all ids of docs stored in the db.'
cursor = self.connection.cursor()
cursor.execute('SELECT id FROM documents')
results = [r[0] for r in cursor.fetchall()]
cursor.close()
return results | Fetch all ids of docs stored in the db. | parlai/agents/tfidf_retriever/doc_db.py | get_doc_ids | ShaojieJiang/FACE_orig | 1 | python | def get_doc_ids(self):
cursor = self.connection.cursor()
cursor.execute('SELECT id FROM documents')
results = [r[0] for r in cursor.fetchall()]
cursor.close()
return results | def get_doc_ids(self):
cursor = self.connection.cursor()
cursor.execute('SELECT id FROM documents')
results = [r[0] for r in cursor.fetchall()]
cursor.close()
return results<|docstring|>Fetch all ids of docs stored in the db.<|endoftext|> |
0ff74c0d51b1960aac61de131289b11c31625c13f7aabf625f3c318ab0318502 | def get_doc_text(self, doc_id):
"Fetch the raw text of the doc for 'doc_id'."
cursor = self.connection.cursor()
cursor.execute('SELECT text FROM documents WHERE id = ?', (utils.normalize(doc_id),))
result = cursor.fetchone()
cursor.close()
return (result if (result is None) else result[0]) | Fetch the raw text of the doc for 'doc_id'. | parlai/agents/tfidf_retriever/doc_db.py | get_doc_text | ShaojieJiang/FACE_orig | 1 | python | def get_doc_text(self, doc_id):
cursor = self.connection.cursor()
cursor.execute('SELECT text FROM documents WHERE id = ?', (utils.normalize(doc_id),))
result = cursor.fetchone()
cursor.close()
return (result if (result is None) else result[0]) | def get_doc_text(self, doc_id):
cursor = self.connection.cursor()
cursor.execute('SELECT text FROM documents WHERE id = ?', (utils.normalize(doc_id),))
result = cursor.fetchone()
cursor.close()
return (result if (result is None) else result[0])<|docstring|>Fetch the raw text of the doc for 'doc_id'.<|endoftext|> |
4fbb7d937a2f5b290a7e68e36fcc86dd4b973e4aa2aefac948c9865927f3bbc0 | def get_doc_value(self, doc_id):
"Fetch the raw text of the doc for 'doc_id'."
cursor = self.connection.cursor()
cursor.execute('SELECT value FROM documents WHERE id = ?', (utils.normalize(doc_id),))
result = cursor.fetchone()
cursor.close()
return (result if (result is None) else result[0]) | Fetch the raw text of the doc for 'doc_id'. | parlai/agents/tfidf_retriever/doc_db.py | get_doc_value | ShaojieJiang/FACE_orig | 1 | python | def get_doc_value(self, doc_id):
cursor = self.connection.cursor()
cursor.execute('SELECT value FROM documents WHERE id = ?', (utils.normalize(doc_id),))
result = cursor.fetchone()
cursor.close()
return (result if (result is None) else result[0]) | def get_doc_value(self, doc_id):
cursor = self.connection.cursor()
cursor.execute('SELECT value FROM documents WHERE id = ?', (utils.normalize(doc_id),))
result = cursor.fetchone()
cursor.close()
return (result if (result is None) else result[0])<|docstring|>Fetch the raw text of the doc for 'doc_id'.<|endoftext|> |
57ac1a9a9e219b594c502d781a6bdd0579ce5943cf07d2bbe53bac57bcf6b7de | def task_id_arg(f: Optional[Callable]=None, *, required=True):
'\n By default, the task ID is made required; pass `required=False` to the\n decorator arguments to make it optional.\n '
if (f is None):
return functools.partial(task_id_arg, required=required)
return click.argument('TASK_ID', required=required)(f) | By default, the task ID is made required; pass `required=False` to the
decorator arguments to make it optional. | src/globus_cli/commands/task/_common.py | task_id_arg | sirosen/temp-cli-test | 47 | python | def task_id_arg(f: Optional[Callable]=None, *, required=True):
'\n By default, the task ID is made required; pass `required=False` to the\n decorator arguments to make it optional.\n '
if (f is None):
return functools.partial(task_id_arg, required=required)
return click.argument('TASK_ID', required=required)(f) | def task_id_arg(f: Optional[Callable]=None, *, required=True):
'\n By default, the task ID is made required; pass `required=False` to the\n decorator arguments to make it optional.\n '
if (f is None):
return functools.partial(task_id_arg, required=required)
return click.argument('TASK_ID', required=required)(f)<|docstring|>By default, the task ID is made required; pass `required=False` to the
decorator arguments to make it optional.<|endoftext|> |
f001155a1f1255ed25bfc4cd031bc4c666dfcf223347cf20f1af84ec9221c165 | def hook_makeOutline(VO, blines):
'Return (tlines, bnodes, levels) for Body lines blines.\n blines is either Vim buffer object (Body) or list of buffer lines.\n '
Z = len(blines)
(tlines, bnodes, levels) = ([], [], [])
(tlines_add, bnodes_add, levels_add) = (tlines.append, bnodes.append, levels.append)
isFenced = False
for i in xrange(Z):
bline = blines[i]
if isFenced:
if re.match(isFenced, bline):
isFenced = False
continue
if (bline.lstrip().startswith('#') and ('<<' in bline)):
r_m = region_match(bline)
if (r_m and (r_m.group(1) != 'Region')):
isFenced = ('^\\s*%s\\s*$' % re.escape((r_m.group(3) or '')))
continue
elif (not bline.startswith('*')):
continue
m = headline_match(bline)
if (not m):
continue
lev = len(m.group(1))
head = bline[lev:].strip()
tline = (' %s|%s' % (('. ' * (lev - 1)), head))
tlines_add(tline)
bnodes_add((i + 1))
levels_add(lev)
return (tlines, bnodes, levels) | Return (tlines, bnodes, levels) for Body lines blines.
blines is either Vim buffer object (Body) or list of buffer lines. | vimsetting/bundle/VOom/autoload/voom/voom_mode_viki.py | hook_makeOutline | thuleqaid/boost_study | 1 | python | def hook_makeOutline(VO, blines):
'Return (tlines, bnodes, levels) for Body lines blines.\n blines is either Vim buffer object (Body) or list of buffer lines.\n '
Z = len(blines)
(tlines, bnodes, levels) = ([], [], [])
(tlines_add, bnodes_add, levels_add) = (tlines.append, bnodes.append, levels.append)
isFenced = False
for i in xrange(Z):
bline = blines[i]
if isFenced:
if re.match(isFenced, bline):
isFenced = False
continue
if (bline.lstrip().startswith('#') and ('<<' in bline)):
r_m = region_match(bline)
if (r_m and (r_m.group(1) != 'Region')):
isFenced = ('^\\s*%s\\s*$' % re.escape((r_m.group(3) or )))
continue
elif (not bline.startswith('*')):
continue
m = headline_match(bline)
if (not m):
continue
lev = len(m.group(1))
head = bline[lev:].strip()
tline = (' %s|%s' % (('. ' * (lev - 1)), head))
tlines_add(tline)
bnodes_add((i + 1))
levels_add(lev)
return (tlines, bnodes, levels) | def hook_makeOutline(VO, blines):
'Return (tlines, bnodes, levels) for Body lines blines.\n blines is either Vim buffer object (Body) or list of buffer lines.\n '
Z = len(blines)
(tlines, bnodes, levels) = ([], [], [])
(tlines_add, bnodes_add, levels_add) = (tlines.append, bnodes.append, levels.append)
isFenced = False
for i in xrange(Z):
bline = blines[i]
if isFenced:
if re.match(isFenced, bline):
isFenced = False
continue
if (bline.lstrip().startswith('#') and ('<<' in bline)):
r_m = region_match(bline)
if (r_m and (r_m.group(1) != 'Region')):
isFenced = ('^\\s*%s\\s*$' % re.escape((r_m.group(3) or )))
continue
elif (not bline.startswith('*')):
continue
m = headline_match(bline)
if (not m):
continue
lev = len(m.group(1))
head = bline[lev:].strip()
tline = (' %s|%s' % (('. ' * (lev - 1)), head))
tlines_add(tline)
bnodes_add((i + 1))
levels_add(lev)
return (tlines, bnodes, levels)<|docstring|>Return (tlines, bnodes, levels) for Body lines blines.
blines is either Vim buffer object (Body) or list of buffer lines.<|endoftext|> |
63759670897a08280b39539f2b49bc34ba3708257b22a1fc5ba2de8a08404d71 | def hook_newHeadline(VO, level, blnum, tlnum):
'Return (tree_head, bodyLines).\n tree_head is new headline string in Tree buffer (text after |).\n bodyLines is list of lines to insert in Body buffer.\n '
tree_head = 'NewHeadline'
bodyLines = [('%s %s' % (('*' * level), tree_head)), '']
return (tree_head, bodyLines) | Return (tree_head, bodyLines).
tree_head is new headline string in Tree buffer (text after |).
bodyLines is list of lines to insert in Body buffer. | vimsetting/bundle/VOom/autoload/voom/voom_mode_viki.py | hook_newHeadline | thuleqaid/boost_study | 1 | python | def hook_newHeadline(VO, level, blnum, tlnum):
'Return (tree_head, bodyLines).\n tree_head is new headline string in Tree buffer (text after |).\n bodyLines is list of lines to insert in Body buffer.\n '
tree_head = 'NewHeadline'
bodyLines = [('%s %s' % (('*' * level), tree_head)), ]
return (tree_head, bodyLines) | def hook_newHeadline(VO, level, blnum, tlnum):
'Return (tree_head, bodyLines).\n tree_head is new headline string in Tree buffer (text after |).\n bodyLines is list of lines to insert in Body buffer.\n '
tree_head = 'NewHeadline'
bodyLines = [('%s %s' % (('*' * level), tree_head)), ]
return (tree_head, bodyLines)<|docstring|>Return (tree_head, bodyLines).
tree_head is new headline string in Tree buffer (text after |).
bodyLines is list of lines to insert in Body buffer.<|endoftext|> |
89dd06d603c8fff41a3730459a31646085cf7cac0e411e98f53afe348baae657 | def hook_changeLevBodyHead(VO, h, levDelta):
'Increase of decrease level number of Body headline by levDelta.'
if (levDelta == 0):
return h
m = headline_match(h)
level = len(m.group(1))
return ('%s%s' % (('*' * (level + levDelta)), h[m.end(1):])) | Increase of decrease level number of Body headline by levDelta. | vimsetting/bundle/VOom/autoload/voom/voom_mode_viki.py | hook_changeLevBodyHead | thuleqaid/boost_study | 1 | python | def hook_changeLevBodyHead(VO, h, levDelta):
if (levDelta == 0):
return h
m = headline_match(h)
level = len(m.group(1))
return ('%s%s' % (('*' * (level + levDelta)), h[m.end(1):])) | def hook_changeLevBodyHead(VO, h, levDelta):
if (levDelta == 0):
return h
m = headline_match(h)
level = len(m.group(1))
return ('%s%s' % (('*' * (level + levDelta)), h[m.end(1):]))<|docstring|>Increase of decrease level number of Body headline by levDelta.<|endoftext|> |
9c83d704153d25f57bc74f7e46873ee0fa50c36ba5ef6df1396515ea9b3707ce | def setup_states(self, state_dict, start_state):
'\n Given a dictionary of states and a state to start in,\n creates the self.state_dict.\n '
self.state_dict = state_dict
self.state_name = start_state
self.state = self.state_dict[self.state_name] | Given a dictionary of states and a state to start in,
creates the self.state_dict. | state_machine.py | setup_states | maxerbox/Lunar-Lander-with-pygame | 2 | python | def setup_states(self, state_dict, start_state):
'\n Given a dictionary of states and a state to start in,\n creates the self.state_dict.\n '
self.state_dict = state_dict
self.state_name = start_state
self.state = self.state_dict[self.state_name] | def setup_states(self, state_dict, start_state):
'\n Given a dictionary of states and a state to start in,\n creates the self.state_dict.\n '
self.state_dict = state_dict
self.state_name = start_state
self.state = self.state_dict[self.state_name]<|docstring|>Given a dictionary of states and a state to start in,
creates the self.state_dict.<|endoftext|> |
a4669dc6c8e39a49162bffcc1eea42f43299b4667db22e019b715cdc56d936d4 | def update(self, keys, now):
'\n Checks if a state is done or has called for a game quit.\n State is flipped if neccessary and State.update is called.\n '
self.now = now
if self.state.quit:
self.done = True
elif self.state.done:
self.flip_state()
self.state.update(keys, now) | Checks if a state is done or has called for a game quit.
State is flipped if neccessary and State.update is called. | state_machine.py | update | maxerbox/Lunar-Lander-with-pygame | 2 | python | def update(self, keys, now):
'\n Checks if a state is done or has called for a game quit.\n State is flipped if neccessary and State.update is called.\n '
self.now = now
if self.state.quit:
self.done = True
elif self.state.done:
self.flip_state()
self.state.update(keys, now) | def update(self, keys, now):
'\n Checks if a state is done or has called for a game quit.\n State is flipped if neccessary and State.update is called.\n '
self.now = now
if self.state.quit:
self.done = True
elif self.state.done:
self.flip_state()
self.state.update(keys, now)<|docstring|>Checks if a state is done or has called for a game quit.
State is flipped if neccessary and State.update is called.<|endoftext|> |
7890b25cdc5f4decbb3a3dae027a6632c19c5a09d8ef803b291330daf447fb0d | def flip_state(self):
'\n When a State changes to done necessary startup and cleanup functions\n are called and the current State is changed.\n '
(previous, self.state_name) = (self.state_name, self.state.next)
persist = self.state.cleanup()
self.state = self.state_dict[self.state_name]
self.state.startup(self.now, persist)
self.state.previous = previous | When a State changes to done necessary startup and cleanup functions
are called and the current State is changed. | state_machine.py | flip_state | maxerbox/Lunar-Lander-with-pygame | 2 | python | def flip_state(self):
'\n When a State changes to done necessary startup and cleanup functions\n are called and the current State is changed.\n '
(previous, self.state_name) = (self.state_name, self.state.next)
persist = self.state.cleanup()
self.state = self.state_dict[self.state_name]
self.state.startup(self.now, persist)
self.state.previous = previous | def flip_state(self):
'\n When a State changes to done necessary startup and cleanup functions\n are called and the current State is changed.\n '
(previous, self.state_name) = (self.state_name, self.state.next)
persist = self.state.cleanup()
self.state = self.state_dict[self.state_name]
self.state.startup(self.now, persist)
self.state.previous = previous<|docstring|>When a State changes to done necessary startup and cleanup functions
are called and the current State is changed.<|endoftext|> |
ac11a630835095a8d3d1c5fd23528a275f658aa34db784ed736cdfbe694bd7dc | def get_event(self, event):
'\n Pass events down to current State.\n '
self.state.get_event(event) | Pass events down to current State. | state_machine.py | get_event | maxerbox/Lunar-Lander-with-pygame | 2 | python | def get_event(self, event):
'\n \n '
self.state.get_event(event) | def get_event(self, event):
'\n \n '
self.state.get_event(event)<|docstring|>Pass events down to current State.<|endoftext|> |
2e7d888822bfc91457c9b06dd5010cffc366d0e7c9a08ae9e92af31410f71bf4 | def get_event(self, event):
'\n Processes events that were passed from the main event loop.\n Must be overloaded in children.\n '
pass | Processes events that were passed from the main event loop.
Must be overloaded in children. | state_machine.py | get_event | maxerbox/Lunar-Lander-with-pygame | 2 | python | def get_event(self, event):
'\n Processes events that were passed from the main event loop.\n Must be overloaded in children.\n '
pass | def get_event(self, event):
'\n Processes events that were passed from the main event loop.\n Must be overloaded in children.\n '
pass<|docstring|>Processes events that were passed from the main event loop.
Must be overloaded in children.<|endoftext|> |
be5d3c5fff0b017c2cecdce451d4b5d91939936fb70053f098d50d7d7095107d | def startup(self, now, persistant):
'\n Add variables passed in persistant to the proper attributes and\n set the start time of the State to the current time.\n '
self.persist = persistant
self.start_time = now | Add variables passed in persistant to the proper attributes and
set the start time of the State to the current time. | state_machine.py | startup | maxerbox/Lunar-Lander-with-pygame | 2 | python | def startup(self, now, persistant):
'\n Add variables passed in persistant to the proper attributes and\n set the start time of the State to the current time.\n '
self.persist = persistant
self.start_time = now | def startup(self, now, persistant):
'\n Add variables passed in persistant to the proper attributes and\n set the start time of the State to the current time.\n '
self.persist = persistant
self.start_time = now<|docstring|>Add variables passed in persistant to the proper attributes and
set the start time of the State to the current time.<|endoftext|> |
0a408402c272943379f0dabe874c7a5b5e095f09f6d0df76eb1510063662c643 | def cleanup(self):
'\n Add variables that should persist to the self.persist dictionary.\n Then reset State.done to False.\n '
self.done = False
return self.persist | Add variables that should persist to the self.persist dictionary.
Then reset State.done to False. | state_machine.py | cleanup | maxerbox/Lunar-Lander-with-pygame | 2 | python | def cleanup(self):
'\n Add variables that should persist to the self.persist dictionary.\n Then reset State.done to False.\n '
self.done = False
return self.persist | def cleanup(self):
'\n Add variables that should persist to the self.persist dictionary.\n Then reset State.done to False.\n '
self.done = False
return self.persist<|docstring|>Add variables that should persist to the self.persist dictionary.
Then reset State.done to False.<|endoftext|> |
96c50c7485d9eec179dc680b2436ad6ffba63f63c89013c43ee0b0eb3f8dc6a8 | def update(self, keys, now):
'Update function for state. Must be overloaded in children.'
pass | Update function for state. Must be overloaded in children. | state_machine.py | update | maxerbox/Lunar-Lander-with-pygame | 2 | python | def update(self, keys, now):
pass | def update(self, keys, now):
pass<|docstring|>Update function for state. Must be overloaded in children.<|endoftext|> |
2b34d8a31ec55d158a64d98f0ab90eaa8a862efd3e281dc973de1ead128858aa | @pytest.mark.sanity
def test_kill_essential():
'kill the essential task, verify that both tasks are relaunched against a matching executor'
verify_shared_executor('hello-0')
old_tasks = sdk_tasks.get_service_tasks(config.SERVICE_NAME, 'hello-0')
assert (len(old_tasks) == 2)
sdk_cmd.kill_task_with_pattern('shared-volume/essential', 'nobody', agent_host=old_tasks[0].host)
sdk_tasks.check_tasks_updated(config.SERVICE_NAME, 'hello-0', [t.id for t in old_tasks])
sdk_plan.wait_for_completed_recovery(config.SERVICE_NAME)
verify_shared_executor('hello-0', delete_files=False) | kill the essential task, verify that both tasks are relaunched against a matching executor | frameworks/helloworld/tests/test_nonessential_tasks.py | test_kill_essential | kaiwalyajoshi/dcos-commons | 201 | python | @pytest.mark.sanity
def test_kill_essential():
verify_shared_executor('hello-0')
old_tasks = sdk_tasks.get_service_tasks(config.SERVICE_NAME, 'hello-0')
assert (len(old_tasks) == 2)
sdk_cmd.kill_task_with_pattern('shared-volume/essential', 'nobody', agent_host=old_tasks[0].host)
sdk_tasks.check_tasks_updated(config.SERVICE_NAME, 'hello-0', [t.id for t in old_tasks])
sdk_plan.wait_for_completed_recovery(config.SERVICE_NAME)
verify_shared_executor('hello-0', delete_files=False) | @pytest.mark.sanity
def test_kill_essential():
verify_shared_executor('hello-0')
old_tasks = sdk_tasks.get_service_tasks(config.SERVICE_NAME, 'hello-0')
assert (len(old_tasks) == 2)
sdk_cmd.kill_task_with_pattern('shared-volume/essential', 'nobody', agent_host=old_tasks[0].host)
sdk_tasks.check_tasks_updated(config.SERVICE_NAME, 'hello-0', [t.id for t in old_tasks])
sdk_plan.wait_for_completed_recovery(config.SERVICE_NAME)
verify_shared_executor('hello-0', delete_files=False)<|docstring|>kill the essential task, verify that both tasks are relaunched against a matching executor<|endoftext|> |
f5d8f57b1e4bf42c5b472f4c23900952c1017fb6e22d9e13d33ccfc420151525 | @pytest.mark.sanity
def test_kill_nonessential():
'kill the nonessential task, verify that the nonessential task is relaunched against the same executor as before'
verify_shared_executor('hello-0')
old_tasks = sdk_tasks.get_service_tasks(config.SERVICE_NAME, 'hello-0')
assert (len(old_tasks) == 2)
old_essential_task = [t for t in old_tasks if (t.name == 'hello-0-essential')][0]
old_nonessential_task = [t for t in old_tasks if (t.name == 'hello-0-nonessential')][0]
sdk_cmd.kill_task_with_pattern('shared-volume/nonessential', 'nobody', agent_host=old_nonessential_task.host)
sdk_tasks.check_tasks_updated(config.SERVICE_NAME, 'hello-0-nonessential', [old_nonessential_task.id])
sdk_plan.wait_for_completed_recovery(config.SERVICE_NAME)
sdk_tasks.check_tasks_not_updated(config.SERVICE_NAME, 'hello-0-essential', [old_essential_task.id])
verify_shared_executor('hello-0', expected_files=['nonessential']) | kill the nonessential task, verify that the nonessential task is relaunched against the same executor as before | frameworks/helloworld/tests/test_nonessential_tasks.py | test_kill_nonessential | kaiwalyajoshi/dcos-commons | 201 | python | @pytest.mark.sanity
def test_kill_nonessential():
verify_shared_executor('hello-0')
old_tasks = sdk_tasks.get_service_tasks(config.SERVICE_NAME, 'hello-0')
assert (len(old_tasks) == 2)
old_essential_task = [t for t in old_tasks if (t.name == 'hello-0-essential')][0]
old_nonessential_task = [t for t in old_tasks if (t.name == 'hello-0-nonessential')][0]
sdk_cmd.kill_task_with_pattern('shared-volume/nonessential', 'nobody', agent_host=old_nonessential_task.host)
sdk_tasks.check_tasks_updated(config.SERVICE_NAME, 'hello-0-nonessential', [old_nonessential_task.id])
sdk_plan.wait_for_completed_recovery(config.SERVICE_NAME)
sdk_tasks.check_tasks_not_updated(config.SERVICE_NAME, 'hello-0-essential', [old_essential_task.id])
verify_shared_executor('hello-0', expected_files=['nonessential']) | @pytest.mark.sanity
def test_kill_nonessential():
verify_shared_executor('hello-0')
old_tasks = sdk_tasks.get_service_tasks(config.SERVICE_NAME, 'hello-0')
assert (len(old_tasks) == 2)
old_essential_task = [t for t in old_tasks if (t.name == 'hello-0-essential')][0]
old_nonessential_task = [t for t in old_tasks if (t.name == 'hello-0-nonessential')][0]
sdk_cmd.kill_task_with_pattern('shared-volume/nonessential', 'nobody', agent_host=old_nonessential_task.host)
sdk_tasks.check_tasks_updated(config.SERVICE_NAME, 'hello-0-nonessential', [old_nonessential_task.id])
sdk_plan.wait_for_completed_recovery(config.SERVICE_NAME)
sdk_tasks.check_tasks_not_updated(config.SERVICE_NAME, 'hello-0-essential', [old_essential_task.id])
verify_shared_executor('hello-0', expected_files=['nonessential'])<|docstring|>kill the nonessential task, verify that the nonessential task is relaunched against the same executor as before<|endoftext|> |
282ad76be48bdc8e25f58be77862db710e7e064298bfd2aeab0f7864614d4e6f | def verify_shared_executor(pod_name, expected_files=['essential', 'nonessential'], delete_files=True):
"verify that both tasks share the same executor:\n - matching ExecutorInfo\n - both 'essential' and 'nonessential' present in shared-volume/ across both tasks\n "
(rc, stdout, _) = sdk_cmd.svc_cli(config.PACKAGE_NAME, config.SERVICE_NAME, 'pod info {}'.format(pod_name), print_output=False)
assert (rc == 0), 'Pod info failed'
try:
tasks = json.loads(stdout)
except Exception:
log.exception('Failed to parse pod info: {}'.format(stdout))
assert False, 'Failed to parse pod info, see above'
assert (len(tasks) == 2), 'Expected 2 tasks: {}'.format(stdout)
executor = tasks[0]['info']['executor']
for task in tasks[1:]:
assert (executor == task['info']['executor'])
task_names = [task['info']['name'] for task in tasks]
for task_name in task_names:
filenames = sdk_cmd.run_cli('task ls {} shared-volume/'.format(task_name))[1].strip().split()
assert (set(expected_files) == set(filenames))
if delete_files:
if sdk_utils.dcos_version_less_than('1.10'):
expected_file_path = sdk_cmd.service_task_exec(config.SERVICE_NAME, task_names[0], ('find /var/lib/mesos/slave/volumes -iname ' + filenames[0]))[1].strip()
volume_dir = os.path.dirname(expected_file_path)
else:
volume_dir = 'shared-volume/'
sdk_cmd.service_task_exec(config.SERVICE_NAME, task_names[0], ('rm ' + ' '.join([os.path.join(volume_dir, name) for name in filenames]))) | verify that both tasks share the same executor:
- matching ExecutorInfo
- both 'essential' and 'nonessential' present in shared-volume/ across both tasks | frameworks/helloworld/tests/test_nonessential_tasks.py | verify_shared_executor | kaiwalyajoshi/dcos-commons | 201 | python | def verify_shared_executor(pod_name, expected_files=['essential', 'nonessential'], delete_files=True):
"verify that both tasks share the same executor:\n - matching ExecutorInfo\n - both 'essential' and 'nonessential' present in shared-volume/ across both tasks\n "
(rc, stdout, _) = sdk_cmd.svc_cli(config.PACKAGE_NAME, config.SERVICE_NAME, 'pod info {}'.format(pod_name), print_output=False)
assert (rc == 0), 'Pod info failed'
try:
tasks = json.loads(stdout)
except Exception:
log.exception('Failed to parse pod info: {}'.format(stdout))
assert False, 'Failed to parse pod info, see above'
assert (len(tasks) == 2), 'Expected 2 tasks: {}'.format(stdout)
executor = tasks[0]['info']['executor']
for task in tasks[1:]:
assert (executor == task['info']['executor'])
task_names = [task['info']['name'] for task in tasks]
for task_name in task_names:
filenames = sdk_cmd.run_cli('task ls {} shared-volume/'.format(task_name))[1].strip().split()
assert (set(expected_files) == set(filenames))
if delete_files:
if sdk_utils.dcos_version_less_than('1.10'):
expected_file_path = sdk_cmd.service_task_exec(config.SERVICE_NAME, task_names[0], ('find /var/lib/mesos/slave/volumes -iname ' + filenames[0]))[1].strip()
volume_dir = os.path.dirname(expected_file_path)
else:
volume_dir = 'shared-volume/'
sdk_cmd.service_task_exec(config.SERVICE_NAME, task_names[0], ('rm ' + ' '.join([os.path.join(volume_dir, name) for name in filenames]))) | def verify_shared_executor(pod_name, expected_files=['essential', 'nonessential'], delete_files=True):
"verify that both tasks share the same executor:\n - matching ExecutorInfo\n - both 'essential' and 'nonessential' present in shared-volume/ across both tasks\n "
(rc, stdout, _) = sdk_cmd.svc_cli(config.PACKAGE_NAME, config.SERVICE_NAME, 'pod info {}'.format(pod_name), print_output=False)
assert (rc == 0), 'Pod info failed'
try:
tasks = json.loads(stdout)
except Exception:
log.exception('Failed to parse pod info: {}'.format(stdout))
assert False, 'Failed to parse pod info, see above'
assert (len(tasks) == 2), 'Expected 2 tasks: {}'.format(stdout)
executor = tasks[0]['info']['executor']
for task in tasks[1:]:
assert (executor == task['info']['executor'])
task_names = [task['info']['name'] for task in tasks]
for task_name in task_names:
filenames = sdk_cmd.run_cli('task ls {} shared-volume/'.format(task_name))[1].strip().split()
assert (set(expected_files) == set(filenames))
if delete_files:
if sdk_utils.dcos_version_less_than('1.10'):
expected_file_path = sdk_cmd.service_task_exec(config.SERVICE_NAME, task_names[0], ('find /var/lib/mesos/slave/volumes -iname ' + filenames[0]))[1].strip()
volume_dir = os.path.dirname(expected_file_path)
else:
volume_dir = 'shared-volume/'
sdk_cmd.service_task_exec(config.SERVICE_NAME, task_names[0], ('rm ' + ' '.join([os.path.join(volume_dir, name) for name in filenames])))<|docstring|>verify that both tasks share the same executor:
- matching ExecutorInfo
- both 'essential' and 'nonessential' present in shared-volume/ across both tasks<|endoftext|> |
9e2fc6b4ae5657a6c412cc5e5c331b5051ad9b2f0fec7a5f709d430c5c36cb03 | def log(message, prefix_newline=False):
'Logging function, provides a hook to suppress or redirect log messages.'
print((((('\n' if prefix_newline else '') + '{0:.2f}'.format(time.time())) + ': ') + str(message))) | Logging function, provides a hook to suppress or redirect log messages. | lib/kb_stringtie/Utils/file_utils.py | log | Tianhao-Gu/kb_stringtie | 1 | python | def log(message, prefix_newline=False):
print((((('\n' if prefix_newline else ) + '{0:.2f}'.format(time.time())) + ': ') + str(message))) | def log(message, prefix_newline=False):
print((((('\n' if prefix_newline else ) + '{0:.2f}'.format(time.time())) + ': ') + str(message)))<|docstring|>Logging function, provides a hook to suppress or redirect log messages.<|endoftext|> |
3ec2341300c534fdf19a040e9d01468f15563129645e208a367d27bbd1180a32 | def _make_gff(file_path, append_file, novel_prefix='MSTRG.'):
'Works on the very narrow case of gtf files from stringtie.'
type_idx = 2
written_genes = set()
timestamp = str(datetime.datetime.now()).split('.')[0]
if (not os.path.isfile(file_path)):
raise ValueError('{} is not a file'.format(file_path))
with open(append_file, 'a') as output_file:
with open(file_path, 'r') as input_file:
for line in input_file:
if (line[0] == '#'):
continue
sl = line.split('\t')
gene_id = sl[(- 1)].split('gene_id "')[1].split('"')[0]
transcript_id = line.split('transcript_id "')[1].split('"')[0]
if ('ref_gene_id "' in sl[(- 1)]):
gene_id = sl[(- 1)].split('ref_gene_id "')[1].split('"')[0]
elif ((sl[type_idx] == 'transcript') and (gene_id not in written_genes)):
sl[type_idx] = 'gene'
sl[(- 1)] = 'ID={}; note="Spoofed gene for a RNASeq transcript\n'.format(gene_id)
output_file.write('\t'.join(sl))
written_genes.add(gene_id)
if (novel_prefix in transcript_id):
if (sl[type_idx] == 'exon'):
sl[(- 1)] = 'Parent={}'.format(transcript_id)
elif gene_id:
sl[type_idx] = 'transcript'
sl[(- 1)] = 'ID={}; Parent={}'.format(transcript_id, gene_id)
sl[(- 1)] += '; note=Predicted transcript from RNASeq run on {}\n'.format(timestamp)
output_file.write('\t'.join(sl))
return append_file | Works on the very narrow case of gtf files from stringtie. | lib/kb_stringtie/Utils/file_utils.py | _make_gff | Tianhao-Gu/kb_stringtie | 1 | python | def _make_gff(file_path, append_file, novel_prefix='MSTRG.'):
type_idx = 2
written_genes = set()
timestamp = str(datetime.datetime.now()).split('.')[0]
if (not os.path.isfile(file_path)):
raise ValueError('{} is not a file'.format(file_path))
with open(append_file, 'a') as output_file:
with open(file_path, 'r') as input_file:
for line in input_file:
if (line[0] == '#'):
continue
sl = line.split('\t')
gene_id = sl[(- 1)].split('gene_id "')[1].split('"')[0]
transcript_id = line.split('transcript_id "')[1].split('"')[0]
if ('ref_gene_id "' in sl[(- 1)]):
gene_id = sl[(- 1)].split('ref_gene_id "')[1].split('"')[0]
elif ((sl[type_idx] == 'transcript') and (gene_id not in written_genes)):
sl[type_idx] = 'gene'
sl[(- 1)] = 'ID={}; note="Spoofed gene for a RNASeq transcript\n'.format(gene_id)
output_file.write('\t'.join(sl))
written_genes.add(gene_id)
if (novel_prefix in transcript_id):
if (sl[type_idx] == 'exon'):
sl[(- 1)] = 'Parent={}'.format(transcript_id)
elif gene_id:
sl[type_idx] = 'transcript'
sl[(- 1)] = 'ID={}; Parent={}'.format(transcript_id, gene_id)
sl[(- 1)] += '; note=Predicted transcript from RNASeq run on {}\n'.format(timestamp)
output_file.write('\t'.join(sl))
return append_file | def _make_gff(file_path, append_file, novel_prefix='MSTRG.'):
type_idx = 2
written_genes = set()
timestamp = str(datetime.datetime.now()).split('.')[0]
if (not os.path.isfile(file_path)):
raise ValueError('{} is not a file'.format(file_path))
with open(append_file, 'a') as output_file:
with open(file_path, 'r') as input_file:
for line in input_file:
if (line[0] == '#'):
continue
sl = line.split('\t')
gene_id = sl[(- 1)].split('gene_id "')[1].split('"')[0]
transcript_id = line.split('transcript_id "')[1].split('"')[0]
if ('ref_gene_id "' in sl[(- 1)]):
gene_id = sl[(- 1)].split('ref_gene_id "')[1].split('"')[0]
elif ((sl[type_idx] == 'transcript') and (gene_id not in written_genes)):
sl[type_idx] = 'gene'
sl[(- 1)] = 'ID={}; note="Spoofed gene for a RNASeq transcript\n'.format(gene_id)
output_file.write('\t'.join(sl))
written_genes.add(gene_id)
if (novel_prefix in transcript_id):
if (sl[type_idx] == 'exon'):
sl[(- 1)] = 'Parent={}'.format(transcript_id)
elif gene_id:
sl[type_idx] = 'transcript'
sl[(- 1)] = 'ID={}; Parent={}'.format(transcript_id, gene_id)
sl[(- 1)] += '; note=Predicted transcript from RNASeq run on {}\n'.format(timestamp)
output_file.write('\t'.join(sl))
return append_file<|docstring|>Works on the very narrow case of gtf files from stringtie.<|endoftext|> |
c20a49ff2d9924b7c5658f0799130e6dfa89832e5ea10f289127377ba8bb19b0 | def exchange_gene_ids(result_directory):
'\n exchange_gene_ids: exchange gene_ids with gene_name\n '
log('starting exchanging gene_ids with gene_name')
result_files = os.listdir(result_directory)
if ('transcripts.gtf' in result_files):
pass
_update_transcripts(result_directory)
if ('t_data.ctab' in result_files):
_update_t_data(result_directory) | exchange_gene_ids: exchange gene_ids with gene_name | lib/kb_stringtie/Utils/file_utils.py | exchange_gene_ids | Tianhao-Gu/kb_stringtie | 1 | python | def exchange_gene_ids(result_directory):
'\n \n '
log('starting exchanging gene_ids with gene_name')
result_files = os.listdir(result_directory)
if ('transcripts.gtf' in result_files):
pass
_update_transcripts(result_directory)
if ('t_data.ctab' in result_files):
_update_t_data(result_directory) | def exchange_gene_ids(result_directory):
'\n \n '
log('starting exchanging gene_ids with gene_name')
result_files = os.listdir(result_directory)
if ('transcripts.gtf' in result_files):
pass
_update_transcripts(result_directory)
if ('t_data.ctab' in result_files):
_update_t_data(result_directory)<|docstring|>exchange_gene_ids: exchange gene_ids with gene_name<|endoftext|> |
c7e24cb101d2ecf1d1fee272000176f2523b886e03c60dd7a9d7f112373a2df8 | def external_superset_url():
'The URL under which the Superset instance can be reached by users e.g. https://superset.bi.example.com'
return 'http://localhost:8088' | The URL under which the Superset instance can be reached by users e.g. https://superset.bi.example.com | mara_superset/config.py | external_superset_url | leo-schick/mara-superset | 3 | python | def external_superset_url():
return 'http://localhost:8088' | def external_superset_url():
return 'http://localhost:8088'<|docstring|>The URL under which the Superset instance can be reached by users e.g. https://superset.bi.example.com<|endoftext|> |
80b2e38b47e94dc5df702720aea9e0e2a4b9f92e491038dd4442070180d4da75 | def internal_superset_url():
'The URL under which the Superset instance can be reached by from mara (usually circumventing SSOs etc.)'
return 'http://localhost:8088' | The URL under which the Superset instance can be reached by from mara (usually circumventing SSOs etc.) | mara_superset/config.py | internal_superset_url | leo-schick/mara-superset | 3 | python | def internal_superset_url():
return 'http://localhost:8088' | def internal_superset_url():
return 'http://localhost:8088'<|docstring|>The URL under which the Superset instance can be reached by from mara (usually circumventing SSOs etc.)<|endoftext|> |
3a0b2f42f2607ac9228f33c87ca4d8ec4acb02046b83a88d0a426b5092b8c2c1 | def superset_api_username() -> str:
'The email of the user for accessing the superset api'
return 'admin' | The email of the user for accessing the superset api | mara_superset/config.py | superset_api_username | leo-schick/mara-superset | 3 | python | def superset_api_username() -> str:
return 'admin' | def superset_api_username() -> str:
return 'admin'<|docstring|>The email of the user for accessing the superset api<|endoftext|> |
559d1fb88e7de7ba74e3746ac7cfdad0115816e130bc1b17350d69a7996ee3b7 | def superset_api_password():
'The password of the user for accessing the superset api'
return 'admin' | The password of the user for accessing the superset api | mara_superset/config.py | superset_api_password | leo-schick/mara-superset | 3 | python | def superset_api_password():
return 'admin' | def superset_api_password():
return 'admin'<|docstring|>The password of the user for accessing the superset api<|endoftext|> |
d2897227210ce903f2a65fc59833162ea10463fecad483ac1a70766f249dce03 | def superset_data_db_alias() -> str:
'The alias of the database that Superset reads data from'
return 'superset-data-read' | The alias of the database that Superset reads data from | mara_superset/config.py | superset_data_db_alias | leo-schick/mara-superset | 3 | python | def superset_data_db_alias() -> str:
return 'superset-data-read' | def superset_data_db_alias() -> str:
return 'superset-data-read'<|docstring|>The alias of the database that Superset reads data from<|endoftext|> |
cf90c0ff64f15003ee795ce4ef527e2089083958b540c122a3b2feb526ed614b | def superset_data_db_name() -> str:
'The name (in Superset) of the database that Superset reads from'
return 'MyCompany DWH' | The name (in Superset) of the database that Superset reads from | mara_superset/config.py | superset_data_db_name | leo-schick/mara-superset | 3 | python | def superset_data_db_name() -> str:
return 'MyCompany DWH' | def superset_data_db_name() -> str:
return 'MyCompany DWH'<|docstring|>The name (in Superset) of the database that Superset reads from<|endoftext|> |
6fdef680fe9eee13a73c5f2b2c6f7013b875a990048bdeba5f875da545df0c89 | def superset_data_db_schema() -> str:
'The name of the schema where the flattered data sets for Superset are stored'
return 'superset' | The name of the schema where the flattered data sets for Superset are stored | mara_superset/config.py | superset_data_db_schema | leo-schick/mara-superset | 3 | python | def superset_data_db_schema() -> str:
return 'superset' | def superset_data_db_schema() -> str:
return 'superset'<|docstring|>The name of the schema where the flattered data sets for Superset are stored<|endoftext|> |
0aa56bb435c7a94c4a775eb27aa12b737c84fde634b1d53131401c7e2b901908 | def metadata_update_strategy():
'The default update strategy to be used when synchronizing metadata'
from .metadata import UpdateStrategy
return (UpdateStrategy.CREATE | UpdateStrategy.UPDATE) | The default update strategy to be used when synchronizing metadata | mara_superset/config.py | metadata_update_strategy | leo-schick/mara-superset | 3 | python | def metadata_update_strategy():
from .metadata import UpdateStrategy
return (UpdateStrategy.CREATE | UpdateStrategy.UPDATE) | def metadata_update_strategy():
from .metadata import UpdateStrategy
return (UpdateStrategy.CREATE | UpdateStrategy.UPDATE)<|docstring|>The default update strategy to be used when synchronizing metadata<|endoftext|> |
0099486041b246607d0ce8a93acdfb35dd0a80f27831a908276b15dbfc4200c1 | def __init__(self, configuration):
'\n Init OpsGenie client to interact with OpsGenie Api\n Parameters\n ----------\n configuration : Configuration\n '
if (not isinstance(configuration, Configuration)):
InvalidConfigurationError()
configuration.validate()
self.configuration = configuration
self._alertService = AlertService(configuration)
self._userService = UserService(configuration)
self._teamService = TeamService(configuration)
self._accountService = AccountService(configuration) | Init OpsGenie client to interact with OpsGenie Api
Parameters
----------
configuration : Configuration | opsgenie/client.py | __init__ | kumarappan-arumugam/opsgenie-py | 0 | python | def __init__(self, configuration):
'\n Init OpsGenie client to interact with OpsGenie Api\n Parameters\n ----------\n configuration : Configuration\n '
if (not isinstance(configuration, Configuration)):
InvalidConfigurationError()
configuration.validate()
self.configuration = configuration
self._alertService = AlertService(configuration)
self._userService = UserService(configuration)
self._teamService = TeamService(configuration)
self._accountService = AccountService(configuration) | def __init__(self, configuration):
'\n Init OpsGenie client to interact with OpsGenie Api\n Parameters\n ----------\n configuration : Configuration\n '
if (not isinstance(configuration, Configuration)):
InvalidConfigurationError()
configuration.validate()
self.configuration = configuration
self._alertService = AlertService(configuration)
self._userService = UserService(configuration)
self._teamService = TeamService(configuration)
self._accountService = AccountService(configuration)<|docstring|>Init OpsGenie client to interact with OpsGenie Api
Parameters
----------
configuration : Configuration<|endoftext|> |
6040ca45eb4d276391afd24ca9e248cb031690b33dd7674485264ba3f7891cda | @property
def accounts(self):
'\n Returns\n -------\n UserService\n '
return self._accountService | Returns
-------
UserService | opsgenie/client.py | accounts | kumarappan-arumugam/opsgenie-py | 0 | python | @property
def accounts(self):
'\n Returns\n -------\n UserService\n '
return self._accountService | @property
def accounts(self):
'\n Returns\n -------\n UserService\n '
return self._accountService<|docstring|>Returns
-------
UserService<|endoftext|> |
1734ff8eccff6317d9be1fdd4ebf8f2f9ebc8967e5553523c7426b63abaa29df | @property
def alerts(self):
'\n Returns\n -------\n AlertService\n '
return self._alertService | Returns
-------
AlertService | opsgenie/client.py | alerts | kumarappan-arumugam/opsgenie-py | 0 | python | @property
def alerts(self):
'\n Returns\n -------\n AlertService\n '
return self._alertService | @property
def alerts(self):
'\n Returns\n -------\n AlertService\n '
return self._alertService<|docstring|>Returns
-------
AlertService<|endoftext|> |
18b374b707e53a110050a6db3fe695e079920a931059ab83417bf0461fefccb6 | @property
def users(self):
'\n Returns\n -------\n UserService\n '
return self._userService | Returns
-------
UserService | opsgenie/client.py | users | kumarappan-arumugam/opsgenie-py | 0 | python | @property
def users(self):
'\n Returns\n -------\n UserService\n '
return self._userService | @property
def users(self):
'\n Returns\n -------\n UserService\n '
return self._userService<|docstring|>Returns
-------
UserService<|endoftext|> |
cc45179d9789f6b5d14cba831b48331cde94ae10ca237944e7ceff201f2a0c23 | @property
def teams(self):
'\n Returns\n -------\n UserService\n '
return self._teamService | Returns
-------
UserService | opsgenie/client.py | teams | kumarappan-arumugam/opsgenie-py | 0 | python | @property
def teams(self):
'\n Returns\n -------\n UserService\n '
return self._teamService | @property
def teams(self):
'\n Returns\n -------\n UserService\n '
return self._teamService<|docstring|>Returns
-------
UserService<|endoftext|> |
fc92f17d51175a2d6c0ba9c47bf9e8d2267eccb05ccc26d5901e99f70a3eed62 | def __init__(self, dataarray):
'Initialize the base accessor.'
self._dataarray = dataarray | Initialize the base accessor. | decode/core/__init__.py | __init__ | deshima-dev/decode | 6 | python | def __init__(self, dataarray):
self._dataarray = dataarray | def __init__(self, dataarray):
self._dataarray = dataarray<|docstring|>Initialize the base accessor.<|endoftext|> |
fe122eadd55052785a7f725c4507c59c529b94a6511b02441e417a7a4ec673a1 | def __getattr__(self, name):
'self._dataarray.name <=> self.name.'
return getattr(self._dataarray, name) | self._dataarray.name <=> self.name. | decode/core/__init__.py | __getattr__ | deshima-dev/decode | 6 | python | def __getattr__(self, name):
return getattr(self._dataarray, name) | def __getattr__(self, name):
return getattr(self._dataarray, name)<|docstring|>self._dataarray.name <=> self.name.<|endoftext|> |
f8cc134b17817f49487f46aecacb5d906de75f9ad55fa2f754f5ecaf7c10aff8 | def __setstate__(self, state):
'A method used for pickling.'
self.__dict__ = state | A method used for pickling. | decode/core/__init__.py | __setstate__ | deshima-dev/decode | 6 | python | def __setstate__(self, state):
self.__dict__ = state | def __setstate__(self, state):
self.__dict__ = state<|docstring|>A method used for pickling.<|endoftext|> |
27528e88eb42d705c9e8de3cfb61a2ca54a96c37ef3d56b48be412037acdbe1a | def __getstate__(self):
'A method used for unpickling.'
return self.__dict__ | A method used for unpickling. | decode/core/__init__.py | __getstate__ | deshima-dev/decode | 6 | python | def __getstate__(self):
return self.__dict__ | def __getstate__(self):
return self.__dict__<|docstring|>A method used for unpickling.<|endoftext|> |
794cbc5caa7db7dbc5c6068a1876292e09f2ff6574ff0c2ce5b4590493e005e8 | @property
def scalarcoords(self):
"A dictionary of values that don't label any axes (point-like)."
return {k: v.values for (k, v) in self.coords.items() if (v.dims == ())} | A dictionary of values that don't label any axes (point-like). | decode/core/__init__.py | scalarcoords | deshima-dev/decode | 6 | python | @property
def scalarcoords(self):
return {k: v.values for (k, v) in self.coords.items() if (v.dims == ())} | @property
def scalarcoords(self):
return {k: v.values for (k, v) in self.coords.items() if (v.dims == ())}<|docstring|>A dictionary of values that don't label any axes (point-like).<|endoftext|> |
186adefbfa75329c10effcae6691fbe5a0ebf258be0a5c978c60069a0b56fc97 | def runTest(self):
'This function will add sequence(s) under schema node.'
db_name = parent_node_dict['database'][(- 1)]['db_name']
schema_info = parent_node_dict['schema'][(- 1)]
self.server_id = schema_info['server_id']
self.db_id = schema_info['db_id']
db_con = database_utils.connect_database(self, utils.SERVER_GROUP, self.server_id, self.db_id)
if (not db_con['data']['connected']):
raise Exception('Could not connect to database to add sequence.')
schema_id = schema_info['schema_id']
schema_name = schema_info['schema_name']
schema_response = schema_utils.verify_schemas(self.server, db_name, schema_name)
if (not schema_response):
raise Exception('Could not find the schema to add sequence.')
db_user = self.server['username']
common_data = {'relacl': [{'grantee': db_user, 'grantor': db_user, 'privileges': [{'privilege_type': 'r', 'privilege': True, 'with_grant': True}, {'privilege_type': 'w', 'privilege': True, 'with_grant': False}, {'privilege_type': 'U', 'privilege': True, 'with_grant': False}]}], 'schema': schema_name, 'seqowner': db_user}
self.data.update(common_data)
response = self.tester.post(((((((((self.url + str(utils.SERVER_GROUP)) + '/') + str(self.server_id)) + '/') + str(self.db_id)) + '/') + str(schema_id)) + '/'), data=json.dumps(self.data), content_type='html/json')
self.assertEquals(response.status_code, 200) | This function will add sequence(s) under schema node. | local/lib/python3.6/site-packages/pgadmin4/pgadmin/browser/server_groups/servers/databases/schemas/sequences/tests/test_sequence_add.py | runTest | sahilsdei/django_ecommerce | 0 | python | def runTest(self):
db_name = parent_node_dict['database'][(- 1)]['db_name']
schema_info = parent_node_dict['schema'][(- 1)]
self.server_id = schema_info['server_id']
self.db_id = schema_info['db_id']
db_con = database_utils.connect_database(self, utils.SERVER_GROUP, self.server_id, self.db_id)
if (not db_con['data']['connected']):
raise Exception('Could not connect to database to add sequence.')
schema_id = schema_info['schema_id']
schema_name = schema_info['schema_name']
schema_response = schema_utils.verify_schemas(self.server, db_name, schema_name)
if (not schema_response):
raise Exception('Could not find the schema to add sequence.')
db_user = self.server['username']
common_data = {'relacl': [{'grantee': db_user, 'grantor': db_user, 'privileges': [{'privilege_type': 'r', 'privilege': True, 'with_grant': True}, {'privilege_type': 'w', 'privilege': True, 'with_grant': False}, {'privilege_type': 'U', 'privilege': True, 'with_grant': False}]}], 'schema': schema_name, 'seqowner': db_user}
self.data.update(common_data)
response = self.tester.post(((((((((self.url + str(utils.SERVER_GROUP)) + '/') + str(self.server_id)) + '/') + str(self.db_id)) + '/') + str(schema_id)) + '/'), data=json.dumps(self.data), content_type='html/json')
self.assertEquals(response.status_code, 200) | def runTest(self):
db_name = parent_node_dict['database'][(- 1)]['db_name']
schema_info = parent_node_dict['schema'][(- 1)]
self.server_id = schema_info['server_id']
self.db_id = schema_info['db_id']
db_con = database_utils.connect_database(self, utils.SERVER_GROUP, self.server_id, self.db_id)
if (not db_con['data']['connected']):
raise Exception('Could not connect to database to add sequence.')
schema_id = schema_info['schema_id']
schema_name = schema_info['schema_name']
schema_response = schema_utils.verify_schemas(self.server, db_name, schema_name)
if (not schema_response):
raise Exception('Could not find the schema to add sequence.')
db_user = self.server['username']
common_data = {'relacl': [{'grantee': db_user, 'grantor': db_user, 'privileges': [{'privilege_type': 'r', 'privilege': True, 'with_grant': True}, {'privilege_type': 'w', 'privilege': True, 'with_grant': False}, {'privilege_type': 'U', 'privilege': True, 'with_grant': False}]}], 'schema': schema_name, 'seqowner': db_user}
self.data.update(common_data)
response = self.tester.post(((((((((self.url + str(utils.SERVER_GROUP)) + '/') + str(self.server_id)) + '/') + str(self.db_id)) + '/') + str(schema_id)) + '/'), data=json.dumps(self.data), content_type='html/json')
self.assertEquals(response.status_code, 200)<|docstring|>This function will add sequence(s) under schema node.<|endoftext|> |
7fbd3935b9bcc6191bebe36e9144aad18fa255a8b258975a894438d67d383f73 | def __init__(self, drop_num=500):
'SRS defense method.\n\n Args:\n drop_num (int, optional): number of points to drop.\n Defaults to 500.\n '
super(SRSDefense, self).__init__()
self.drop_num = drop_num | SRS defense method.
Args:
drop_num (int, optional): number of points to drop.
Defaults to 500. | baselines/defense/drop_points/SRS.py | __init__ | code-roamer/IF-Defense | 36 | python | def __init__(self, drop_num=500):
'SRS defense method.\n\n Args:\n drop_num (int, optional): number of points to drop.\n Defaults to 500.\n '
super(SRSDefense, self).__init__()
self.drop_num = drop_num | def __init__(self, drop_num=500):
'SRS defense method.\n\n Args:\n drop_num (int, optional): number of points to drop.\n Defaults to 500.\n '
super(SRSDefense, self).__init__()
self.drop_num = drop_num<|docstring|>SRS defense method.
Args:
drop_num (int, optional): number of points to drop.
Defaults to 500.<|endoftext|> |
4838462ce0b5a6a8075a33e8209ca092192d94471d32c7d0478a203faf994088 | def random_drop(self, pc):
'Random drop self.drop_num points in each pc.\n\n Args:\n pc (torch.FloatTensor): batch input pc, [B, K, 3]\n '
(B, K) = pc.shape[:2]
idx = [np.random.choice(K, (K - self.drop_num), replace=False) for _ in range(B)]
pc = torch.stack([pc[i][torch.from_numpy(idx[i]).long().to(pc.device)] for i in range(B)])
return pc | Random drop self.drop_num points in each pc.
Args:
pc (torch.FloatTensor): batch input pc, [B, K, 3] | baselines/defense/drop_points/SRS.py | random_drop | code-roamer/IF-Defense | 36 | python | def random_drop(self, pc):
'Random drop self.drop_num points in each pc.\n\n Args:\n pc (torch.FloatTensor): batch input pc, [B, K, 3]\n '
(B, K) = pc.shape[:2]
idx = [np.random.choice(K, (K - self.drop_num), replace=False) for _ in range(B)]
pc = torch.stack([pc[i][torch.from_numpy(idx[i]).long().to(pc.device)] for i in range(B)])
return pc | def random_drop(self, pc):
'Random drop self.drop_num points in each pc.\n\n Args:\n pc (torch.FloatTensor): batch input pc, [B, K, 3]\n '
(B, K) = pc.shape[:2]
idx = [np.random.choice(K, (K - self.drop_num), replace=False) for _ in range(B)]
pc = torch.stack([pc[i][torch.from_numpy(idx[i]).long().to(pc.device)] for i in range(B)])
return pc<|docstring|>Random drop self.drop_num points in each pc.
Args:
pc (torch.FloatTensor): batch input pc, [B, K, 3]<|endoftext|> |
ce8676ab32c141f44ec837f8d657b73c67698504852ed18abb35cd1e2e569b77 | def FoodsView(request):
'\n API endpoint that retunrs filtered foods\n '
from urllib.parse import urlparse, parse_qs
with open(os.path.join(os.path.dirname(__file__), 'food_data.json'), 'r') as json_file:
data = json.load(json_file)
foods = data['report']['foods']
params = parse_qs(request.META['QUERY_STRING'])
print('params: {}'.format(params))
def filter_foods(nutrients, ranges):
return (is_in_range(nutrients[1], ranges['protein[0]'], ranges['protein[1]']) and is_in_range(nutrients[2], ranges['fat[0]'], ranges['fat[1]']) and is_in_range(nutrients[3], ranges['carb[0]'], ranges['carb[1]']) and is_in_range(nutrients[4], ranges['sugar[0]'], ranges['sugar[1]']))
def is_in_range(nutrient, min, max):
gm = (nutrient['gm'] if (nutrient['gm'] != '--') else 0)
return (int(min[0]) <= gm <= int(max[0]))
response_data = ([food for food in foods if filter_foods(food['nutrients'], params)] if (params != None) else foods)
return JsonResponse(response_data, safe=False) | API endpoint that retunrs filtered foods | project/api/views.py | FoodsView | daryllcheng/python_rest | 0 | python | def FoodsView(request):
'\n \n '
from urllib.parse import urlparse, parse_qs
with open(os.path.join(os.path.dirname(__file__), 'food_data.json'), 'r') as json_file:
data = json.load(json_file)
foods = data['report']['foods']
params = parse_qs(request.META['QUERY_STRING'])
print('params: {}'.format(params))
def filter_foods(nutrients, ranges):
return (is_in_range(nutrients[1], ranges['protein[0]'], ranges['protein[1]']) and is_in_range(nutrients[2], ranges['fat[0]'], ranges['fat[1]']) and is_in_range(nutrients[3], ranges['carb[0]'], ranges['carb[1]']) and is_in_range(nutrients[4], ranges['sugar[0]'], ranges['sugar[1]']))
def is_in_range(nutrient, min, max):
gm = (nutrient['gm'] if (nutrient['gm'] != '--') else 0)
return (int(min[0]) <= gm <= int(max[0]))
response_data = ([food for food in foods if filter_foods(food['nutrients'], params)] if (params != None) else foods)
return JsonResponse(response_data, safe=False) | def FoodsView(request):
'\n \n '
from urllib.parse import urlparse, parse_qs
with open(os.path.join(os.path.dirname(__file__), 'food_data.json'), 'r') as json_file:
data = json.load(json_file)
foods = data['report']['foods']
params = parse_qs(request.META['QUERY_STRING'])
print('params: {}'.format(params))
def filter_foods(nutrients, ranges):
return (is_in_range(nutrients[1], ranges['protein[0]'], ranges['protein[1]']) and is_in_range(nutrients[2], ranges['fat[0]'], ranges['fat[1]']) and is_in_range(nutrients[3], ranges['carb[0]'], ranges['carb[1]']) and is_in_range(nutrients[4], ranges['sugar[0]'], ranges['sugar[1]']))
def is_in_range(nutrient, min, max):
gm = (nutrient['gm'] if (nutrient['gm'] != '--') else 0)
return (int(min[0]) <= gm <= int(max[0]))
response_data = ([food for food in foods if filter_foods(food['nutrients'], params)] if (params != None) else foods)
return JsonResponse(response_data, safe=False)<|docstring|>API endpoint that retunrs filtered foods<|endoftext|> |
9d1710c36e8f2ec29be0d0e97da1e4a4dc19d95b85bc34c151d00eabab4300ce | def test_zerocross():
'Test zerocross with legendre polynomials.'
def _bonnet(d, x):
if (d == 0):
return np.ones_like(x)
elif (d == 1):
return x
else:
return ((((((2 * d) - 1) * x) * _bonnet((d - 1), x)) - ((d - 1) * _bonnet((d - 2), x))) / d)
x = np.linspace((- 1), 1, 100)
legendre = np.empty([100, 5], dtype='float32')
for n in range(5):
legendre[(:, n)] = _bonnet(n, x)
zx = np.linspace(0, 4, 5)
assert all((zx == graph.zerocross(legendre))) | Test zerocross with legendre polynomials. | nigsp/tests/test_graph.py | test_zerocross | smoia/nigsp | 2 | python | def test_zerocross():
def _bonnet(d, x):
if (d == 0):
return np.ones_like(x)
elif (d == 1):
return x
else:
return ((((((2 * d) - 1) * x) * _bonnet((d - 1), x)) - ((d - 1) * _bonnet((d - 2), x))) / d)
x = np.linspace((- 1), 1, 100)
legendre = np.empty([100, 5], dtype='float32')
for n in range(5):
legendre[(:, n)] = _bonnet(n, x)
zx = np.linspace(0, 4, 5)
assert all((zx == graph.zerocross(legendre))) | def test_zerocross():
def _bonnet(d, x):
if (d == 0):
return np.ones_like(x)
elif (d == 1):
return x
else:
return ((((((2 * d) - 1) * x) * _bonnet((d - 1), x)) - ((d - 1) * _bonnet((d - 2), x))) / d)
x = np.linspace((- 1), 1, 100)
legendre = np.empty([100, 5], dtype='float32')
for n in range(5):
legendre[(:, n)] = _bonnet(n, x)
zx = np.linspace(0, 4, 5)
assert all((zx == graph.zerocross(legendre)))<|docstring|>Test zerocross with legendre polynomials.<|endoftext|> |
0a753d112550deb189c5383bcdee14ffbf73518118621c0a2c9fe691cb11da17 | def test_nodestrength():
'Test nodestrength with random matrix.'
a = np.random.rand(3, 3)
a = (a - a.mean())
b = np.abs(a)
s = b.sum(axis=0)
f = s.mean(axis=(- 1))
n = graph.nodestrength(a)
m = graph.nodestrength(b)
o = graph.nodestrength(a, mean=True)
assert all((n == m))
assert all((n == s))
assert (f == o) | Test nodestrength with random matrix. | nigsp/tests/test_graph.py | test_nodestrength | smoia/nigsp | 2 | python | def test_nodestrength():
a = np.random.rand(3, 3)
a = (a - a.mean())
b = np.abs(a)
s = b.sum(axis=0)
f = s.mean(axis=(- 1))
n = graph.nodestrength(a)
m = graph.nodestrength(b)
o = graph.nodestrength(a, mean=True)
assert all((n == m))
assert all((n == s))
assert (f == o) | def test_nodestrength():
a = np.random.rand(3, 3)
a = (a - a.mean())
b = np.abs(a)
s = b.sum(axis=0)
f = s.mean(axis=(- 1))
n = graph.nodestrength(a)
m = graph.nodestrength(b)
o = graph.nodestrength(a, mean=True)
assert all((n == m))
assert all((n == s))
assert (f == o)<|docstring|>Test nodestrength with random matrix.<|endoftext|> |
fa751f4dbe93747e29610591676530e123a80ef3b07d090fa7fe93c23fa5379d | def find_dataset_lib(dataset_name):
'\n Give the option --dataset [datasetname], import "data/datasetname_dataset.py"\n :param dataset_name: --dataset\n :return: "data/datasetname_dataset.py"\n '
dataset_filename = (('data.' + dataset_name) + '_dataset')
datasetlib = importlib.import_module(dataset_filename)
dataset = None
target_dataset_name = (dataset_name.replace('_', '') + 'dataset')
for (name, cls) in datasetlib.__dict__.items():
if (name.lower() == target_dataset_name.lower()):
dataset = cls
if (dataset is None):
logger.info(('In %s.py, there should be a class name that matches %s in lowercase.' % (dataset_filename, target_dataset_name)))
exit(0)
return dataset | Give the option --dataset [datasetname], import "data/datasetname_dataset.py"
:param dataset_name: --dataset
:return: "data/datasetname_dataset.py" | merged_depth/nets/DiverseDepth/data/load_dataset.py | find_dataset_lib | mfkiwl/merged_depth | 33 | python | def find_dataset_lib(dataset_name):
'\n Give the option --dataset [datasetname], import "data/datasetname_dataset.py"\n :param dataset_name: --dataset\n :return: "data/datasetname_dataset.py"\n '
dataset_filename = (('data.' + dataset_name) + '_dataset')
datasetlib = importlib.import_module(dataset_filename)
dataset = None
target_dataset_name = (dataset_name.replace('_', ) + 'dataset')
for (name, cls) in datasetlib.__dict__.items():
if (name.lower() == target_dataset_name.lower()):
dataset = cls
if (dataset is None):
logger.info(('In %s.py, there should be a class name that matches %s in lowercase.' % (dataset_filename, target_dataset_name)))
exit(0)
return dataset | def find_dataset_lib(dataset_name):
'\n Give the option --dataset [datasetname], import "data/datasetname_dataset.py"\n :param dataset_name: --dataset\n :return: "data/datasetname_dataset.py"\n '
dataset_filename = (('data.' + dataset_name) + '_dataset')
datasetlib = importlib.import_module(dataset_filename)
dataset = None
target_dataset_name = (dataset_name.replace('_', ) + 'dataset')
for (name, cls) in datasetlib.__dict__.items():
if (name.lower() == target_dataset_name.lower()):
dataset = cls
if (dataset is None):
logger.info(('In %s.py, there should be a class name that matches %s in lowercase.' % (dataset_filename, target_dataset_name)))
exit(0)
return dataset<|docstring|>Give the option --dataset [datasetname], import "data/datasetname_dataset.py"
:param dataset_name: --dataset
:return: "data/datasetname_dataset.py"<|endoftext|> |
f5c172668cb740cda66181d80dece0c8ff7317b5634fe7bbc103d66ea8f1d841 | def draw_subspectrogram(spectrogram, duration_s, fft_rate, random_pick=False):
'\n Draw a random subspectrogram of given time length from the given spectrogram\n '
if (not random_pick):
np.random.seed(42)
offset = int((np.random.random() * (spectrogram.shape[1] - (duration_s * fft_rate))))
return spectrogram[(:, offset:(offset + int((duration_s * fft_rate))))] | Draw a random subspectrogram of given time length from the given spectrogram | utils.py | draw_subspectrogram | quentinverlhac/music-emotion-recognition | 0 | python | def draw_subspectrogram(spectrogram, duration_s, fft_rate, random_pick=False):
'\n \n '
if (not random_pick):
np.random.seed(42)
offset = int((np.random.random() * (spectrogram.shape[1] - (duration_s * fft_rate))))
return spectrogram[(:, offset:(offset + int((duration_s * fft_rate))))] | def draw_subspectrogram(spectrogram, duration_s, fft_rate, random_pick=False):
'\n \n '
if (not random_pick):
np.random.seed(42)
offset = int((np.random.random() * (spectrogram.shape[1] - (duration_s * fft_rate))))
return spectrogram[(:, offset:(offset + int((duration_s * fft_rate))))]<|docstring|>Draw a random subspectrogram of given time length from the given spectrogram<|endoftext|> |
e77225295a2c3762fac224cb10eb1eab96b3f0ae9208c05f93daf8b11a1248b8 | def segment_spectrogram(spectrogram, duration_s, fft_rate):
'\n Segment the spectrogram into successive subspectrograms of given length and returns\n the list of all subspectrograms\n '
spectrograms = []
sub_len = int((duration_s * fft_rate))
n_subspectros = int((spectrogram.shape[1] / sub_len))
for i in range(n_subspectros):
spectrograms.append(spectrogram[(:, (sub_len * i):(sub_len * (i + 1)))])
return spectrograms | Segment the spectrogram into successive subspectrograms of given length and returns
the list of all subspectrograms | utils.py | segment_spectrogram | quentinverlhac/music-emotion-recognition | 0 | python | def segment_spectrogram(spectrogram, duration_s, fft_rate):
'\n Segment the spectrogram into successive subspectrograms of given length and returns\n the list of all subspectrograms\n '
spectrograms = []
sub_len = int((duration_s * fft_rate))
n_subspectros = int((spectrogram.shape[1] / sub_len))
for i in range(n_subspectros):
spectrograms.append(spectrogram[(:, (sub_len * i):(sub_len * (i + 1)))])
return spectrograms | def segment_spectrogram(spectrogram, duration_s, fft_rate):
'\n Segment the spectrogram into successive subspectrograms of given length and returns\n the list of all subspectrograms\n '
spectrograms = []
sub_len = int((duration_s * fft_rate))
n_subspectros = int((spectrogram.shape[1] / sub_len))
for i in range(n_subspectros):
spectrograms.append(spectrogram[(:, (sub_len * i):(sub_len * (i + 1)))])
return spectrograms<|docstring|>Segment the spectrogram into successive subspectrograms of given length and returns
the list of all subspectrograms<|endoftext|> |
0a52d6bc6b85fc0e1ec52084da218961d7343b9f12b80e7d457fee86e4e4b0f4 | def segment_dataset(all_spectrograms, labels, duration_s, fft_rate):
'\n Segment all spectrograms in the dataset in snippets of the given duration, and update\n the labels accordingly\n '
new_spectrograms = []
new_labels = []
for i in range(len(all_spectrograms)):
segments = segment_spectrogram(all_spectrograms[i], duration_s, fft_rate)
new_spectrograms += segments
new_labels += [labels[i] for spectro in segments]
return (new_spectrograms, new_labels) | Segment all spectrograms in the dataset in snippets of the given duration, and update
the labels accordingly | utils.py | segment_dataset | quentinverlhac/music-emotion-recognition | 0 | python | def segment_dataset(all_spectrograms, labels, duration_s, fft_rate):
'\n Segment all spectrograms in the dataset in snippets of the given duration, and update\n the labels accordingly\n '
new_spectrograms = []
new_labels = []
for i in range(len(all_spectrograms)):
segments = segment_spectrogram(all_spectrograms[i], duration_s, fft_rate)
new_spectrograms += segments
new_labels += [labels[i] for spectro in segments]
return (new_spectrograms, new_labels) | def segment_dataset(all_spectrograms, labels, duration_s, fft_rate):
'\n Segment all spectrograms in the dataset in snippets of the given duration, and update\n the labels accordingly\n '
new_spectrograms = []
new_labels = []
for i in range(len(all_spectrograms)):
segments = segment_spectrogram(all_spectrograms[i], duration_s, fft_rate)
new_spectrograms += segments
new_labels += [labels[i] for spectro in segments]
return (new_spectrograms, new_labels)<|docstring|>Segment all spectrograms in the dataset in snippets of the given duration, and update
the labels accordingly<|endoftext|> |
84598ec15ad4b6438b2f15fb8e85e0643255ffa4db7506f9146154ce44078e09 | def dump_elements(elements, dump_path):
'\n Dumps all elements in the list to a binary file on the dump path\n '
with open(dump_path, 'wb') as f:
pkl.dump(elements, f) | Dumps all elements in the list to a binary file on the dump path | utils.py | dump_elements | quentinverlhac/music-emotion-recognition | 0 | python | def dump_elements(elements, dump_path):
'\n \n '
with open(dump_path, 'wb') as f:
pkl.dump(elements, f) | def dump_elements(elements, dump_path):
'\n \n '
with open(dump_path, 'wb') as f:
pkl.dump(elements, f)<|docstring|>Dumps all elements in the list to a binary file on the dump path<|endoftext|> |
97eae0c95c4d9be85e7099595a63ae8a1f26a00ca28d65b8acb124867b3c9ac7 | def load_dump(dump_path):
'\n Load the content of the file at dump path\n '
with open(dump_path, 'rb') as file:
return pkl.load(file) | Load the content of the file at dump path | utils.py | load_dump | quentinverlhac/music-emotion-recognition | 0 | python | def load_dump(dump_path):
'\n \n '
with open(dump_path, 'rb') as file:
return pkl.load(file) | def load_dump(dump_path):
'\n \n '
with open(dump_path, 'rb') as file:
return pkl.load(file)<|docstring|>Load the content of the file at dump path<|endoftext|> |
b2da0c01610cf98ece6d195dd71564df98c4d89d8dd783e7beb0aba2fbbe7503 | def load_labels(path):
'\n Load a pickle file containing list of labels and return as pandas DataFrame\n '
with open(path, 'rb') as f:
labels = pkl.load(f)
labels = pd.DataFrame(labels)
labels.columns = config.EMOTIFY_EMOTIONS_ORDERED_LIST
return labels | Load a pickle file containing list of labels and return as pandas DataFrame | utils.py | load_labels | quentinverlhac/music-emotion-recognition | 0 | python | def load_labels(path):
'\n \n '
with open(path, 'rb') as f:
labels = pkl.load(f)
labels = pd.DataFrame(labels)
labels.columns = config.EMOTIFY_EMOTIONS_ORDERED_LIST
return labels | def load_labels(path):
'\n \n '
with open(path, 'rb') as f:
labels = pkl.load(f)
labels = pd.DataFrame(labels)
labels.columns = config.EMOTIFY_EMOTIONS_ORDERED_LIST
return labels<|docstring|>Load a pickle file containing list of labels and return as pandas DataFrame<|endoftext|> |
341065812402418d2e20d3b86cea63852f4cbabf73b2d0cee90b6968b849f78a | def average_predictions(full_spectrogram, model):
'\n Average predictions of the model over all segments in the spectrogram\n '
segmented_spectro = segment_spectrogram(full_spectrogram, config.SUBSPECTROGRAM_DURATION_S, config.FFT_RATE)
all_predictions = []
for spectro in segmented_spectro:
tensor_spectro = tf.convert_to_tensor(spectro)
tensor_spectro = tf.transpose(tensor_spectro)
tensor_spectro = tf.reshape(tensor_spectro, [1, tensor_spectro.shape[0], tensor_spectro.shape[1]])
all_predictions.append(model.call(tensor_spectro))
return (tf.add_n(all_predictions) / len(segmented_spectro)) | Average predictions of the model over all segments in the spectrogram | utils.py | average_predictions | quentinverlhac/music-emotion-recognition | 0 | python | def average_predictions(full_spectrogram, model):
'\n \n '
segmented_spectro = segment_spectrogram(full_spectrogram, config.SUBSPECTROGRAM_DURATION_S, config.FFT_RATE)
all_predictions = []
for spectro in segmented_spectro:
tensor_spectro = tf.convert_to_tensor(spectro)
tensor_spectro = tf.transpose(tensor_spectro)
tensor_spectro = tf.reshape(tensor_spectro, [1, tensor_spectro.shape[0], tensor_spectro.shape[1]])
all_predictions.append(model.call(tensor_spectro))
return (tf.add_n(all_predictions) / len(segmented_spectro)) | def average_predictions(full_spectrogram, model):
'\n \n '
segmented_spectro = segment_spectrogram(full_spectrogram, config.SUBSPECTROGRAM_DURATION_S, config.FFT_RATE)
all_predictions = []
for spectro in segmented_spectro:
tensor_spectro = tf.convert_to_tensor(spectro)
tensor_spectro = tf.transpose(tensor_spectro)
tensor_spectro = tf.reshape(tensor_spectro, [1, tensor_spectro.shape[0], tensor_spectro.shape[1]])
all_predictions.append(model.call(tensor_spectro))
return (tf.add_n(all_predictions) / len(segmented_spectro))<|docstring|>Average predictions of the model over all segments in the spectrogram<|endoftext|> |
e2cfc2f79d65cf203a4ab1f72be1798e3f82f107532fd100556697da21b1c125 | def shuffle_data_and_labels(data, labels):
'\n Shuffles data and labels the same way\n :param data: list: input data\n :param labels: list: corresponding labels\n :return: tuple, shuffled data and labels\n '
temp = list(zip(data, labels))
random.shuffle(temp)
return zip(*temp) | Shuffles data and labels the same way
:param data: list: input data
:param labels: list: corresponding labels
:return: tuple, shuffled data and labels | utils.py | shuffle_data_and_labels | quentinverlhac/music-emotion-recognition | 0 | python | def shuffle_data_and_labels(data, labels):
'\n Shuffles data and labels the same way\n :param data: list: input data\n :param labels: list: corresponding labels\n :return: tuple, shuffled data and labels\n '
temp = list(zip(data, labels))
random.shuffle(temp)
return zip(*temp) | def shuffle_data_and_labels(data, labels):
'\n Shuffles data and labels the same way\n :param data: list: input data\n :param labels: list: corresponding labels\n :return: tuple, shuffled data and labels\n '
temp = list(zip(data, labels))
random.shuffle(temp)
return zip(*temp)<|docstring|>Shuffles data and labels the same way
:param data: list: input data
:param labels: list: corresponding labels
:return: tuple, shuffled data and labels<|endoftext|> |
2fb30905c793ec4f3fe7f396ba3fbf6019670a5aaa961b833196e92572800faa | def restart(self):
'\n Restart ibash process.\n '
self.child.kill()
self.start()
root.status.set_msg('Process killed and started !') | Restart ibash process. | vyapp/plugins/ibash.py | restart | AndreasDavour/vy | 0 | python | def restart(self):
'\n \n '
self.child.kill()
self.start()
root.status.set_msg('Process killed and started !') | def restart(self):
'\n \n '
self.child.kill()
self.start()
root.status.set_msg('Process killed and started !')<|docstring|>Restart ibash process.<|endoftext|> |
296ea716addbcc7e6450ee7520715598eef36276c5890e102dbc102142bdc4ce | @abstractmethod
def create(self, query: SearchParams) -> Tuple[(str, Dict)]:
'abstract' | abstract | src/moz_books/interface/i_request_params_factory.py | create | yukkun007/mmbooks | 0 | python | @method
def create(self, query: SearchParams) -> Tuple[(str, Dict)]:
| @method
def create(self, query: SearchParams) -> Tuple[(str, Dict)]:
<|docstring|>abstract<|endoftext|> |
9a5ce9e6e9d81fb39781558bf47e1c5a91d1735fada1a169960cd3b00885a618 | def parse_timestamp(timestamp):
'Convert a timestamp string to a datetime object.'
(sec, ms) = timestamp.split(',')
microseconds = (1000 * int(ms))
fields = (time.strptime(sec, '%Y-%m-%d %H:%M:%S')[0:6] + (microseconds,))
return datetime.datetime(*fields) | Convert a timestamp string to a datetime object. | util/task_times.py | parse_timestamp | WillChilds-Klein/mistress-mapreduce | 2 | python | def parse_timestamp(timestamp):
(sec, ms) = timestamp.split(',')
microseconds = (1000 * int(ms))
fields = (time.strptime(sec, '%Y-%m-%d %H:%M:%S')[0:6] + (microseconds,))
return datetime.datetime(*fields) | def parse_timestamp(timestamp):
(sec, ms) = timestamp.split(',')
microseconds = (1000 * int(ms))
fields = (time.strptime(sec, '%Y-%m-%d %H:%M:%S')[0:6] + (microseconds,))
return datetime.datetime(*fields)<|docstring|>Convert a timestamp string to a datetime object.<|endoftext|> |
2f940caab0020a16f72ba350915cd2c52445ee2a8260bf00708ebe27f8ab9620 | def _make_env(scenario_name, benchmark=False):
'\n Creates a MultiAgentEnv object as env. This can be used similar to a gym\n environment by calling env.reset() and env.step().\n Use env.render() to view the environment on the screen.\n Input:\n scenario_name : name of the scenario from ./scenarios/ to be Returns\n (without the .py extension)\n benchmark : whether you want to produce benchmarking data\n (usually only done during evaluation)\n Some useful env properties (see environment.py):\n .observation_space : Returns the observation space for each agent\n .action_space : Returns the action space for each agent\n .n : Returns the number of Agents\n '
from multiagent.environment import MultiAgentEnv
from multiagent import scenarios
scenario = scenarios.load((scenario_name + '.py')).Scenario()
world = scenario.make_world()
if benchmark:
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation, scenario.benchmark_data)
else:
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation)
return env | Creates a MultiAgentEnv object as env. This can be used similar to a gym
environment by calling env.reset() and env.step().
Use env.render() to view the environment on the screen.
Input:
scenario_name : name of the scenario from ./scenarios/ to be Returns
(without the .py extension)
benchmark : whether you want to produce benchmarking data
(usually only done during evaluation)
Some useful env properties (see environment.py):
.observation_space : Returns the observation space for each agent
.action_space : Returns the action space for each agent
.n : Returns the number of Agents | ma-poca/mapoca/mapoca/particles_env.py | _make_env | Unity-Technologies/paper-ml-agents | 1 | python | def _make_env(scenario_name, benchmark=False):
'\n Creates a MultiAgentEnv object as env. This can be used similar to a gym\n environment by calling env.reset() and env.step().\n Use env.render() to view the environment on the screen.\n Input:\n scenario_name : name of the scenario from ./scenarios/ to be Returns\n (without the .py extension)\n benchmark : whether you want to produce benchmarking data\n (usually only done during evaluation)\n Some useful env properties (see environment.py):\n .observation_space : Returns the observation space for each agent\n .action_space : Returns the action space for each agent\n .n : Returns the number of Agents\n '
from multiagent.environment import MultiAgentEnv
from multiagent import scenarios
scenario = scenarios.load((scenario_name + '.py')).Scenario()
world = scenario.make_world()
if benchmark:
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation, scenario.benchmark_data)
else:
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation)
return env | def _make_env(scenario_name, benchmark=False):
'\n Creates a MultiAgentEnv object as env. This can be used similar to a gym\n environment by calling env.reset() and env.step().\n Use env.render() to view the environment on the screen.\n Input:\n scenario_name : name of the scenario from ./scenarios/ to be Returns\n (without the .py extension)\n benchmark : whether you want to produce benchmarking data\n (usually only done during evaluation)\n Some useful env properties (see environment.py):\n .observation_space : Returns the observation space for each agent\n .action_space : Returns the action space for each agent\n .n : Returns the number of Agents\n '
from multiagent.environment import MultiAgentEnv
from multiagent import scenarios
scenario = scenarios.load((scenario_name + '.py')).Scenario()
world = scenario.make_world()
if benchmark:
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation, scenario.benchmark_data)
else:
env = MultiAgentEnv(world, scenario.reset_world, scenario.reward, scenario.observation)
return env<|docstring|>Creates a MultiAgentEnv object as env. This can be used similar to a gym
environment by calling env.reset() and env.step().
Use env.render() to view the environment on the screen.
Input:
scenario_name : name of the scenario from ./scenarios/ to be Returns
(without the .py extension)
benchmark : whether you want to produce benchmarking data
(usually only done during evaluation)
Some useful env properties (see environment.py):
.observation_space : Returns the observation space for each agent
.action_space : Returns the action space for each agent
.n : Returns the number of Agents<|endoftext|> |
48612fc1628369dea79e802bedfbaeef79c6eaec76443a0236b600e3843a48d6 | def _check_array_dimensions(self, array=None):
"\n Check that a grid's array dimensions agree with this grid's metadata\n\n Parameters\n ----------\n array : np.ndarray or list of np.ndarray, optional\n The array for which to test the dimensions. If this is not\n specified, this method performs a self-consistency check of array\n dimensions and meta-data.\n "
n_pop_ref = None
if isinstance(array, SphericalPolarGridView):
array = array.quantities[array.viewed_quantity]
for quantity in self.quantities:
if (array is None):
(n_pop, shape) = single_grid_dims(self.quantities[quantity])
else:
(n_pop, shape) = single_grid_dims(array)
if (shape != self.shape):
raise ValueError(('Quantity arrays do not have the right dimensions: %s instead of %s' % (shape, self.shape)))
if (n_pop is not None):
if (n_pop_ref is None):
n_pop_ref = n_pop
elif (n_pop != n_pop_ref):
raise ValueError('Not all dust lists in the grid have the same size') | Check that a grid's array dimensions agree with this grid's metadata
Parameters
----------
array : np.ndarray or list of np.ndarray, optional
The array for which to test the dimensions. If this is not
specified, this method performs a self-consistency check of array
dimensions and meta-data. | hyperion/grid/spherical_polar_grid.py | _check_array_dimensions | keflavich/hyperion | 37 | python | def _check_array_dimensions(self, array=None):
"\n Check that a grid's array dimensions agree with this grid's metadata\n\n Parameters\n ----------\n array : np.ndarray or list of np.ndarray, optional\n The array for which to test the dimensions. If this is not\n specified, this method performs a self-consistency check of array\n dimensions and meta-data.\n "
n_pop_ref = None
if isinstance(array, SphericalPolarGridView):
array = array.quantities[array.viewed_quantity]
for quantity in self.quantities:
if (array is None):
(n_pop, shape) = single_grid_dims(self.quantities[quantity])
else:
(n_pop, shape) = single_grid_dims(array)
if (shape != self.shape):
raise ValueError(('Quantity arrays do not have the right dimensions: %s instead of %s' % (shape, self.shape)))
if (n_pop is not None):
if (n_pop_ref is None):
n_pop_ref = n_pop
elif (n_pop != n_pop_ref):
raise ValueError('Not all dust lists in the grid have the same size') | def _check_array_dimensions(self, array=None):
"\n Check that a grid's array dimensions agree with this grid's metadata\n\n Parameters\n ----------\n array : np.ndarray or list of np.ndarray, optional\n The array for which to test the dimensions. If this is not\n specified, this method performs a self-consistency check of array\n dimensions and meta-data.\n "
n_pop_ref = None
if isinstance(array, SphericalPolarGridView):
array = array.quantities[array.viewed_quantity]
for quantity in self.quantities:
if (array is None):
(n_pop, shape) = single_grid_dims(self.quantities[quantity])
else:
(n_pop, shape) = single_grid_dims(array)
if (shape != self.shape):
raise ValueError(('Quantity arrays do not have the right dimensions: %s instead of %s' % (shape, self.shape)))
if (n_pop is not None):
if (n_pop_ref is None):
n_pop_ref = n_pop
elif (n_pop != n_pop_ref):
raise ValueError('Not all dust lists in the grid have the same size')<|docstring|>Check that a grid's array dimensions agree with this grid's metadata
Parameters
----------
array : np.ndarray or list of np.ndarray, optional
The array for which to test the dimensions. If this is not
specified, this method performs a self-consistency check of array
dimensions and meta-data.<|endoftext|> |
6188421b344d8910855cedefc318d27ade5a0905d342c2b9fc65a887e15b1dcf | def read(self, group, quantities='all'):
"\n Read the geometry and physical quantities from a spherical polar grid\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to read the grid from. This group should contain\n groups named 'Geometry' and 'Quantities'.\n quantities : 'all' or list\n Which physical quantities to read in. Use 'all' to read in all\n quantities or a list of strings to read only specific quantities.\n "
self.read_geometry(group['Geometry'])
self.read_quantities(group['Quantities'], quantities=quantities)
self._check_array_dimensions() | Read the geometry and physical quantities from a spherical polar grid
Parameters
----------
group : h5py.Group
The HDF5 group to read the grid from. This group should contain
groups named 'Geometry' and 'Quantities'.
quantities : 'all' or list
Which physical quantities to read in. Use 'all' to read in all
quantities or a list of strings to read only specific quantities. | hyperion/grid/spherical_polar_grid.py | read | keflavich/hyperion | 37 | python | def read(self, group, quantities='all'):
"\n Read the geometry and physical quantities from a spherical polar grid\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to read the grid from. This group should contain\n groups named 'Geometry' and 'Quantities'.\n quantities : 'all' or list\n Which physical quantities to read in. Use 'all' to read in all\n quantities or a list of strings to read only specific quantities.\n "
self.read_geometry(group['Geometry'])
self.read_quantities(group['Quantities'], quantities=quantities)
self._check_array_dimensions() | def read(self, group, quantities='all'):
"\n Read the geometry and physical quantities from a spherical polar grid\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to read the grid from. This group should contain\n groups named 'Geometry' and 'Quantities'.\n quantities : 'all' or list\n Which physical quantities to read in. Use 'all' to read in all\n quantities or a list of strings to read only specific quantities.\n "
self.read_geometry(group['Geometry'])
self.read_quantities(group['Quantities'], quantities=quantities)
self._check_array_dimensions()<|docstring|>Read the geometry and physical quantities from a spherical polar grid
Parameters
----------
group : h5py.Group
The HDF5 group to read the grid from. This group should contain
groups named 'Geometry' and 'Quantities'.
quantities : 'all' or list
Which physical quantities to read in. Use 'all' to read in all
quantities or a list of strings to read only specific quantities.<|endoftext|> |
806911f5082f9fe3254be7810788962dfbbdbd6f158548af6fdcfe886b530760 | def read_geometry(self, group):
'\n Read in geometry information from a spherical polar grid\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to read the grid geometry from.\n '
if (group.attrs['grid_type'].decode('utf-8') != 'sph_pol'):
raise ValueError('Grid is not spherical polar')
self.set_walls(group['walls_1']['r'], group['walls_2']['t'], group['walls_3']['p'])
if (group.attrs['geometry'].decode('utf-8') != self.get_geometry_id()):
raise Exception('Calculated geometry hash does not match hash in file') | Read in geometry information from a spherical polar grid
Parameters
----------
group : h5py.Group
The HDF5 group to read the grid geometry from. | hyperion/grid/spherical_polar_grid.py | read_geometry | keflavich/hyperion | 37 | python | def read_geometry(self, group):
'\n Read in geometry information from a spherical polar grid\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to read the grid geometry from.\n '
if (group.attrs['grid_type'].decode('utf-8') != 'sph_pol'):
raise ValueError('Grid is not spherical polar')
self.set_walls(group['walls_1']['r'], group['walls_2']['t'], group['walls_3']['p'])
if (group.attrs['geometry'].decode('utf-8') != self.get_geometry_id()):
raise Exception('Calculated geometry hash does not match hash in file') | def read_geometry(self, group):
'\n Read in geometry information from a spherical polar grid\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to read the grid geometry from.\n '
if (group.attrs['grid_type'].decode('utf-8') != 'sph_pol'):
raise ValueError('Grid is not spherical polar')
self.set_walls(group['walls_1']['r'], group['walls_2']['t'], group['walls_3']['p'])
if (group.attrs['geometry'].decode('utf-8') != self.get_geometry_id()):
raise Exception('Calculated geometry hash does not match hash in file')<|docstring|>Read in geometry information from a spherical polar grid
Parameters
----------
group : h5py.Group
The HDF5 group to read the grid geometry from.<|endoftext|> |
6c809ccf37b6878b8319b98ec0d7545c36efb7bedbe7e979040d21c8b8eec662 | def read_quantities(self, group, quantities='all'):
"\n Read in physical quantities from a spherical polar grid\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to read the grid quantities from\n quantities : 'all' or list\n Which physical quantities to read in. Use 'all' to read in all\n quantities or a list of strings to read only specific quantities.\n "
if (quantities is not None):
for quantity in group:
if ((quantities == 'all') or (quantity in quantities)):
array = np.array(group[quantity])
if (array.ndim == 4):
self.quantities[quantity] = [array[i] for i in range(array.shape[0])]
else:
self.quantities[quantity] = array
self._check_array_dimensions() | Read in physical quantities from a spherical polar grid
Parameters
----------
group : h5py.Group
The HDF5 group to read the grid quantities from
quantities : 'all' or list
Which physical quantities to read in. Use 'all' to read in all
quantities or a list of strings to read only specific quantities. | hyperion/grid/spherical_polar_grid.py | read_quantities | keflavich/hyperion | 37 | python | def read_quantities(self, group, quantities='all'):
"\n Read in physical quantities from a spherical polar grid\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to read the grid quantities from\n quantities : 'all' or list\n Which physical quantities to read in. Use 'all' to read in all\n quantities or a list of strings to read only specific quantities.\n "
if (quantities is not None):
for quantity in group:
if ((quantities == 'all') or (quantity in quantities)):
array = np.array(group[quantity])
if (array.ndim == 4):
self.quantities[quantity] = [array[i] for i in range(array.shape[0])]
else:
self.quantities[quantity] = array
self._check_array_dimensions() | def read_quantities(self, group, quantities='all'):
"\n Read in physical quantities from a spherical polar grid\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to read the grid quantities from\n quantities : 'all' or list\n Which physical quantities to read in. Use 'all' to read in all\n quantities or a list of strings to read only specific quantities.\n "
if (quantities is not None):
for quantity in group:
if ((quantities == 'all') or (quantity in quantities)):
array = np.array(group[quantity])
if (array.ndim == 4):
self.quantities[quantity] = [array[i] for i in range(array.shape[0])]
else:
self.quantities[quantity] = array
self._check_array_dimensions()<|docstring|>Read in physical quantities from a spherical polar grid
Parameters
----------
group : h5py.Group
The HDF5 group to read the grid quantities from
quantities : 'all' or list
Which physical quantities to read in. Use 'all' to read in all
quantities or a list of strings to read only specific quantities.<|endoftext|> |
28749e0ee7bb916c1d612acba934d516b367faecbddfee93258bd573dabccd1c | def write(self, group, quantities='all', copy=True, absolute_paths=False, compression=True, wall_dtype=float, physics_dtype=float):
"\n Write out the spherical polar grid\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to write the grid to\n quantities : 'all' or list\n Which physical quantities to write out. Use 'all' to write out all\n quantities or a list of strings to write only specific quantities.\n copy : bool\n Whether to copy external links, or leave them as links.\n absolute_paths : bool\n If copy is False, then this indicates whether to use absolute or\n relative paths for links.\n compression : bool\n Whether to compress the arrays in the HDF5 file\n wall_dtype : type\n The datatype to use to write the wall positions\n physics_dtype : type\n The datatype to use to write the physical quantities\n "
if ('Geometry' not in group):
g_geometry = group.create_group('Geometry')
else:
g_geometry = group['Geometry']
if ('Quantities' not in group):
g_quantities = group.create_group('Quantities')
else:
g_quantities = group['Quantities']
g_geometry.attrs['grid_type'] = np.string_('sph_pol'.encode('utf-8'))
g_geometry.attrs['geometry'] = np.string_(self.get_geometry_id().encode('utf-8'))
dset = g_geometry.create_dataset('walls_1', data=np.array(list(zip(self.r_wall)), dtype=[('r', wall_dtype)]), compression=compression)
dset.attrs['Unit'] = np.string_('cm'.encode('utf-8'))
dset = g_geometry.create_dataset('walls_2', data=np.array(list(zip(self.t_wall)), dtype=[('t', wall_dtype)]), compression=compression)
dset.attrs['Unit'] = np.string_('rad'.encode('utf-8'))
dset = g_geometry.create_dataset('walls_3', data=np.array(list(zip(self.p_wall)), dtype=[('p', wall_dtype)]), compression=compression)
dset.attrs['Unit'] = np.string_('rad'.encode('utf-8'))
self._check_array_dimensions()
for quantity in self.quantities:
if ((quantities == 'all') or (quantity in quantities)):
if isinstance(self.quantities[quantity], h5py.ExternalLink):
link_or_copy(g_quantities, quantity, self.quantities[quantity], copy, absolute_paths=absolute_paths)
else:
dset = g_quantities.create_dataset(quantity, data=self.quantities[quantity], compression=compression, dtype=physics_dtype)
dset.attrs['geometry'] = np.string_(self.get_geometry_id().encode('utf-8')) | Write out the spherical polar grid
Parameters
----------
group : h5py.Group
The HDF5 group to write the grid to
quantities : 'all' or list
Which physical quantities to write out. Use 'all' to write out all
quantities or a list of strings to write only specific quantities.
copy : bool
Whether to copy external links, or leave them as links.
absolute_paths : bool
If copy is False, then this indicates whether to use absolute or
relative paths for links.
compression : bool
Whether to compress the arrays in the HDF5 file
wall_dtype : type
The datatype to use to write the wall positions
physics_dtype : type
The datatype to use to write the physical quantities | hyperion/grid/spherical_polar_grid.py | write | keflavich/hyperion | 37 | python | def write(self, group, quantities='all', copy=True, absolute_paths=False, compression=True, wall_dtype=float, physics_dtype=float):
"\n Write out the spherical polar grid\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to write the grid to\n quantities : 'all' or list\n Which physical quantities to write out. Use 'all' to write out all\n quantities or a list of strings to write only specific quantities.\n copy : bool\n Whether to copy external links, or leave them as links.\n absolute_paths : bool\n If copy is False, then this indicates whether to use absolute or\n relative paths for links.\n compression : bool\n Whether to compress the arrays in the HDF5 file\n wall_dtype : type\n The datatype to use to write the wall positions\n physics_dtype : type\n The datatype to use to write the physical quantities\n "
if ('Geometry' not in group):
g_geometry = group.create_group('Geometry')
else:
g_geometry = group['Geometry']
if ('Quantities' not in group):
g_quantities = group.create_group('Quantities')
else:
g_quantities = group['Quantities']
g_geometry.attrs['grid_type'] = np.string_('sph_pol'.encode('utf-8'))
g_geometry.attrs['geometry'] = np.string_(self.get_geometry_id().encode('utf-8'))
dset = g_geometry.create_dataset('walls_1', data=np.array(list(zip(self.r_wall)), dtype=[('r', wall_dtype)]), compression=compression)
dset.attrs['Unit'] = np.string_('cm'.encode('utf-8'))
dset = g_geometry.create_dataset('walls_2', data=np.array(list(zip(self.t_wall)), dtype=[('t', wall_dtype)]), compression=compression)
dset.attrs['Unit'] = np.string_('rad'.encode('utf-8'))
dset = g_geometry.create_dataset('walls_3', data=np.array(list(zip(self.p_wall)), dtype=[('p', wall_dtype)]), compression=compression)
dset.attrs['Unit'] = np.string_('rad'.encode('utf-8'))
self._check_array_dimensions()
for quantity in self.quantities:
if ((quantities == 'all') or (quantity in quantities)):
if isinstance(self.quantities[quantity], h5py.ExternalLink):
link_or_copy(g_quantities, quantity, self.quantities[quantity], copy, absolute_paths=absolute_paths)
else:
dset = g_quantities.create_dataset(quantity, data=self.quantities[quantity], compression=compression, dtype=physics_dtype)
dset.attrs['geometry'] = np.string_(self.get_geometry_id().encode('utf-8')) | def write(self, group, quantities='all', copy=True, absolute_paths=False, compression=True, wall_dtype=float, physics_dtype=float):
"\n Write out the spherical polar grid\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to write the grid to\n quantities : 'all' or list\n Which physical quantities to write out. Use 'all' to write out all\n quantities or a list of strings to write only specific quantities.\n copy : bool\n Whether to copy external links, or leave them as links.\n absolute_paths : bool\n If copy is False, then this indicates whether to use absolute or\n relative paths for links.\n compression : bool\n Whether to compress the arrays in the HDF5 file\n wall_dtype : type\n The datatype to use to write the wall positions\n physics_dtype : type\n The datatype to use to write the physical quantities\n "
if ('Geometry' not in group):
g_geometry = group.create_group('Geometry')
else:
g_geometry = group['Geometry']
if ('Quantities' not in group):
g_quantities = group.create_group('Quantities')
else:
g_quantities = group['Quantities']
g_geometry.attrs['grid_type'] = np.string_('sph_pol'.encode('utf-8'))
g_geometry.attrs['geometry'] = np.string_(self.get_geometry_id().encode('utf-8'))
dset = g_geometry.create_dataset('walls_1', data=np.array(list(zip(self.r_wall)), dtype=[('r', wall_dtype)]), compression=compression)
dset.attrs['Unit'] = np.string_('cm'.encode('utf-8'))
dset = g_geometry.create_dataset('walls_2', data=np.array(list(zip(self.t_wall)), dtype=[('t', wall_dtype)]), compression=compression)
dset.attrs['Unit'] = np.string_('rad'.encode('utf-8'))
dset = g_geometry.create_dataset('walls_3', data=np.array(list(zip(self.p_wall)), dtype=[('p', wall_dtype)]), compression=compression)
dset.attrs['Unit'] = np.string_('rad'.encode('utf-8'))
self._check_array_dimensions()
for quantity in self.quantities:
if ((quantities == 'all') or (quantity in quantities)):
if isinstance(self.quantities[quantity], h5py.ExternalLink):
link_or_copy(g_quantities, quantity, self.quantities[quantity], copy, absolute_paths=absolute_paths)
else:
dset = g_quantities.create_dataset(quantity, data=self.quantities[quantity], compression=compression, dtype=physics_dtype)
dset.attrs['geometry'] = np.string_(self.get_geometry_id().encode('utf-8'))<|docstring|>Write out the spherical polar grid
Parameters
----------
group : h5py.Group
The HDF5 group to write the grid to
quantities : 'all' or list
Which physical quantities to write out. Use 'all' to write out all
quantities or a list of strings to write only specific quantities.
copy : bool
Whether to copy external links, or leave them as links.
absolute_paths : bool
If copy is False, then this indicates whether to use absolute or
relative paths for links.
compression : bool
Whether to compress the arrays in the HDF5 file
wall_dtype : type
The datatype to use to write the wall positions
physics_dtype : type
The datatype to use to write the physical quantities<|endoftext|> |
1c37bec25387075724bccc86430d7dcccf3f52680060a098bd0c6ca652938b2a | def write_single_array(self, group, name, array, copy=True, absolute_paths=False, compression=True, physics_dtype=float):
'\n Write out a single quantity, checking for consistency with geometry\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to write the grid to\n name : str\n The name of the array in the group\n array : np.ndarray\n The array to write out\n copy : bool\n Whether to copy external links, or leave them as links.\n absolute_paths : bool\n If copy is False, then this indicates whether to use absolute or\n relative paths for links.\n compression : bool\n Whether to compress the arrays in the HDF5 file\n wall_dtype : type\n The datatype to use to write the wall positions\n physics_dtype : type\n The datatype to use to write the physical quantities\n '
self._check_array_dimensions(array)
if isinstance(array, h5py.ExternalLink):
link_or_copy(group, name, array, copy, absolute_paths=absolute_paths)
else:
dset = group.create_dataset(name, data=array, compression=compression, dtype=physics_dtype)
dset.attrs['geometry'] = np.string_(self.get_geometry_id().encode('utf-8')) | Write out a single quantity, checking for consistency with geometry
Parameters
----------
group : h5py.Group
The HDF5 group to write the grid to
name : str
The name of the array in the group
array : np.ndarray
The array to write out
copy : bool
Whether to copy external links, or leave them as links.
absolute_paths : bool
If copy is False, then this indicates whether to use absolute or
relative paths for links.
compression : bool
Whether to compress the arrays in the HDF5 file
wall_dtype : type
The datatype to use to write the wall positions
physics_dtype : type
The datatype to use to write the physical quantities | hyperion/grid/spherical_polar_grid.py | write_single_array | keflavich/hyperion | 37 | python | def write_single_array(self, group, name, array, copy=True, absolute_paths=False, compression=True, physics_dtype=float):
'\n Write out a single quantity, checking for consistency with geometry\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to write the grid to\n name : str\n The name of the array in the group\n array : np.ndarray\n The array to write out\n copy : bool\n Whether to copy external links, or leave them as links.\n absolute_paths : bool\n If copy is False, then this indicates whether to use absolute or\n relative paths for links.\n compression : bool\n Whether to compress the arrays in the HDF5 file\n wall_dtype : type\n The datatype to use to write the wall positions\n physics_dtype : type\n The datatype to use to write the physical quantities\n '
self._check_array_dimensions(array)
if isinstance(array, h5py.ExternalLink):
link_or_copy(group, name, array, copy, absolute_paths=absolute_paths)
else:
dset = group.create_dataset(name, data=array, compression=compression, dtype=physics_dtype)
dset.attrs['geometry'] = np.string_(self.get_geometry_id().encode('utf-8')) | def write_single_array(self, group, name, array, copy=True, absolute_paths=False, compression=True, physics_dtype=float):
'\n Write out a single quantity, checking for consistency with geometry\n\n Parameters\n ----------\n group : h5py.Group\n The HDF5 group to write the grid to\n name : str\n The name of the array in the group\n array : np.ndarray\n The array to write out\n copy : bool\n Whether to copy external links, or leave them as links.\n absolute_paths : bool\n If copy is False, then this indicates whether to use absolute or\n relative paths for links.\n compression : bool\n Whether to compress the arrays in the HDF5 file\n wall_dtype : type\n The datatype to use to write the wall positions\n physics_dtype : type\n The datatype to use to write the physical quantities\n '
self._check_array_dimensions(array)
if isinstance(array, h5py.ExternalLink):
link_or_copy(group, name, array, copy, absolute_paths=absolute_paths)
else:
dset = group.create_dataset(name, data=array, compression=compression, dtype=physics_dtype)
dset.attrs['geometry'] = np.string_(self.get_geometry_id().encode('utf-8'))<|docstring|>Write out a single quantity, checking for consistency with geometry
Parameters
----------
group : h5py.Group
The HDF5 group to write the grid to
name : str
The name of the array in the group
array : np.ndarray
The array to write out
copy : bool
Whether to copy external links, or leave them as links.
absolute_paths : bool
If copy is False, then this indicates whether to use absolute or
relative paths for links.
compression : bool
Whether to compress the arrays in the HDF5 file
wall_dtype : type
The datatype to use to write the wall positions
physics_dtype : type
The datatype to use to write the physical quantities<|endoftext|> |
1ca52b3d6aa132f85010c83762f0becfa68f005bab4964d2a5d231a5f12a4053 | def append(self, grid):
'\n Used to append quantities from another grid\n\n Parameters\n ----------\n grid : 3D Numpy array or SphericalPolarGridView instance\n The grid to copy the quantity from\n '
if isinstance(grid, SphericalPolarGridView):
if (self.quantities[self.viewed_quantity] is grid.quantities[grid.viewed_quantity]):
raise Exception('Calling append recursively')
if (type(grid.quantities[grid.viewed_quantity]) is list):
raise Exception('Can only append a single grid')
self._check_array_dimensions(grid.quantities[grid.viewed_quantity])
self.quantities[self.viewed_quantity].append(deepcopy(grid.quantities[grid.viewed_quantity]))
elif isinstance(grid, np.ndarray):
self._check_array_dimensions(grid)
self.quantities[self.viewed_quantity].append(deepcopy(grid))
else:
raise ValueError('grid should be a Numpy array or a SphericalPolarGridView instance') | Used to append quantities from another grid
Parameters
----------
grid : 3D Numpy array or SphericalPolarGridView instance
The grid to copy the quantity from | hyperion/grid/spherical_polar_grid.py | append | keflavich/hyperion | 37 | python | def append(self, grid):
'\n Used to append quantities from another grid\n\n Parameters\n ----------\n grid : 3D Numpy array or SphericalPolarGridView instance\n The grid to copy the quantity from\n '
if isinstance(grid, SphericalPolarGridView):
if (self.quantities[self.viewed_quantity] is grid.quantities[grid.viewed_quantity]):
raise Exception('Calling append recursively')
if (type(grid.quantities[grid.viewed_quantity]) is list):
raise Exception('Can only append a single grid')
self._check_array_dimensions(grid.quantities[grid.viewed_quantity])
self.quantities[self.viewed_quantity].append(deepcopy(grid.quantities[grid.viewed_quantity]))
elif isinstance(grid, np.ndarray):
self._check_array_dimensions(grid)
self.quantities[self.viewed_quantity].append(deepcopy(grid))
else:
raise ValueError('grid should be a Numpy array or a SphericalPolarGridView instance') | def append(self, grid):
'\n Used to append quantities from another grid\n\n Parameters\n ----------\n grid : 3D Numpy array or SphericalPolarGridView instance\n The grid to copy the quantity from\n '
if isinstance(grid, SphericalPolarGridView):
if (self.quantities[self.viewed_quantity] is grid.quantities[grid.viewed_quantity]):
raise Exception('Calling append recursively')
if (type(grid.quantities[grid.viewed_quantity]) is list):
raise Exception('Can only append a single grid')
self._check_array_dimensions(grid.quantities[grid.viewed_quantity])
self.quantities[self.viewed_quantity].append(deepcopy(grid.quantities[grid.viewed_quantity]))
elif isinstance(grid, np.ndarray):
self._check_array_dimensions(grid)
self.quantities[self.viewed_quantity].append(deepcopy(grid))
else:
raise ValueError('grid should be a Numpy array or a SphericalPolarGridView instance')<|docstring|>Used to append quantities from another grid
Parameters
----------
grid : 3D Numpy array or SphericalPolarGridView instance
The grid to copy the quantity from<|endoftext|> |
5ae061e71b6988472a47f4dc52222401fdcbbb53fc43b491f10728b8cc60ec00 | def add(self, grid):
'\n Used to add quantities from another grid\n\n Parameters\n ----------\n grid : 3D Numpy array or SphericalPolarGridView instance\n The grid to copy the quantity from\n '
if (type(self.quantities[self.viewed_quantity]) is list):
raise Exception('need to first specify the item to add to')
if isinstance(grid, SphericalPolarGridView):
if (type(grid.quantities[grid.viewed_quantity]) is list):
raise Exception('need to first specify the item to add')
self._check_array_dimensions(grid.quantities[grid.viewed_quantity])
self.quantities[self.viewed_quantity] += grid.quantities[grid.viewed_quantity]
elif isinstance(grid, np.ndarray):
self._check_array_dimensions(grid)
self.quantities[self.viewed_quantity] += grid
else:
raise ValueError('grid should be a Numpy array or a SphericalPolarGridView instance') | Used to add quantities from another grid
Parameters
----------
grid : 3D Numpy array or SphericalPolarGridView instance
The grid to copy the quantity from | hyperion/grid/spherical_polar_grid.py | add | keflavich/hyperion | 37 | python | def add(self, grid):
'\n Used to add quantities from another grid\n\n Parameters\n ----------\n grid : 3D Numpy array or SphericalPolarGridView instance\n The grid to copy the quantity from\n '
if (type(self.quantities[self.viewed_quantity]) is list):
raise Exception('need to first specify the item to add to')
if isinstance(grid, SphericalPolarGridView):
if (type(grid.quantities[grid.viewed_quantity]) is list):
raise Exception('need to first specify the item to add')
self._check_array_dimensions(grid.quantities[grid.viewed_quantity])
self.quantities[self.viewed_quantity] += grid.quantities[grid.viewed_quantity]
elif isinstance(grid, np.ndarray):
self._check_array_dimensions(grid)
self.quantities[self.viewed_quantity] += grid
else:
raise ValueError('grid should be a Numpy array or a SphericalPolarGridView instance') | def add(self, grid):
'\n Used to add quantities from another grid\n\n Parameters\n ----------\n grid : 3D Numpy array or SphericalPolarGridView instance\n The grid to copy the quantity from\n '
if (type(self.quantities[self.viewed_quantity]) is list):
raise Exception('need to first specify the item to add to')
if isinstance(grid, SphericalPolarGridView):
if (type(grid.quantities[grid.viewed_quantity]) is list):
raise Exception('need to first specify the item to add')
self._check_array_dimensions(grid.quantities[grid.viewed_quantity])
self.quantities[self.viewed_quantity] += grid.quantities[grid.viewed_quantity]
elif isinstance(grid, np.ndarray):
self._check_array_dimensions(grid)
self.quantities[self.viewed_quantity] += grid
else:
raise ValueError('grid should be a Numpy array or a SphericalPolarGridView instance')<|docstring|>Used to add quantities from another grid
Parameters
----------
grid : 3D Numpy array or SphericalPolarGridView instance
The grid to copy the quantity from<|endoftext|> |
899702169f05214365679538c3664fa126c5956f8720729a501a3a4e10dd0926 | def set(self, paramters):
' @paramters: ordered dict '
if (paramters is None):
return
header = list(paramters)
self.setColNames(header)
self[0] = [(x if ((x is None) or isinstance(x, str) or isinstance(x, int) or isinstance(x, float)) else str(x)) for x in paramters.values()]
for (i, name) in enumerate(header):
if ('权重' in name):
self.setItemBackground(0, i, Qt.yellow)
self.setItemForeground(0, i, Qt.black) | @paramters: ordered dict | Stock/Select/Ui/Basic/Param/DyStockSelectStrategyParamWidget.py | set | emaiqi/DevilYuan | 135 | python | def set(self, paramters):
' '
if (paramters is None):
return
header = list(paramters)
self.setColNames(header)
self[0] = [(x if ((x is None) or isinstance(x, str) or isinstance(x, int) or isinstance(x, float)) else str(x)) for x in paramters.values()]
for (i, name) in enumerate(header):
if ('权重' in name):
self.setItemBackground(0, i, Qt.yellow)
self.setItemForeground(0, i, Qt.black) | def set(self, paramters):
' '
if (paramters is None):
return
header = list(paramters)
self.setColNames(header)
self[0] = [(x if ((x is None) or isinstance(x, str) or isinstance(x, int) or isinstance(x, float)) else str(x)) for x in paramters.values()]
for (i, name) in enumerate(header):
if ('权重' in name):
self.setItemBackground(0, i, Qt.yellow)
self.setItemForeground(0, i, Qt.black)<|docstring|>@paramters: ordered dict<|endoftext|> |
e6be1093054da03ddae68736ca81a04693c853af481d036990ea77771a8a976f | def align_tensor(new_inputs, x, expand=False):
'\n Permute and add dims to a tensor to match desired ``new_inputs``.\n\n :param OrderedDict new_inputs: A target set of inputs.\n :param funsor.terms.Funsor x: A :class:`Tensor` or\n :class:`~funsor.terms.Number` .\n :param bool expand: If False (default), set result size to 1 for any input\n of ``x`` not in ``new_inputs``; if True expand to ``new_inputs`` size.\n :return: a number or :class:`torch.Tensor` or :class:`np.ndarray` that can be broadcast to other\n tensors with inputs ``new_inputs``.\n :rtype: int or float or torch.Tensor or np.ndarray\n '
assert isinstance(new_inputs, OrderedDict)
assert isinstance(x, (Number, Tensor))
assert all((isinstance(d.dtype, int) for d in x.inputs.values()))
data = x.data
if isinstance(x, Number):
return data
old_inputs = x.inputs
if (old_inputs == new_inputs):
return data
x_keys = tuple(old_inputs)
data = ops.permute(data, (tuple((x_keys.index(k) for k in new_inputs if (k in old_inputs))) + tuple(range(len(old_inputs), len(data.shape)))))
data = data.reshape((tuple(((old_inputs[k].dtype if (k in old_inputs) else 1) for k in new_inputs)) + x.output.shape))
if expand:
data = ops.expand(data, (tuple((d.dtype for d in new_inputs.values())) + x.output.shape))
return data | Permute and add dims to a tensor to match desired ``new_inputs``.
:param OrderedDict new_inputs: A target set of inputs.
:param funsor.terms.Funsor x: A :class:`Tensor` or
:class:`~funsor.terms.Number` .
:param bool expand: If False (default), set result size to 1 for any input
of ``x`` not in ``new_inputs``; if True expand to ``new_inputs`` size.
:return: a number or :class:`torch.Tensor` or :class:`np.ndarray` that can be broadcast to other
tensors with inputs ``new_inputs``.
:rtype: int or float or torch.Tensor or np.ndarray | funsor/tensor.py | align_tensor | ordabayevy/funsor | 0 | python | def align_tensor(new_inputs, x, expand=False):
'\n Permute and add dims to a tensor to match desired ``new_inputs``.\n\n :param OrderedDict new_inputs: A target set of inputs.\n :param funsor.terms.Funsor x: A :class:`Tensor` or\n :class:`~funsor.terms.Number` .\n :param bool expand: If False (default), set result size to 1 for any input\n of ``x`` not in ``new_inputs``; if True expand to ``new_inputs`` size.\n :return: a number or :class:`torch.Tensor` or :class:`np.ndarray` that can be broadcast to other\n tensors with inputs ``new_inputs``.\n :rtype: int or float or torch.Tensor or np.ndarray\n '
assert isinstance(new_inputs, OrderedDict)
assert isinstance(x, (Number, Tensor))
assert all((isinstance(d.dtype, int) for d in x.inputs.values()))
data = x.data
if isinstance(x, Number):
return data
old_inputs = x.inputs
if (old_inputs == new_inputs):
return data
x_keys = tuple(old_inputs)
data = ops.permute(data, (tuple((x_keys.index(k) for k in new_inputs if (k in old_inputs))) + tuple(range(len(old_inputs), len(data.shape)))))
data = data.reshape((tuple(((old_inputs[k].dtype if (k in old_inputs) else 1) for k in new_inputs)) + x.output.shape))
if expand:
data = ops.expand(data, (tuple((d.dtype for d in new_inputs.values())) + x.output.shape))
return data | def align_tensor(new_inputs, x, expand=False):
'\n Permute and add dims to a tensor to match desired ``new_inputs``.\n\n :param OrderedDict new_inputs: A target set of inputs.\n :param funsor.terms.Funsor x: A :class:`Tensor` or\n :class:`~funsor.terms.Number` .\n :param bool expand: If False (default), set result size to 1 for any input\n of ``x`` not in ``new_inputs``; if True expand to ``new_inputs`` size.\n :return: a number or :class:`torch.Tensor` or :class:`np.ndarray` that can be broadcast to other\n tensors with inputs ``new_inputs``.\n :rtype: int or float or torch.Tensor or np.ndarray\n '
assert isinstance(new_inputs, OrderedDict)
assert isinstance(x, (Number, Tensor))
assert all((isinstance(d.dtype, int) for d in x.inputs.values()))
data = x.data
if isinstance(x, Number):
return data
old_inputs = x.inputs
if (old_inputs == new_inputs):
return data
x_keys = tuple(old_inputs)
data = ops.permute(data, (tuple((x_keys.index(k) for k in new_inputs if (k in old_inputs))) + tuple(range(len(old_inputs), len(data.shape)))))
data = data.reshape((tuple(((old_inputs[k].dtype if (k in old_inputs) else 1) for k in new_inputs)) + x.output.shape))
if expand:
data = ops.expand(data, (tuple((d.dtype for d in new_inputs.values())) + x.output.shape))
return data<|docstring|>Permute and add dims to a tensor to match desired ``new_inputs``.
:param OrderedDict new_inputs: A target set of inputs.
:param funsor.terms.Funsor x: A :class:`Tensor` or
:class:`~funsor.terms.Number` .
:param bool expand: If False (default), set result size to 1 for any input
of ``x`` not in ``new_inputs``; if True expand to ``new_inputs`` size.
:return: a number or :class:`torch.Tensor` or :class:`np.ndarray` that can be broadcast to other
tensors with inputs ``new_inputs``.
:rtype: int or float or torch.Tensor or np.ndarray<|endoftext|> |
418bac1229213af8e7a3b0c4f846d1eb4397d36c26b11eefe38e71163f90c054 | def align_tensors(*args, **kwargs):
'\n Permute multiple tensors before applying a broadcasted op.\n\n This is mainly useful for implementing eager funsor operations.\n\n :param funsor.terms.Funsor \\*args: Multiple :class:`Tensor` s and\n :class:`~funsor.terms.Number` s.\n :param bool expand: Whether to expand input tensors. Defaults to False.\n :return: a pair ``(inputs, tensors)`` where tensors are all\n :class:`torch.Tensor` s or :class:`np.ndarray` s\n that can be broadcast together to a single data\n with given ``inputs``.\n :rtype: tuple\n '
expand = kwargs.pop('expand', False)
assert (not kwargs)
inputs = OrderedDict()
for x in args:
inputs.update(x.inputs)
tensors = [align_tensor(inputs, x, expand=expand) for x in args]
return (inputs, tensors) | Permute multiple tensors before applying a broadcasted op.
This is mainly useful for implementing eager funsor operations.
:param funsor.terms.Funsor \*args: Multiple :class:`Tensor` s and
:class:`~funsor.terms.Number` s.
:param bool expand: Whether to expand input tensors. Defaults to False.
:return: a pair ``(inputs, tensors)`` where tensors are all
:class:`torch.Tensor` s or :class:`np.ndarray` s
that can be broadcast together to a single data
with given ``inputs``.
:rtype: tuple | funsor/tensor.py | align_tensors | ordabayevy/funsor | 0 | python | def align_tensors(*args, **kwargs):
'\n Permute multiple tensors before applying a broadcasted op.\n\n This is mainly useful for implementing eager funsor operations.\n\n :param funsor.terms.Funsor \\*args: Multiple :class:`Tensor` s and\n :class:`~funsor.terms.Number` s.\n :param bool expand: Whether to expand input tensors. Defaults to False.\n :return: a pair ``(inputs, tensors)`` where tensors are all\n :class:`torch.Tensor` s or :class:`np.ndarray` s\n that can be broadcast together to a single data\n with given ``inputs``.\n :rtype: tuple\n '
expand = kwargs.pop('expand', False)
assert (not kwargs)
inputs = OrderedDict()
for x in args:
inputs.update(x.inputs)
tensors = [align_tensor(inputs, x, expand=expand) for x in args]
return (inputs, tensors) | def align_tensors(*args, **kwargs):
'\n Permute multiple tensors before applying a broadcasted op.\n\n This is mainly useful for implementing eager funsor operations.\n\n :param funsor.terms.Funsor \\*args: Multiple :class:`Tensor` s and\n :class:`~funsor.terms.Number` s.\n :param bool expand: Whether to expand input tensors. Defaults to False.\n :return: a pair ``(inputs, tensors)`` where tensors are all\n :class:`torch.Tensor` s or :class:`np.ndarray` s\n that can be broadcast together to a single data\n with given ``inputs``.\n :rtype: tuple\n '
expand = kwargs.pop('expand', False)
assert (not kwargs)
inputs = OrderedDict()
for x in args:
inputs.update(x.inputs)
tensors = [align_tensor(inputs, x, expand=expand) for x in args]
return (inputs, tensors)<|docstring|>Permute multiple tensors before applying a broadcasted op.
This is mainly useful for implementing eager funsor operations.
:param funsor.terms.Funsor \*args: Multiple :class:`Tensor` s and
:class:`~funsor.terms.Number` s.
:param bool expand: Whether to expand input tensors. Defaults to False.
:return: a pair ``(inputs, tensors)`` where tensors are all
:class:`torch.Tensor` s or :class:`np.ndarray` s
that can be broadcast together to a single data
with given ``inputs``.
:rtype: tuple<|endoftext|> |
551768af662168de6e5f57e5a3f8096574dc5e461a13f1ae4d5e4a9ab895b11c | def function(*signature):
'\n Decorator to wrap a PyTorch/NumPy function, using either type hints or\n explicit type annotations.\n\n Example::\n\n # Using type hints:\n @funsor.tensor.function\n def matmul(x: Reals[3, 4], y: Reals[4, 5]) -> Reals[3, 5]:\n return torch.matmul(x, y)\n\n # Using explicit type annotations:\n @funsor.tensor.function(Reals[3, 4], Reals[4, 5], Reals[3, 5])\n def matmul(x, y):\n return torch.matmul(x, y)\n\n @funsor.tensor.function(Reals[10], Reals[10, 10], Reals[10], Real)\n def mvn_log_prob(loc, scale_tril, x):\n d = torch.distributions.MultivariateNormal(loc, scale_tril)\n return d.log_prob(x)\n\n To support functions that output nested tuples of tensors, specify a nested\n :py:class:`~typing.Tuple` of output types, for example::\n\n @funsor.tensor.function\n def max_and_argmax(x: Reals[8]) -> Tuple[Real, Bint[8]]:\n return torch.max(x, dim=-1)\n\n :param \\*signature: A sequence if input domains followed by a final output\n domain or nested tuple of output domains.\n '
assert signature
if (len(signature) == 1):
fn = signature[0]
if (callable(fn) and (not isinstance(fn, ArrayType))):
inputs = typing.get_type_hints(fn)
output = inputs.pop('return')
assert all((isinstance(d, ArrayType) for d in inputs.values()))
assert (isinstance(output, (ArrayType, tuple)) or (output.__origin__ in (tuple, typing.Tuple)))
return _function(inputs, output, fn)
(inputs, output) = (signature[:(- 1)], signature[(- 1)])
output = _tuple_to_Tuple(output)
assert all((isinstance(d, ArrayType) for d in inputs))
assert (isinstance(output, (ArrayType, tuple)) or (output.__origin__ in (tuple, typing.Tuple)))
return functools.partial(_function, inputs, output) | Decorator to wrap a PyTorch/NumPy function, using either type hints or
explicit type annotations.
Example::
# Using type hints:
@funsor.tensor.function
def matmul(x: Reals[3, 4], y: Reals[4, 5]) -> Reals[3, 5]:
return torch.matmul(x, y)
# Using explicit type annotations:
@funsor.tensor.function(Reals[3, 4], Reals[4, 5], Reals[3, 5])
def matmul(x, y):
return torch.matmul(x, y)
@funsor.tensor.function(Reals[10], Reals[10, 10], Reals[10], Real)
def mvn_log_prob(loc, scale_tril, x):
d = torch.distributions.MultivariateNormal(loc, scale_tril)
return d.log_prob(x)
To support functions that output nested tuples of tensors, specify a nested
:py:class:`~typing.Tuple` of output types, for example::
@funsor.tensor.function
def max_and_argmax(x: Reals[8]) -> Tuple[Real, Bint[8]]:
return torch.max(x, dim=-1)
:param \*signature: A sequence if input domains followed by a final output
domain or nested tuple of output domains. | funsor/tensor.py | function | ordabayevy/funsor | 0 | python | def function(*signature):
'\n Decorator to wrap a PyTorch/NumPy function, using either type hints or\n explicit type annotations.\n\n Example::\n\n # Using type hints:\n @funsor.tensor.function\n def matmul(x: Reals[3, 4], y: Reals[4, 5]) -> Reals[3, 5]:\n return torch.matmul(x, y)\n\n # Using explicit type annotations:\n @funsor.tensor.function(Reals[3, 4], Reals[4, 5], Reals[3, 5])\n def matmul(x, y):\n return torch.matmul(x, y)\n\n @funsor.tensor.function(Reals[10], Reals[10, 10], Reals[10], Real)\n def mvn_log_prob(loc, scale_tril, x):\n d = torch.distributions.MultivariateNormal(loc, scale_tril)\n return d.log_prob(x)\n\n To support functions that output nested tuples of tensors, specify a nested\n :py:class:`~typing.Tuple` of output types, for example::\n\n @funsor.tensor.function\n def max_and_argmax(x: Reals[8]) -> Tuple[Real, Bint[8]]:\n return torch.max(x, dim=-1)\n\n :param \\*signature: A sequence if input domains followed by a final output\n domain or nested tuple of output domains.\n '
assert signature
if (len(signature) == 1):
fn = signature[0]
if (callable(fn) and (not isinstance(fn, ArrayType))):
inputs = typing.get_type_hints(fn)
output = inputs.pop('return')
assert all((isinstance(d, ArrayType) for d in inputs.values()))
assert (isinstance(output, (ArrayType, tuple)) or (output.__origin__ in (tuple, typing.Tuple)))
return _function(inputs, output, fn)
(inputs, output) = (signature[:(- 1)], signature[(- 1)])
output = _tuple_to_Tuple(output)
assert all((isinstance(d, ArrayType) for d in inputs))
assert (isinstance(output, (ArrayType, tuple)) or (output.__origin__ in (tuple, typing.Tuple)))
return functools.partial(_function, inputs, output) | def function(*signature):
'\n Decorator to wrap a PyTorch/NumPy function, using either type hints or\n explicit type annotations.\n\n Example::\n\n # Using type hints:\n @funsor.tensor.function\n def matmul(x: Reals[3, 4], y: Reals[4, 5]) -> Reals[3, 5]:\n return torch.matmul(x, y)\n\n # Using explicit type annotations:\n @funsor.tensor.function(Reals[3, 4], Reals[4, 5], Reals[3, 5])\n def matmul(x, y):\n return torch.matmul(x, y)\n\n @funsor.tensor.function(Reals[10], Reals[10, 10], Reals[10], Real)\n def mvn_log_prob(loc, scale_tril, x):\n d = torch.distributions.MultivariateNormal(loc, scale_tril)\n return d.log_prob(x)\n\n To support functions that output nested tuples of tensors, specify a nested\n :py:class:`~typing.Tuple` of output types, for example::\n\n @funsor.tensor.function\n def max_and_argmax(x: Reals[8]) -> Tuple[Real, Bint[8]]:\n return torch.max(x, dim=-1)\n\n :param \\*signature: A sequence if input domains followed by a final output\n domain or nested tuple of output domains.\n '
assert signature
if (len(signature) == 1):
fn = signature[0]
if (callable(fn) and (not isinstance(fn, ArrayType))):
inputs = typing.get_type_hints(fn)
output = inputs.pop('return')
assert all((isinstance(d, ArrayType) for d in inputs.values()))
assert (isinstance(output, (ArrayType, tuple)) or (output.__origin__ in (tuple, typing.Tuple)))
return _function(inputs, output, fn)
(inputs, output) = (signature[:(- 1)], signature[(- 1)])
output = _tuple_to_Tuple(output)
assert all((isinstance(d, ArrayType) for d in inputs))
assert (isinstance(output, (ArrayType, tuple)) or (output.__origin__ in (tuple, typing.Tuple)))
return functools.partial(_function, inputs, output)<|docstring|>Decorator to wrap a PyTorch/NumPy function, using either type hints or
explicit type annotations.
Example::
# Using type hints:
@funsor.tensor.function
def matmul(x: Reals[3, 4], y: Reals[4, 5]) -> Reals[3, 5]:
return torch.matmul(x, y)
# Using explicit type annotations:
@funsor.tensor.function(Reals[3, 4], Reals[4, 5], Reals[3, 5])
def matmul(x, y):
return torch.matmul(x, y)
@funsor.tensor.function(Reals[10], Reals[10, 10], Reals[10], Real)
def mvn_log_prob(loc, scale_tril, x):
d = torch.distributions.MultivariateNormal(loc, scale_tril)
return d.log_prob(x)
To support functions that output nested tuples of tensors, specify a nested
:py:class:`~typing.Tuple` of output types, for example::
@funsor.tensor.function
def max_and_argmax(x: Reals[8]) -> Tuple[Real, Bint[8]]:
return torch.max(x, dim=-1)
:param \*signature: A sequence if input domains followed by a final output
domain or nested tuple of output domains.<|endoftext|> |
be52de52644bb365699030a9be380bf2b57e424e9126430b88355a8fe27ff26d | def tensordot(x, y, dims):
'\n Wrapper around :func:`torch.tensordot` or :func:`np.tensordot`\n to operate on real-valued Funsors.\n\n Note this operates only on the ``output`` tensor. To perform sum-product\n contractions on named dimensions, instead use ``+`` and\n :class:`~funsor.terms.Reduce`.\n\n Arguments should satisfy::\n\n len(x.shape) >= dims\n len(y.shape) >= dims\n dims == 0 or x.shape[-dims:] == y.shape[:dims]\n\n :param Funsor x: A left hand argument.\n :param Funsor y: A y hand argument.\n :param int dims: The number of dimension of overlap of output shape.\n :rtype: Funsor\n '
assert (dims >= 0)
assert (len(x.shape) >= dims)
assert (len(y.shape) >= dims)
assert ((dims == 0) or (x.shape[(- dims):] == y.shape[:dims]))
(x_start, x_end) = (0, len(x.output.shape))
y_start = (x_end - dims)
y_end = (y_start + len(y.output.shape))
symbols = 'abcdefghijklmnopqrstuvwxyz'
equation = '{},{}->{}'.format(symbols[x_start:x_end], symbols[y_start:y_end], (symbols[x_start:y_start] + symbols[x_end:y_end]))
return Einsum(equation, (x, y)) | Wrapper around :func:`torch.tensordot` or :func:`np.tensordot`
to operate on real-valued Funsors.
Note this operates only on the ``output`` tensor. To perform sum-product
contractions on named dimensions, instead use ``+`` and
:class:`~funsor.terms.Reduce`.
Arguments should satisfy::
len(x.shape) >= dims
len(y.shape) >= dims
dims == 0 or x.shape[-dims:] == y.shape[:dims]
:param Funsor x: A left hand argument.
:param Funsor y: A y hand argument.
:param int dims: The number of dimension of overlap of output shape.
:rtype: Funsor | funsor/tensor.py | tensordot | ordabayevy/funsor | 0 | python | def tensordot(x, y, dims):
'\n Wrapper around :func:`torch.tensordot` or :func:`np.tensordot`\n to operate on real-valued Funsors.\n\n Note this operates only on the ``output`` tensor. To perform sum-product\n contractions on named dimensions, instead use ``+`` and\n :class:`~funsor.terms.Reduce`.\n\n Arguments should satisfy::\n\n len(x.shape) >= dims\n len(y.shape) >= dims\n dims == 0 or x.shape[-dims:] == y.shape[:dims]\n\n :param Funsor x: A left hand argument.\n :param Funsor y: A y hand argument.\n :param int dims: The number of dimension of overlap of output shape.\n :rtype: Funsor\n '
assert (dims >= 0)
assert (len(x.shape) >= dims)
assert (len(y.shape) >= dims)
assert ((dims == 0) or (x.shape[(- dims):] == y.shape[:dims]))
(x_start, x_end) = (0, len(x.output.shape))
y_start = (x_end - dims)
y_end = (y_start + len(y.output.shape))
symbols = 'abcdefghijklmnopqrstuvwxyz'
equation = '{},{}->{}'.format(symbols[x_start:x_end], symbols[y_start:y_end], (symbols[x_start:y_start] + symbols[x_end:y_end]))
return Einsum(equation, (x, y)) | def tensordot(x, y, dims):
'\n Wrapper around :func:`torch.tensordot` or :func:`np.tensordot`\n to operate on real-valued Funsors.\n\n Note this operates only on the ``output`` tensor. To perform sum-product\n contractions on named dimensions, instead use ``+`` and\n :class:`~funsor.terms.Reduce`.\n\n Arguments should satisfy::\n\n len(x.shape) >= dims\n len(y.shape) >= dims\n dims == 0 or x.shape[-dims:] == y.shape[:dims]\n\n :param Funsor x: A left hand argument.\n :param Funsor y: A y hand argument.\n :param int dims: The number of dimension of overlap of output shape.\n :rtype: Funsor\n '
assert (dims >= 0)
assert (len(x.shape) >= dims)
assert (len(y.shape) >= dims)
assert ((dims == 0) or (x.shape[(- dims):] == y.shape[:dims]))
(x_start, x_end) = (0, len(x.output.shape))
y_start = (x_end - dims)
y_end = (y_start + len(y.output.shape))
symbols = 'abcdefghijklmnopqrstuvwxyz'
equation = '{},{}->{}'.format(symbols[x_start:x_end], symbols[y_start:y_end], (symbols[x_start:y_start] + symbols[x_end:y_end]))
return Einsum(equation, (x, y))<|docstring|>Wrapper around :func:`torch.tensordot` or :func:`np.tensordot`
to operate on real-valued Funsors.
Note this operates only on the ``output`` tensor. To perform sum-product
contractions on named dimensions, instead use ``+`` and
:class:`~funsor.terms.Reduce`.
Arguments should satisfy::
len(x.shape) >= dims
len(y.shape) >= dims
dims == 0 or x.shape[-dims:] == y.shape[:dims]
:param Funsor x: A left hand argument.
:param Funsor y: A y hand argument.
:param int dims: The number of dimension of overlap of output shape.
:rtype: Funsor<|endoftext|> |
0f12deb34439897988174505f83e9891e25ea783a954544748585ba6b0ef70c6 | def stack(parts, dim=0):
'\n Wrapper around :func:`torch.stack` or :func:`np.stack` to operate on real-valued Funsors.\n\n Note this operates only on the ``output`` tensor. To stack funsors in a\n new named dim, instead use :class:`~funsor.terms.Stack`.\n\n :param tuple parts: A tuple of funsors.\n :param int dim: A torch dim along which to stack.\n :rtype: Funsor\n '
assert isinstance(dim, int)
assert isinstance(parts, tuple)
assert (len(set((x.output for x in parts))) == 1)
shape = parts[0].output.shape
if (dim >= 0):
dim = ((dim - len(shape)) - 1)
assert (dim < 0)
split = ((dim + len(shape)) + 1)
shape = ((shape[:split] + (len(parts),)) + shape[split:])
output = Array[(parts[0].dtype, shape)]
fn = functools.partial(ops.stack, dim)
return Function(fn, output, parts) | Wrapper around :func:`torch.stack` or :func:`np.stack` to operate on real-valued Funsors.
Note this operates only on the ``output`` tensor. To stack funsors in a
new named dim, instead use :class:`~funsor.terms.Stack`.
:param tuple parts: A tuple of funsors.
:param int dim: A torch dim along which to stack.
:rtype: Funsor | funsor/tensor.py | stack | ordabayevy/funsor | 0 | python | def stack(parts, dim=0):
'\n Wrapper around :func:`torch.stack` or :func:`np.stack` to operate on real-valued Funsors.\n\n Note this operates only on the ``output`` tensor. To stack funsors in a\n new named dim, instead use :class:`~funsor.terms.Stack`.\n\n :param tuple parts: A tuple of funsors.\n :param int dim: A torch dim along which to stack.\n :rtype: Funsor\n '
assert isinstance(dim, int)
assert isinstance(parts, tuple)
assert (len(set((x.output for x in parts))) == 1)
shape = parts[0].output.shape
if (dim >= 0):
dim = ((dim - len(shape)) - 1)
assert (dim < 0)
split = ((dim + len(shape)) + 1)
shape = ((shape[:split] + (len(parts),)) + shape[split:])
output = Array[(parts[0].dtype, shape)]
fn = functools.partial(ops.stack, dim)
return Function(fn, output, parts) | def stack(parts, dim=0):
'\n Wrapper around :func:`torch.stack` or :func:`np.stack` to operate on real-valued Funsors.\n\n Note this operates only on the ``output`` tensor. To stack funsors in a\n new named dim, instead use :class:`~funsor.terms.Stack`.\n\n :param tuple parts: A tuple of funsors.\n :param int dim: A torch dim along which to stack.\n :rtype: Funsor\n '
assert isinstance(dim, int)
assert isinstance(parts, tuple)
assert (len(set((x.output for x in parts))) == 1)
shape = parts[0].output.shape
if (dim >= 0):
dim = ((dim - len(shape)) - 1)
assert (dim < 0)
split = ((dim + len(shape)) + 1)
shape = ((shape[:split] + (len(parts),)) + shape[split:])
output = Array[(parts[0].dtype, shape)]
fn = functools.partial(ops.stack, dim)
return Function(fn, output, parts)<|docstring|>Wrapper around :func:`torch.stack` or :func:`np.stack` to operate on real-valued Funsors.
Note this operates only on the ``output`` tensor. To stack funsors in a
new named dim, instead use :class:`~funsor.terms.Stack`.
:param tuple parts: A tuple of funsors.
:param int dim: A torch dim along which to stack.
:rtype: Funsor<|endoftext|> |
2ae5590055f847de32277eb99c8f3a1bd32c811cd8b1920924b64310b0bf4c46 | def new_arange(self, name, *args, **kwargs):
'\n Helper to create a named :func:`torch.arange` or :func:`np.arange` funsor.\n In some cases this can be replaced by a symbolic\n :class:`~funsor.terms.Slice` .\n\n :param str name: A variable name.\n :param int start:\n :param int stop:\n :param int step: Three args following :py:class:`slice` semantics.\n :param int dtype: An optional bounded integer type of this slice.\n :rtype: Tensor\n '
start = 0
step = 1
dtype = None
if (len(args) == 1):
stop = args[0]
dtype = kwargs.pop('dtype', stop)
elif (len(args) == 2):
(start, stop) = args
dtype = kwargs.pop('dtype', stop)
elif (len(args) == 3):
(start, stop, step) = args
dtype = kwargs.pop('dtype', stop)
elif (len(args) == 4):
(start, stop, step, dtype) = args
else:
raise ValueError
if (step <= 0):
raise ValueError
stop = min(dtype, max(start, stop))
data = ops.new_arange(self.data, start, stop, step)
inputs = OrderedDict([(name, Bint[len(data)])])
return Tensor(data, inputs, dtype=dtype) | Helper to create a named :func:`torch.arange` or :func:`np.arange` funsor.
In some cases this can be replaced by a symbolic
:class:`~funsor.terms.Slice` .
:param str name: A variable name.
:param int start:
:param int stop:
:param int step: Three args following :py:class:`slice` semantics.
:param int dtype: An optional bounded integer type of this slice.
:rtype: Tensor | funsor/tensor.py | new_arange | ordabayevy/funsor | 0 | python | def new_arange(self, name, *args, **kwargs):
'\n Helper to create a named :func:`torch.arange` or :func:`np.arange` funsor.\n In some cases this can be replaced by a symbolic\n :class:`~funsor.terms.Slice` .\n\n :param str name: A variable name.\n :param int start:\n :param int stop:\n :param int step: Three args following :py:class:`slice` semantics.\n :param int dtype: An optional bounded integer type of this slice.\n :rtype: Tensor\n '
start = 0
step = 1
dtype = None
if (len(args) == 1):
stop = args[0]
dtype = kwargs.pop('dtype', stop)
elif (len(args) == 2):
(start, stop) = args
dtype = kwargs.pop('dtype', stop)
elif (len(args) == 3):
(start, stop, step) = args
dtype = kwargs.pop('dtype', stop)
elif (len(args) == 4):
(start, stop, step, dtype) = args
else:
raise ValueError
if (step <= 0):
raise ValueError
stop = min(dtype, max(start, stop))
data = ops.new_arange(self.data, start, stop, step)
inputs = OrderedDict([(name, Bint[len(data)])])
return Tensor(data, inputs, dtype=dtype) | def new_arange(self, name, *args, **kwargs):
'\n Helper to create a named :func:`torch.arange` or :func:`np.arange` funsor.\n In some cases this can be replaced by a symbolic\n :class:`~funsor.terms.Slice` .\n\n :param str name: A variable name.\n :param int start:\n :param int stop:\n :param int step: Three args following :py:class:`slice` semantics.\n :param int dtype: An optional bounded integer type of this slice.\n :rtype: Tensor\n '
start = 0
step = 1
dtype = None
if (len(args) == 1):
stop = args[0]
dtype = kwargs.pop('dtype', stop)
elif (len(args) == 2):
(start, stop) = args
dtype = kwargs.pop('dtype', stop)
elif (len(args) == 3):
(start, stop, step) = args
dtype = kwargs.pop('dtype', stop)
elif (len(args) == 4):
(start, stop, step, dtype) = args
else:
raise ValueError
if (step <= 0):
raise ValueError
stop = min(dtype, max(start, stop))
data = ops.new_arange(self.data, start, stop, step)
inputs = OrderedDict([(name, Bint[len(data)])])
return Tensor(data, inputs, dtype=dtype)<|docstring|>Helper to create a named :func:`torch.arange` or :func:`np.arange` funsor.
In some cases this can be replaced by a symbolic
:class:`~funsor.terms.Slice` .
:param str name: A variable name.
:param int start:
:param int stop:
:param int step: Three args following :py:class:`slice` semantics.
:param int dtype: An optional bounded integer type of this slice.
:rtype: Tensor<|endoftext|> |
86b77c51f2437b4876d849ce38858f16e60abd6d9db41867de95d43f60e4ae53 | def materialize(self, x):
'\n Attempt to convert a Funsor to a :class:`~funsor.terms.Number` or\n :class:`Tensor` by substituting :func:`arange` s into its free variables.\n\n :arg Funsor x: A funsor.\n :rtype: Funsor\n '
assert isinstance(x, Funsor)
if isinstance(x, (Number, Tensor)):
return x
subs = []
for (name, domain) in x.inputs.items():
if isinstance(domain.dtype, int):
subs.append((name, self.new_arange(name, domain.dtype)))
subs = tuple(subs)
return substitute(x, subs) | Attempt to convert a Funsor to a :class:`~funsor.terms.Number` or
:class:`Tensor` by substituting :func:`arange` s into its free variables.
:arg Funsor x: A funsor.
:rtype: Funsor | funsor/tensor.py | materialize | ordabayevy/funsor | 0 | python | def materialize(self, x):
'\n Attempt to convert a Funsor to a :class:`~funsor.terms.Number` or\n :class:`Tensor` by substituting :func:`arange` s into its free variables.\n\n :arg Funsor x: A funsor.\n :rtype: Funsor\n '
assert isinstance(x, Funsor)
if isinstance(x, (Number, Tensor)):
return x
subs = []
for (name, domain) in x.inputs.items():
if isinstance(domain.dtype, int):
subs.append((name, self.new_arange(name, domain.dtype)))
subs = tuple(subs)
return substitute(x, subs) | def materialize(self, x):
'\n Attempt to convert a Funsor to a :class:`~funsor.terms.Number` or\n :class:`Tensor` by substituting :func:`arange` s into its free variables.\n\n :arg Funsor x: A funsor.\n :rtype: Funsor\n '
assert isinstance(x, Funsor)
if isinstance(x, (Number, Tensor)):
return x
subs = []
for (name, domain) in x.inputs.items():
if isinstance(domain.dtype, int):
subs.append((name, self.new_arange(name, domain.dtype)))
subs = tuple(subs)
return substitute(x, subs)<|docstring|>Attempt to convert a Funsor to a :class:`~funsor.terms.Number` or
:class:`Tensor` by substituting :func:`arange` s into its free variables.
:arg Funsor x: A funsor.
:rtype: Funsor<|endoftext|> |
f7306d372926930a05ba52fd9995ff1759f8d3635d92d8864b4fa9f03b554838 | def test_smoke(self):
'Smoke test for KeyUtil.'
with UnitTestContext() as context:
self.assertTrue((KeyUtil.remove_prefix('MyType=A;B;C', 'MyType') == 'A;B;C'))
with self.assertRaises(Exception):
KeyUtil.remove_prefix('MyType=A;B;C', 'OtherType')
self.assertTrue((KeyUtil.get_token('MyType=A;B;C', 'MyType', 3, 1) == 'B'))
with self.assertRaises(Exception):
KeyUtil.get_token('MyType=A;B;C', 'OtherType', 3, 1)
with self.assertRaises(Exception):
KeyUtil.get_token('MyType=A;B', 'MyType', 3, 1)
with self.assertRaises(Exception):
KeyUtil.get_token('MyType=A;B;C', 'MyType', 3, 3) | Smoke test for KeyUtil. | py/datacentric/test/storage/test_key_util.py | test_smoke | datacentricorg/datacentric-py | 1 | python | def test_smoke(self):
with UnitTestContext() as context:
self.assertTrue((KeyUtil.remove_prefix('MyType=A;B;C', 'MyType') == 'A;B;C'))
with self.assertRaises(Exception):
KeyUtil.remove_prefix('MyType=A;B;C', 'OtherType')
self.assertTrue((KeyUtil.get_token('MyType=A;B;C', 'MyType', 3, 1) == 'B'))
with self.assertRaises(Exception):
KeyUtil.get_token('MyType=A;B;C', 'OtherType', 3, 1)
with self.assertRaises(Exception):
KeyUtil.get_token('MyType=A;B', 'MyType', 3, 1)
with self.assertRaises(Exception):
KeyUtil.get_token('MyType=A;B;C', 'MyType', 3, 3) | def test_smoke(self):
with UnitTestContext() as context:
self.assertTrue((KeyUtil.remove_prefix('MyType=A;B;C', 'MyType') == 'A;B;C'))
with self.assertRaises(Exception):
KeyUtil.remove_prefix('MyType=A;B;C', 'OtherType')
self.assertTrue((KeyUtil.get_token('MyType=A;B;C', 'MyType', 3, 1) == 'B'))
with self.assertRaises(Exception):
KeyUtil.get_token('MyType=A;B;C', 'OtherType', 3, 1)
with self.assertRaises(Exception):
KeyUtil.get_token('MyType=A;B', 'MyType', 3, 1)
with self.assertRaises(Exception):
KeyUtil.get_token('MyType=A;B;C', 'MyType', 3, 3)<|docstring|>Smoke test for KeyUtil.<|endoftext|> |
b3af549fe04c108dd5714ecd7127742b8617e48b59bee2539209fd4616f93309 | def test_vacuum_model_vacuums_test_model(vacuum: vacuum.Vacuum, test_project_name, test_model, test_unused_explores):
'vacuum.models() should return unused explores in a used model.'
result1 = vacuum.models(project=test_project_name, model=test_model['name'])
result2 = vacuum.models(model=test_model['name'])
assert (result1 == result2)
assert isinstance(result1, list)
assert (len(result1) == 1)
result = result1[0]
assert (result['Model'] == test_model['name'])
assert (result['Unused Explores'] == '\n'.join(test_unused_explores))
assert (result['Model Query Count'] > 0) | vacuum.models() should return unused explores in a used model. | tests/test_vacuum.py | test_vacuum_model_vacuums_test_model | iamaziz/henry | 42 | python | def test_vacuum_model_vacuums_test_model(vacuum: vacuum.Vacuum, test_project_name, test_model, test_unused_explores):
result1 = vacuum.models(project=test_project_name, model=test_model['name'])
result2 = vacuum.models(model=test_model['name'])
assert (result1 == result2)
assert isinstance(result1, list)
assert (len(result1) == 1)
result = result1[0]
assert (result['Model'] == test_model['name'])
assert (result['Unused Explores'] == '\n'.join(test_unused_explores))
assert (result['Model Query Count'] > 0) | def test_vacuum_model_vacuums_test_model(vacuum: vacuum.Vacuum, test_project_name, test_model, test_unused_explores):
result1 = vacuum.models(project=test_project_name, model=test_model['name'])
result2 = vacuum.models(model=test_model['name'])
assert (result1 == result2)
assert isinstance(result1, list)
assert (len(result1) == 1)
result = result1[0]
assert (result['Model'] == test_model['name'])
assert (result['Unused Explores'] == '\n'.join(test_unused_explores))
assert (result['Model Query Count'] > 0)<|docstring|>vacuum.models() should return unused explores in a used model.<|endoftext|> |
85b35a73abbb6d0dbec040a9235e28212fa20bd5173e969adc08a19ae2ded3da | def test_vacuum_models_vacuums_unused_test_model(vacuum: vacuum.Vacuum, test_project_name, test_unused_model, test_unused_model_explore_names):
'vacuum.models() should return all explores in unused models.'
result = vacuum.models(model=test_unused_model['name'])
assert isinstance(result, list)
assert (len(result) == 1)
result = result[0]
assert (result['Model'] == test_unused_model['name'])
assert (result['Unused Explores'] == '\n'.join(test_unused_model_explore_names))
assert (result['Model Query Count'] == 0) | vacuum.models() should return all explores in unused models. | tests/test_vacuum.py | test_vacuum_models_vacuums_unused_test_model | iamaziz/henry | 42 | python | def test_vacuum_models_vacuums_unused_test_model(vacuum: vacuum.Vacuum, test_project_name, test_unused_model, test_unused_model_explore_names):
result = vacuum.models(model=test_unused_model['name'])
assert isinstance(result, list)
assert (len(result) == 1)
result = result[0]
assert (result['Model'] == test_unused_model['name'])
assert (result['Unused Explores'] == '\n'.join(test_unused_model_explore_names))
assert (result['Model Query Count'] == 0) | def test_vacuum_models_vacuums_unused_test_model(vacuum: vacuum.Vacuum, test_project_name, test_unused_model, test_unused_model_explore_names):
result = vacuum.models(model=test_unused_model['name'])
assert isinstance(result, list)
assert (len(result) == 1)
result = result[0]
assert (result['Model'] == test_unused_model['name'])
assert (result['Unused Explores'] == '\n'.join(test_unused_model_explore_names))
assert (result['Model Query Count'] == 0)<|docstring|>vacuum.models() should return all explores in unused models.<|endoftext|> |
a1961aa2ad10194791f065c40d84d8e841dc81206438f54187a66337c519124c | @pytest.mark.parametrize('project, model, msg', [('BadProject', 'henry_qa', 'error occured while getting projects.'), ('henry', 'BadModel', 'error occured while getting models.'), ('BadProject', 'BadModel', 'error occured while getting projects.')])
def test_vacuum_models_throws_for_bad_filters(vacuum: vacuum.Vacuum, project, model, msg):
'vacuum.models() should error for bad project/model filter values.'
with pytest.raises(exceptions.NotFoundError) as exc:
vacuum.models(project=project, model=model)
assert (msg in str(exc.value)) | vacuum.models() should error for bad project/model filter values. | tests/test_vacuum.py | test_vacuum_models_throws_for_bad_filters | iamaziz/henry | 42 | python | @pytest.mark.parametrize('project, model, msg', [('BadProject', 'henry_qa', 'error occured while getting projects.'), ('henry', 'BadModel', 'error occured while getting models.'), ('BadProject', 'BadModel', 'error occured while getting projects.')])
def test_vacuum_models_throws_for_bad_filters(vacuum: vacuum.Vacuum, project, model, msg):
with pytest.raises(exceptions.NotFoundError) as exc:
vacuum.models(project=project, model=model)
assert (msg in str(exc.value)) | @pytest.mark.parametrize('project, model, msg', [('BadProject', 'henry_qa', 'error occured while getting projects.'), ('henry', 'BadModel', 'error occured while getting models.'), ('BadProject', 'BadModel', 'error occured while getting projects.')])
def test_vacuum_models_throws_for_bad_filters(vacuum: vacuum.Vacuum, project, model, msg):
with pytest.raises(exceptions.NotFoundError) as exc:
vacuum.models(project=project, model=model)
assert (msg in str(exc.value))<|docstring|>vacuum.models() should error for bad project/model filter values.<|endoftext|> |
80d9e1b7a57e3bd3a410643fd0eb7a56756eee29781500c320034b1837b74cd5 | def test_vacuum_explores_filters(vacuum: vacuum.Vacuum, test_project_name, test_model, test_explores_stats):
'vacuum.explores() should be able to filter models and explores.'
result = vacuum.explores(model=test_model['name'])
assert isinstance(result, list)
assert (len(result) == len(test_explores_stats))
assert all(((r['Model'] == test_model['name']) for r in result))
test_explore_names = [e['name'] for e in test_explores_stats]
assert all(((r['Explore'] in test_explore_names) for r in result))
test_explore = test_explores_stats[0]
result = vacuum.explores(model=test_model['name'], explore=test_explore['name'])
assert isinstance(result, list)
assert (len(result) == 1)
result = result[0]
assert (result['Model'] == test_model['name'])
assert (result['Explore'] == test_explore['name'])
assert (result['Unused Joins'] == test_explore['unused_joins'])
assert (result['Unused Fields'] == test_explore['unused_fields']) | vacuum.explores() should be able to filter models and explores. | tests/test_vacuum.py | test_vacuum_explores_filters | iamaziz/henry | 42 | python | def test_vacuum_explores_filters(vacuum: vacuum.Vacuum, test_project_name, test_model, test_explores_stats):
result = vacuum.explores(model=test_model['name'])
assert isinstance(result, list)
assert (len(result) == len(test_explores_stats))
assert all(((r['Model'] == test_model['name']) for r in result))
test_explore_names = [e['name'] for e in test_explores_stats]
assert all(((r['Explore'] in test_explore_names) for r in result))
test_explore = test_explores_stats[0]
result = vacuum.explores(model=test_model['name'], explore=test_explore['name'])
assert isinstance(result, list)
assert (len(result) == 1)
result = result[0]
assert (result['Model'] == test_model['name'])
assert (result['Explore'] == test_explore['name'])
assert (result['Unused Joins'] == test_explore['unused_joins'])
assert (result['Unused Fields'] == test_explore['unused_fields']) | def test_vacuum_explores_filters(vacuum: vacuum.Vacuum, test_project_name, test_model, test_explores_stats):
result = vacuum.explores(model=test_model['name'])
assert isinstance(result, list)
assert (len(result) == len(test_explores_stats))
assert all(((r['Model'] == test_model['name']) for r in result))
test_explore_names = [e['name'] for e in test_explores_stats]
assert all(((r['Explore'] in test_explore_names) for r in result))
test_explore = test_explores_stats[0]
result = vacuum.explores(model=test_model['name'], explore=test_explore['name'])
assert isinstance(result, list)
assert (len(result) == 1)
result = result[0]
assert (result['Model'] == test_model['name'])
assert (result['Explore'] == test_explore['name'])
assert (result['Unused Joins'] == test_explore['unused_joins'])
assert (result['Unused Fields'] == test_explore['unused_fields'])<|docstring|>vacuum.explores() should be able to filter models and explores.<|endoftext|> |
06994d0c0a053422110f111960b9e1ca9685bb73fdc4b12c438ac953cefc7167 | @pytest.mark.parametrize('test_explore', ['explore_2_joins_all_used', 'explore_2_joins_1_used', 'unused_explore_2_joins', 'unused_explore_no_joins'])
def test_vacuum_explores_vacuums(vacuum: vacuum.Vacuum, test_model, test_explore, test_explores_stats):
'vacuum.explores() should return the unused joins and fields for a given\n explore.\n '
result = vacuum.explores(model=test_model['name'], explore=test_explore)
assert isinstance(result, list)
assert (len(result) == 1)
result = result[0]
test_explore_stats = list(filter((lambda e: (e['name'] == test_explore)), test_explores_stats))[0]
assert (result['Model'] == test_model['name'])
assert (result['Explore'] == test_explore)
assert (result['Unused Joins'] == '\n'.join(test_explore_stats['unused_joins']))
assert (result['Unused Fields'] == '\n'.join(test_explore_stats['unused_fields'])) | vacuum.explores() should return the unused joins and fields for a given
explore. | tests/test_vacuum.py | test_vacuum_explores_vacuums | iamaziz/henry | 42 | python | @pytest.mark.parametrize('test_explore', ['explore_2_joins_all_used', 'explore_2_joins_1_used', 'unused_explore_2_joins', 'unused_explore_no_joins'])
def test_vacuum_explores_vacuums(vacuum: vacuum.Vacuum, test_model, test_explore, test_explores_stats):
'vacuum.explores() should return the unused joins and fields for a given\n explore.\n '
result = vacuum.explores(model=test_model['name'], explore=test_explore)
assert isinstance(result, list)
assert (len(result) == 1)
result = result[0]
test_explore_stats = list(filter((lambda e: (e['name'] == test_explore)), test_explores_stats))[0]
assert (result['Model'] == test_model['name'])
assert (result['Explore'] == test_explore)
assert (result['Unused Joins'] == '\n'.join(test_explore_stats['unused_joins']))
assert (result['Unused Fields'] == '\n'.join(test_explore_stats['unused_fields'])) | @pytest.mark.parametrize('test_explore', ['explore_2_joins_all_used', 'explore_2_joins_1_used', 'unused_explore_2_joins', 'unused_explore_no_joins'])
def test_vacuum_explores_vacuums(vacuum: vacuum.Vacuum, test_model, test_explore, test_explores_stats):
'vacuum.explores() should return the unused joins and fields for a given\n explore.\n '
result = vacuum.explores(model=test_model['name'], explore=test_explore)
assert isinstance(result, list)
assert (len(result) == 1)
result = result[0]
test_explore_stats = list(filter((lambda e: (e['name'] == test_explore)), test_explores_stats))[0]
assert (result['Model'] == test_model['name'])
assert (result['Explore'] == test_explore)
assert (result['Unused Joins'] == '\n'.join(test_explore_stats['unused_joins']))
assert (result['Unused Fields'] == '\n'.join(test_explore_stats['unused_fields']))<|docstring|>vacuum.explores() should return the unused joins and fields for a given
explore.<|endoftext|> |
09241af2747d827cbac12ffde7ba8d3e3c9ea5faea0c209153bd109365ae3610 | @pytest.mark.parametrize('model, explore, msg', [('BadModel', None, 'error occured while getting models.'), ('BadModel', 'explore_2_joins_used', 'error occured while getting models/explores.'), ('BadModel', 'BadExplore', 'error occured while getting models/explores'), ('henry_qa', 'BadExplore', 'error occured while getting models/explores')])
def test_vacuum_explores_throws_for_bad_filters(vacuum: vacuum.Vacuum, model, explore, msg):
'vacuum.explores() should error for bad model/explore filter values.'
with pytest.raises(exceptions.NotFoundError) as exc:
vacuum.explores(model=model, explore=explore)
assert (msg in str(exc.value)) | vacuum.explores() should error for bad model/explore filter values. | tests/test_vacuum.py | test_vacuum_explores_throws_for_bad_filters | iamaziz/henry | 42 | python | @pytest.mark.parametrize('model, explore, msg', [('BadModel', None, 'error occured while getting models.'), ('BadModel', 'explore_2_joins_used', 'error occured while getting models/explores.'), ('BadModel', 'BadExplore', 'error occured while getting models/explores'), ('henry_qa', 'BadExplore', 'error occured while getting models/explores')])
def test_vacuum_explores_throws_for_bad_filters(vacuum: vacuum.Vacuum, model, explore, msg):
with pytest.raises(exceptions.NotFoundError) as exc:
vacuum.explores(model=model, explore=explore)
assert (msg in str(exc.value)) | @pytest.mark.parametrize('model, explore, msg', [('BadModel', None, 'error occured while getting models.'), ('BadModel', 'explore_2_joins_used', 'error occured while getting models/explores.'), ('BadModel', 'BadExplore', 'error occured while getting models/explores'), ('henry_qa', 'BadExplore', 'error occured while getting models/explores')])
def test_vacuum_explores_throws_for_bad_filters(vacuum: vacuum.Vacuum, model, explore, msg):
with pytest.raises(exceptions.NotFoundError) as exc:
vacuum.explores(model=model, explore=explore)
assert (msg in str(exc.value))<|docstring|>vacuum.explores() should error for bad model/explore filter values.<|endoftext|> |
d65e98b664618585fe244086a439c020d436f21261e55339013b05a1fcd9e238 | def sort_items(self):
'Organises each item according to category'
for item in self.items:
if ('Aged Brie' in item.name):
self.aged_brie.append(item)
self.organised_items['Aged Brie'] = self.aged_brie
elif ('Sulfuras' in item.name):
self.sulfuras.append(item)
self.organised_items['Sulfuras'] = self.sulfuras
elif ('Backstage' in item.name):
self.backstage.append(item)
self.organised_items['Backstage'] = self.backstage
elif ('Conjured' in item.name):
self.conjured.append(item)
self.organised_items['Conjured'] = self.conjured
elif (('Aged Brie' not in item.name) and ('Sulfuras' not in item.name) and ('Backstage' not in item.name) and ('Conjured' not in item.name)):
self.general_items.append(item)
self.organised_items['General Items'] = self.general_items
return self.organised_items | Organises each item according to category | python/gilded_rose.py | sort_items | Mayo-Theodore/GildedRose-Refactoring-Kata | 0 | python | def sort_items(self):
for item in self.items:
if ('Aged Brie' in item.name):
self.aged_brie.append(item)
self.organised_items['Aged Brie'] = self.aged_brie
elif ('Sulfuras' in item.name):
self.sulfuras.append(item)
self.organised_items['Sulfuras'] = self.sulfuras
elif ('Backstage' in item.name):
self.backstage.append(item)
self.organised_items['Backstage'] = self.backstage
elif ('Conjured' in item.name):
self.conjured.append(item)
self.organised_items['Conjured'] = self.conjured
elif (('Aged Brie' not in item.name) and ('Sulfuras' not in item.name) and ('Backstage' not in item.name) and ('Conjured' not in item.name)):
self.general_items.append(item)
self.organised_items['General Items'] = self.general_items
return self.organised_items | def sort_items(self):
for item in self.items:
if ('Aged Brie' in item.name):
self.aged_brie.append(item)
self.organised_items['Aged Brie'] = self.aged_brie
elif ('Sulfuras' in item.name):
self.sulfuras.append(item)
self.organised_items['Sulfuras'] = self.sulfuras
elif ('Backstage' in item.name):
self.backstage.append(item)
self.organised_items['Backstage'] = self.backstage
elif ('Conjured' in item.name):
self.conjured.append(item)
self.organised_items['Conjured'] = self.conjured
elif (('Aged Brie' not in item.name) and ('Sulfuras' not in item.name) and ('Backstage' not in item.name) and ('Conjured' not in item.name)):
self.general_items.append(item)
self.organised_items['General Items'] = self.general_items
return self.organised_items<|docstring|>Organises each item according to category<|endoftext|> |
89e49207fac292a50d8f506ef9249a8cd4c8b965aad6bd7a7d8408395def8d72 | def list_items(self):
'Displays available items in gilded rose'
return self.organised_items | Displays available items in gilded rose | python/gilded_rose.py | list_items | Mayo-Theodore/GildedRose-Refactoring-Kata | 0 | python | def list_items(self):
return self.organised_items | def list_items(self):
return self.organised_items<|docstring|>Displays available items in gilded rose<|endoftext|> |
7cf4a70407951379fd9407723d1b6a80884e54d67f5ece9d2461b067bdb56a7c | def update_sell_in(self):
'Update sell in value for all items'
for (key, values) in self.organised_items.items():
for item in values:
if ('Sulfuras' in item.name):
item.sell_in = None
else:
item.sell_in -= 1
return self.organised_items | Update sell in value for all items | python/gilded_rose.py | update_sell_in | Mayo-Theodore/GildedRose-Refactoring-Kata | 0 | python | def update_sell_in(self):
for (key, values) in self.organised_items.items():
for item in values:
if ('Sulfuras' in item.name):
item.sell_in = None
else:
item.sell_in -= 1
return self.organised_items | def update_sell_in(self):
for (key, values) in self.organised_items.items():
for item in values:
if ('Sulfuras' in item.name):
item.sell_in = None
else:
item.sell_in -= 1
return self.organised_items<|docstring|>Update sell in value for all items<|endoftext|> |
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