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sonyxperiadev/pygerrit
|
pygerrit/stream.py
|
GerritStream.run
|
def run(self):
""" Listen to the stream and send events to the client. """
channel = self._ssh_client.get_transport().open_session()
self._channel = channel
channel.exec_command("gerrit stream-events")
stdout = channel.makefile()
stderr = channel.makefile_stderr()
while not self._stop.is_set():
try:
if channel.exit_status_ready():
if channel.recv_stderr_ready():
error = stderr.readline().strip()
else:
error = "Remote server connection closed"
self._error_event(error)
self._stop.set()
else:
data = stdout.readline()
self._gerrit.put_event(data)
except Exception as e: # pylint: disable=W0703
self._error_event(repr(e))
self._stop.set()
|
python
|
def run(self):
""" Listen to the stream and send events to the client. """
channel = self._ssh_client.get_transport().open_session()
self._channel = channel
channel.exec_command("gerrit stream-events")
stdout = channel.makefile()
stderr = channel.makefile_stderr()
while not self._stop.is_set():
try:
if channel.exit_status_ready():
if channel.recv_stderr_ready():
error = stderr.readline().strip()
else:
error = "Remote server connection closed"
self._error_event(error)
self._stop.set()
else:
data = stdout.readline()
self._gerrit.put_event(data)
except Exception as e: # pylint: disable=W0703
self._error_event(repr(e))
self._stop.set()
|
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"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/stream.py#L56-L77
|
sonyxperiadev/pygerrit
|
pygerrit/client.py
|
GerritClient.run_command
|
def run_command(self, command):
""" Run a command.
:arg str command: The command to run.
:Return: The result as a string.
:Raises: `ValueError` if `command` is not a string.
"""
if not isinstance(command, basestring):
raise ValueError("command must be a string")
return self._ssh_client.run_gerrit_command(command)
|
python
|
def run_command(self, command):
""" Run a command.
:arg str command: The command to run.
:Return: The result as a string.
:Raises: `ValueError` if `command` is not a string.
"""
if not isinstance(command, basestring):
raise ValueError("command must be a string")
return self._ssh_client.run_gerrit_command(command)
|
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"_ssh_client",
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"(",
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")"
] |
Run a command.
:arg str command: The command to run.
:Return: The result as a string.
:Raises: `ValueError` if `command` is not a string.
|
[
"Run",
"a",
"command",
"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/client.py#L79-L91
|
sonyxperiadev/pygerrit
|
pygerrit/client.py
|
GerritClient.query
|
def query(self, term):
""" Run a query.
:arg str term: The query term to run.
:Returns: A list of results as :class:`pygerrit.models.Change` objects.
:Raises: `ValueError` if `term` is not a string.
"""
results = []
command = ["query", "--current-patch-set", "--all-approvals",
"--format JSON", "--commit-message"]
if not isinstance(term, basestring):
raise ValueError("term must be a string")
command.append(escape_string(term))
result = self._ssh_client.run_gerrit_command(" ".join(command))
decoder = JSONDecoder()
for line in result.stdout.read().splitlines():
# Gerrit's response to the query command contains one or more
# lines of JSON-encoded strings. The last one is a status
# dictionary containing the key "type" whose value indicates
# whether or not the operation was successful.
# According to http://goo.gl/h13HD it should be safe to use the
# presence of the "type" key to determine whether the dictionary
# represents a change or if it's the query status indicator.
try:
data = decoder.decode(line)
except ValueError as err:
raise GerritError("Query returned invalid data: %s", err)
if "type" in data and data["type"] == "error":
raise GerritError("Query error: %s" % data["message"])
elif "project" in data:
results.append(Change(data))
return results
|
python
|
def query(self, term):
""" Run a query.
:arg str term: The query term to run.
:Returns: A list of results as :class:`pygerrit.models.Change` objects.
:Raises: `ValueError` if `term` is not a string.
"""
results = []
command = ["query", "--current-patch-set", "--all-approvals",
"--format JSON", "--commit-message"]
if not isinstance(term, basestring):
raise ValueError("term must be a string")
command.append(escape_string(term))
result = self._ssh_client.run_gerrit_command(" ".join(command))
decoder = JSONDecoder()
for line in result.stdout.read().splitlines():
# Gerrit's response to the query command contains one or more
# lines of JSON-encoded strings. The last one is a status
# dictionary containing the key "type" whose value indicates
# whether or not the operation was successful.
# According to http://goo.gl/h13HD it should be safe to use the
# presence of the "type" key to determine whether the dictionary
# represents a change or if it's the query status indicator.
try:
data = decoder.decode(line)
except ValueError as err:
raise GerritError("Query returned invalid data: %s", err)
if "type" in data and data["type"] == "error":
raise GerritError("Query error: %s" % data["message"])
elif "project" in data:
results.append(Change(data))
return results
|
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Run a query.
:arg str term: The query term to run.
:Returns: A list of results as :class:`pygerrit.models.Change` objects.
:Raises: `ValueError` if `term` is not a string.
|
[
"Run",
"a",
"query",
"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/client.py#L93-L129
|
sonyxperiadev/pygerrit
|
pygerrit/client.py
|
GerritClient.start_event_stream
|
def start_event_stream(self):
""" Start streaming events from `gerrit stream-events`. """
if not self._stream:
self._stream = GerritStream(self, ssh_client=self._ssh_client)
self._stream.start()
|
python
|
def start_event_stream(self):
""" Start streaming events from `gerrit stream-events`. """
if not self._stream:
self._stream = GerritStream(self, ssh_client=self._ssh_client)
self._stream.start()
|
[
"def",
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"self",
")",
":",
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"=",
"self",
".",
"_ssh_client",
")",
"self",
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"_stream",
".",
"start",
"(",
")"
] |
Start streaming events from `gerrit stream-events`.
|
[
"Start",
"streaming",
"events",
"from",
"gerrit",
"stream",
"-",
"events",
"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/client.py#L131-L135
|
sonyxperiadev/pygerrit
|
pygerrit/client.py
|
GerritClient.stop_event_stream
|
def stop_event_stream(self):
""" Stop streaming events from `gerrit stream-events`."""
if self._stream:
self._stream.stop()
self._stream.join()
self._stream = None
with self._events.mutex:
self._events.queue.clear()
|
python
|
def stop_event_stream(self):
""" Stop streaming events from `gerrit stream-events`."""
if self._stream:
self._stream.stop()
self._stream.join()
self._stream = None
with self._events.mutex:
self._events.queue.clear()
|
[
"def",
"stop_event_stream",
"(",
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")",
":",
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"self",
".",
"_stream",
":",
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Stop streaming events from `gerrit stream-events`.
|
[
"Stop",
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"events",
"from",
"gerrit",
"stream",
"-",
"events",
"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/client.py#L137-L144
|
sonyxperiadev/pygerrit
|
pygerrit/client.py
|
GerritClient.get_event
|
def get_event(self, block=True, timeout=None):
""" Get the next event from the queue.
:arg boolean block: Set to True to block if no event is available.
:arg seconds timeout: Timeout to wait if no event is available.
:Returns: The next event as a :class:`pygerrit.events.GerritEvent`
instance, or `None` if:
- `block` is False and there is no event available in the queue, or
- `block` is True and no event is available within the time
specified by `timeout`.
"""
try:
return self._events.get(block, timeout)
except Empty:
return None
|
python
|
def get_event(self, block=True, timeout=None):
""" Get the next event from the queue.
:arg boolean block: Set to True to block if no event is available.
:arg seconds timeout: Timeout to wait if no event is available.
:Returns: The next event as a :class:`pygerrit.events.GerritEvent`
instance, or `None` if:
- `block` is False and there is no event available in the queue, or
- `block` is True and no event is available within the time
specified by `timeout`.
"""
try:
return self._events.get(block, timeout)
except Empty:
return None
|
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] |
Get the next event from the queue.
:arg boolean block: Set to True to block if no event is available.
:arg seconds timeout: Timeout to wait if no event is available.
:Returns: The next event as a :class:`pygerrit.events.GerritEvent`
instance, or `None` if:
- `block` is False and there is no event available in the queue, or
- `block` is True and no event is available within the time
specified by `timeout`.
|
[
"Get",
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"next",
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"from",
"the",
"queue",
"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/client.py#L146-L162
|
sonyxperiadev/pygerrit
|
pygerrit/client.py
|
GerritClient.put_event
|
def put_event(self, data):
""" Create event from `data` and add it to the queue.
:arg json data: The JSON data from which to create the event.
:Raises: :class:`pygerrit.error.GerritError` if the queue is full, or
the factory could not create the event.
"""
try:
event = self._factory.create(data)
self._events.put(event)
except Full:
raise GerritError("Unable to add event: queue is full")
|
python
|
def put_event(self, data):
""" Create event from `data` and add it to the queue.
:arg json data: The JSON data from which to create the event.
:Raises: :class:`pygerrit.error.GerritError` if the queue is full, or
the factory could not create the event.
"""
try:
event = self._factory.create(data)
self._events.put(event)
except Full:
raise GerritError("Unable to add event: queue is full")
|
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] |
Create event from `data` and add it to the queue.
:arg json data: The JSON data from which to create the event.
:Raises: :class:`pygerrit.error.GerritError` if the queue is full, or
the factory could not create the event.
|
[
"Create",
"event",
"from",
"data",
"and",
"add",
"it",
"to",
"the",
"queue",
"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/client.py#L164-L177
|
sonyxperiadev/pygerrit
|
pygerrit/ssh.py
|
_extract_version
|
def _extract_version(version_string, pattern):
""" Extract the version from `version_string` using `pattern`.
Return the version as a string, with leading/trailing whitespace
stripped.
"""
if version_string:
match = pattern.match(version_string.strip())
if match:
return match.group(1)
return ""
|
python
|
def _extract_version(version_string, pattern):
""" Extract the version from `version_string` using `pattern`.
Return the version as a string, with leading/trailing whitespace
stripped.
"""
if version_string:
match = pattern.match(version_string.strip())
if match:
return match.group(1)
return ""
|
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")",
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] |
Extract the version from `version_string` using `pattern`.
Return the version as a string, with leading/trailing whitespace
stripped.
|
[
"Extract",
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"from",
"version_string",
"using",
"pattern",
"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/ssh.py#L36-L47
|
sonyxperiadev/pygerrit
|
pygerrit/ssh.py
|
GerritSSHClient._configure
|
def _configure(self):
""" Configure the ssh parameters from the config file. """
configfile = expanduser("~/.ssh/config")
if not isfile(configfile):
raise GerritError("ssh config file '%s' does not exist" %
configfile)
config = SSHConfig()
config.parse(open(configfile))
data = config.lookup(self.hostname)
if not data:
raise GerritError("No ssh config for host %s" % self.hostname)
if 'hostname' not in data or 'port' not in data or 'user' not in data:
raise GerritError("Missing configuration data in %s" % configfile)
self.hostname = data['hostname']
self.username = data['user']
if 'identityfile' in data:
key_filename = abspath(expanduser(data['identityfile'][0]))
if not isfile(key_filename):
raise GerritError("Identity file '%s' does not exist" %
key_filename)
self.key_filename = key_filename
try:
self.port = int(data['port'])
except ValueError:
raise GerritError("Invalid port: %s" % data['port'])
if 'proxycommand' in data:
self.proxy = ProxyCommand(data['proxycommand'])
|
python
|
def _configure(self):
""" Configure the ssh parameters from the config file. """
configfile = expanduser("~/.ssh/config")
if not isfile(configfile):
raise GerritError("ssh config file '%s' does not exist" %
configfile)
config = SSHConfig()
config.parse(open(configfile))
data = config.lookup(self.hostname)
if not data:
raise GerritError("No ssh config for host %s" % self.hostname)
if 'hostname' not in data or 'port' not in data or 'user' not in data:
raise GerritError("Missing configuration data in %s" % configfile)
self.hostname = data['hostname']
self.username = data['user']
if 'identityfile' in data:
key_filename = abspath(expanduser(data['identityfile'][0]))
if not isfile(key_filename):
raise GerritError("Identity file '%s' does not exist" %
key_filename)
self.key_filename = key_filename
try:
self.port = int(data['port'])
except ValueError:
raise GerritError("Invalid port: %s" % data['port'])
if 'proxycommand' in data:
self.proxy = ProxyCommand(data['proxycommand'])
|
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|
[
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train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/ssh.py#L84-L111
|
sonyxperiadev/pygerrit
|
pygerrit/ssh.py
|
GerritSSHClient._do_connect
|
def _do_connect(self):
""" Connect to the remote. """
self.load_system_host_keys()
if self.username is None or self.port is None:
self._configure()
try:
self.connect(hostname=self.hostname,
port=self.port,
username=self.username,
key_filename=self.key_filename,
sock=self.proxy)
except socket.error as e:
raise GerritError("Failed to connect to server: %s" % e)
try:
version_string = self._transport.remote_version
pattern = re.compile(r'^.*GerritCodeReview_([a-z0-9-\.]*) .*$')
self.remote_version = _extract_version(version_string, pattern)
except AttributeError:
self.remote_version = None
|
python
|
def _do_connect(self):
""" Connect to the remote. """
self.load_system_host_keys()
if self.username is None or self.port is None:
self._configure()
try:
self.connect(hostname=self.hostname,
port=self.port,
username=self.username,
key_filename=self.key_filename,
sock=self.proxy)
except socket.error as e:
raise GerritError("Failed to connect to server: %s" % e)
try:
version_string = self._transport.remote_version
pattern = re.compile(r'^.*GerritCodeReview_([a-z0-9-\.]*) .*$')
self.remote_version = _extract_version(version_string, pattern)
except AttributeError:
self.remote_version = None
|
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Connect to the remote.
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[
"Connect",
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"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/ssh.py#L113-L132
|
sonyxperiadev/pygerrit
|
pygerrit/ssh.py
|
GerritSSHClient._connect
|
def _connect(self):
""" Connect to the remote if not already connected. """
if not self.connected.is_set():
try:
self.lock.acquire()
# Another thread may have connected while we were
# waiting to acquire the lock
if not self.connected.is_set():
self._do_connect()
if self.keepalive:
self._transport.set_keepalive(self.keepalive)
self.connected.set()
except GerritError:
raise
finally:
self.lock.release()
|
python
|
def _connect(self):
""" Connect to the remote if not already connected. """
if not self.connected.is_set():
try:
self.lock.acquire()
# Another thread may have connected while we were
# waiting to acquire the lock
if not self.connected.is_set():
self._do_connect()
if self.keepalive:
self._transport.set_keepalive(self.keepalive)
self.connected.set()
except GerritError:
raise
finally:
self.lock.release()
|
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Connect to the remote if not already connected.
|
[
"Connect",
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"if",
"not",
"already",
"connected",
"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/ssh.py#L134-L149
|
sonyxperiadev/pygerrit
|
pygerrit/ssh.py
|
GerritSSHClient.get_remote_version
|
def get_remote_version(self):
""" Return the version of the remote Gerrit server. """
if self.remote_version is None:
result = self.run_gerrit_command("version")
version_string = result.stdout.read()
pattern = re.compile(r'^gerrit version (.*)$')
self.remote_version = _extract_version(version_string, pattern)
return self.remote_version
|
python
|
def get_remote_version(self):
""" Return the version of the remote Gerrit server. """
if self.remote_version is None:
result = self.run_gerrit_command("version")
version_string = result.stdout.read()
pattern = re.compile(r'^gerrit version (.*)$')
self.remote_version = _extract_version(version_string, pattern)
return self.remote_version
|
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Return the version of the remote Gerrit server.
|
[
"Return",
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"remote",
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"server",
"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/ssh.py#L151-L158
|
sonyxperiadev/pygerrit
|
pygerrit/ssh.py
|
GerritSSHClient.run_gerrit_command
|
def run_gerrit_command(self, command):
""" Run the given command.
Make sure we're connected to the remote server, and run `command`.
Return the results as a `GerritSSHCommandResult`.
Raise `ValueError` if `command` is not a string, or `GerritError` if
command execution fails.
"""
if not isinstance(command, basestring):
raise ValueError("command must be a string")
gerrit_command = "gerrit " + command
# are we sending non-ascii data?
try:
gerrit_command.encode('ascii')
except UnicodeEncodeError:
gerrit_command = gerrit_command.encode('utf-8')
self._connect()
try:
stdin, stdout, stderr = self.exec_command(gerrit_command,
bufsize=1,
timeout=None,
get_pty=False)
except SSHException as err:
raise GerritError("Command execution error: %s" % err)
return GerritSSHCommandResult(command, stdin, stdout, stderr)
|
python
|
def run_gerrit_command(self, command):
""" Run the given command.
Make sure we're connected to the remote server, and run `command`.
Return the results as a `GerritSSHCommandResult`.
Raise `ValueError` if `command` is not a string, or `GerritError` if
command execution fails.
"""
if not isinstance(command, basestring):
raise ValueError("command must be a string")
gerrit_command = "gerrit " + command
# are we sending non-ascii data?
try:
gerrit_command.encode('ascii')
except UnicodeEncodeError:
gerrit_command = gerrit_command.encode('utf-8')
self._connect()
try:
stdin, stdout, stderr = self.exec_command(gerrit_command,
bufsize=1,
timeout=None,
get_pty=False)
except SSHException as err:
raise GerritError("Command execution error: %s" % err)
return GerritSSHCommandResult(command, stdin, stdout, stderr)
|
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Run the given command.
Make sure we're connected to the remote server, and run `command`.
Return the results as a `GerritSSHCommandResult`.
Raise `ValueError` if `command` is not a string, or `GerritError` if
command execution fails.
|
[
"Run",
"the",
"given",
"command",
"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/ssh.py#L165-L194
|
sonyxperiadev/pygerrit
|
pygerrit/events.py
|
GerritEventFactory.register
|
def register(cls, name):
""" Decorator to register the event identified by `name`.
Return the decorated class.
Raise GerritError if the event is already registered.
"""
def decorate(klazz):
""" Decorator. """
if name in cls._events:
raise GerritError("Duplicate event: %s" % name)
cls._events[name] = [klazz.__module__, klazz.__name__]
klazz.name = name
return klazz
return decorate
|
python
|
def register(cls, name):
""" Decorator to register the event identified by `name`.
Return the decorated class.
Raise GerritError if the event is already registered.
"""
def decorate(klazz):
""" Decorator. """
if name in cls._events:
raise GerritError("Duplicate event: %s" % name)
cls._events[name] = [klazz.__module__, klazz.__name__]
klazz.name = name
return klazz
return decorate
|
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Decorator to register the event identified by `name`.
Return the decorated class.
Raise GerritError if the event is already registered.
|
[
"Decorator",
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"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/events.py#L40-L56
|
sonyxperiadev/pygerrit
|
pygerrit/events.py
|
GerritEventFactory.create
|
def create(cls, data):
""" Create a new event instance.
Return an instance of the `GerritEvent` subclass after converting
`data` to json.
Raise GerritError if json parsed from `data` does not contain a `type`
key.
"""
try:
json_data = json.loads(data)
except ValueError as err:
logging.debug("Failed to load json data: %s: [%s]", str(err), data)
json_data = json.loads(ErrorEvent.error_json(err))
if "type" not in json_data:
raise GerritError("`type` not in json_data")
name = json_data["type"]
if name not in cls._events:
name = 'unhandled-event'
event = cls._events[name]
module_name = event[0]
class_name = event[1]
module = __import__(module_name, fromlist=[module_name])
klazz = getattr(module, class_name)
return klazz(json_data)
|
python
|
def create(cls, data):
""" Create a new event instance.
Return an instance of the `GerritEvent` subclass after converting
`data` to json.
Raise GerritError if json parsed from `data` does not contain a `type`
key.
"""
try:
json_data = json.loads(data)
except ValueError as err:
logging.debug("Failed to load json data: %s: [%s]", str(err), data)
json_data = json.loads(ErrorEvent.error_json(err))
if "type" not in json_data:
raise GerritError("`type` not in json_data")
name = json_data["type"]
if name not in cls._events:
name = 'unhandled-event'
event = cls._events[name]
module_name = event[0]
class_name = event[1]
module = __import__(module_name, fromlist=[module_name])
klazz = getattr(module, class_name)
return klazz(json_data)
|
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Raise GerritError if json parsed from `data` does not contain a `type`
key.
|
[
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"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/events.py#L59-L85
|
sonyxperiadev/pygerrit
|
pygerrit/rest/__init__.py
|
_decode_response
|
def _decode_response(response):
""" Strip off Gerrit's magic prefix and decode a response.
:returns:
Decoded JSON content as a dict, or raw text if content could not be
decoded as JSON.
:raises:
requests.HTTPError if the response contains an HTTP error status code.
"""
content = response.content.strip()
logging.debug(content[:512])
response.raise_for_status()
if content.startswith(GERRIT_MAGIC_JSON_PREFIX):
content = content[len(GERRIT_MAGIC_JSON_PREFIX):]
try:
return json.loads(content)
except ValueError:
logging.error('Invalid json content: %s' % content)
raise
|
python
|
def _decode_response(response):
""" Strip off Gerrit's magic prefix and decode a response.
:returns:
Decoded JSON content as a dict, or raw text if content could not be
decoded as JSON.
:raises:
requests.HTTPError if the response contains an HTTP error status code.
"""
content = response.content.strip()
logging.debug(content[:512])
response.raise_for_status()
if content.startswith(GERRIT_MAGIC_JSON_PREFIX):
content = content[len(GERRIT_MAGIC_JSON_PREFIX):]
try:
return json.loads(content)
except ValueError:
logging.error('Invalid json content: %s' % content)
raise
|
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Strip off Gerrit's magic prefix and decode a response.
:returns:
Decoded JSON content as a dict, or raw text if content could not be
decoded as JSON.
:raises:
requests.HTTPError if the response contains an HTTP error status code.
|
[
"Strip",
"off",
"Gerrit",
"s",
"magic",
"prefix",
"and",
"decode",
"a",
"response",
"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/rest/__init__.py#L33-L53
|
sonyxperiadev/pygerrit
|
pygerrit/rest/__init__.py
|
GerritRestAPI.put
|
def put(self, endpoint, **kwargs):
""" Send HTTP PUT to the endpoint.
:arg str endpoint: The endpoint to send to.
:returns:
JSON decoded result.
:raises:
requests.RequestException on timeout or connection error.
"""
kwargs.update(self.kwargs.copy())
if "data" in kwargs:
kwargs["headers"].update(
{"Content-Type": "application/json;charset=UTF-8"})
response = requests.put(self.make_url(endpoint), **kwargs)
return _decode_response(response)
|
python
|
def put(self, endpoint, **kwargs):
""" Send HTTP PUT to the endpoint.
:arg str endpoint: The endpoint to send to.
:returns:
JSON decoded result.
:raises:
requests.RequestException on timeout or connection error.
"""
kwargs.update(self.kwargs.copy())
if "data" in kwargs:
kwargs["headers"].update(
{"Content-Type": "application/json;charset=UTF-8"})
response = requests.put(self.make_url(endpoint), **kwargs)
return _decode_response(response)
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JSON decoded result.
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requests.RequestException on timeout or connection error.
|
[
"Send",
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] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/rest/__init__.py#L121-L138
|
sonyxperiadev/pygerrit
|
pygerrit/rest/__init__.py
|
GerritRestAPI.delete
|
def delete(self, endpoint, **kwargs):
""" Send HTTP DELETE to the endpoint.
:arg str endpoint: The endpoint to send to.
:returns:
JSON decoded result.
:raises:
requests.RequestException on timeout or connection error.
"""
kwargs.update(self.kwargs.copy())
response = requests.delete(self.make_url(endpoint), **kwargs)
return _decode_response(response)
|
python
|
def delete(self, endpoint, **kwargs):
""" Send HTTP DELETE to the endpoint.
:arg str endpoint: The endpoint to send to.
:returns:
JSON decoded result.
:raises:
requests.RequestException on timeout or connection error.
"""
kwargs.update(self.kwargs.copy())
response = requests.delete(self.make_url(endpoint), **kwargs)
return _decode_response(response)
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Send HTTP DELETE to the endpoint.
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JSON decoded result.
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requests.RequestException on timeout or connection error.
|
[
"Send",
"HTTP",
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"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/rest/__init__.py#L159-L173
|
sonyxperiadev/pygerrit
|
pygerrit/rest/__init__.py
|
GerritRestAPI.review
|
def review(self, change_id, revision, review):
""" Submit a review.
:arg str change_id: The change ID.
:arg str revision: The revision.
:arg str review: The review details as a :class:`GerritReview`.
:returns:
JSON decoded result.
:raises:
requests.RequestException on timeout or connection error.
"""
endpoint = "changes/%s/revisions/%s/review" % (change_id, revision)
self.post(endpoint, data=str(review))
|
python
|
def review(self, change_id, revision, review):
""" Submit a review.
:arg str change_id: The change ID.
:arg str revision: The revision.
:arg str review: The review details as a :class:`GerritReview`.
:returns:
JSON decoded result.
:raises:
requests.RequestException on timeout or connection error.
"""
endpoint = "changes/%s/revisions/%s/review" % (change_id, revision)
self.post(endpoint, data=str(review))
|
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Submit a review.
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:arg str review: The review details as a :class:`GerritReview`.
:returns:
JSON decoded result.
:raises:
requests.RequestException on timeout or connection error.
|
[
"Submit",
"a",
"review",
"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/rest/__init__.py#L175-L191
|
sonyxperiadev/pygerrit
|
pygerrit/rest/__init__.py
|
GerritReview.add_comments
|
def add_comments(self, comments):
""" Add inline comments.
:arg dict comments: Comments to add.
Usage::
add_comments([{'filename': 'Makefile',
'line': 10,
'message': 'inline message'}])
add_comments([{'filename': 'Makefile',
'range': {'start_line': 0,
'start_character': 1,
'end_line': 0,
'end_character': 5},
'message': 'inline message'}])
"""
for comment in comments:
if 'filename' and 'message' in comment.keys():
msg = {}
if 'range' in comment.keys():
msg = {"range": comment['range'],
"message": comment['message']}
elif 'line' in comment.keys():
msg = {"line": comment['line'],
"message": comment['message']}
else:
continue
file_comment = {comment['filename']: [msg]}
if self.comments:
if comment['filename'] in self.comments.keys():
self.comments[comment['filename']].append(msg)
else:
self.comments.update(file_comment)
else:
self.comments.update(file_comment)
|
python
|
def add_comments(self, comments):
""" Add inline comments.
:arg dict comments: Comments to add.
Usage::
add_comments([{'filename': 'Makefile',
'line': 10,
'message': 'inline message'}])
add_comments([{'filename': 'Makefile',
'range': {'start_line': 0,
'start_character': 1,
'end_line': 0,
'end_character': 5},
'message': 'inline message'}])
"""
for comment in comments:
if 'filename' and 'message' in comment.keys():
msg = {}
if 'range' in comment.keys():
msg = {"range": comment['range'],
"message": comment['message']}
elif 'line' in comment.keys():
msg = {"line": comment['line'],
"message": comment['message']}
else:
continue
file_comment = {comment['filename']: [msg]}
if self.comments:
if comment['filename'] in self.comments.keys():
self.comments[comment['filename']].append(msg)
else:
self.comments.update(file_comment)
else:
self.comments.update(file_comment)
|
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Add inline comments.
:arg dict comments: Comments to add.
Usage::
add_comments([{'filename': 'Makefile',
'line': 10,
'message': 'inline message'}])
add_comments([{'filename': 'Makefile',
'range': {'start_line': 0,
'start_character': 1,
'end_line': 0,
'end_character': 5},
'message': 'inline message'}])
|
[
"Add",
"inline",
"comments",
"."
] |
train
|
https://github.com/sonyxperiadev/pygerrit/blob/756300120b0f4f4af19e0f985566d82bc80b4359/pygerrit/rest/__init__.py#L241-L278
|
SiriDB/siridb-connector
|
siridb/connector/lib/protocol.py
|
_SiriDBProtocol.connection_made
|
def connection_made(self, transport):
'''
override asyncio.Protocol
'''
self._connected = True
self.transport = transport
self.remote_ip, self.port = transport.get_extra_info('peername')[:2]
logging.debug(
'Connection made (address: {} port: {})'
.format(self.remote_ip, self.port))
self.auth_future = self.send_package(protomap.CPROTO_REQ_AUTH,
data=(self._username,
self._password,
self._dbname),
timeout=10)
self._password = None
self.on_connection_made()
|
python
|
def connection_made(self, transport):
'''
override asyncio.Protocol
'''
self._connected = True
self.transport = transport
self.remote_ip, self.port = transport.get_extra_info('peername')[:2]
logging.debug(
'Connection made (address: {} port: {})'
.format(self.remote_ip, self.port))
self.auth_future = self.send_package(protomap.CPROTO_REQ_AUTH,
data=(self._username,
self._password,
self._dbname),
timeout=10)
self._password = None
self.on_connection_made()
|
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"(",
")"
] |
override asyncio.Protocol
|
[
"override",
"asyncio",
".",
"Protocol"
] |
train
|
https://github.com/SiriDB/siridb-connector/blob/dff33c183899c3ee21c3eab6b90cf4668afef1b0/siridb/connector/lib/protocol.py#L85-L106
|
SiriDB/siridb-connector
|
siridb/connector/lib/protocol.py
|
_SiriDBProtocol.connection_lost
|
def connection_lost(self, exc):
'''
override asyncio.Protocol
'''
self._connected = False
logging.debug(
'Connection lost (address: {} port: {})'
.format(self.remote_ip, self.port))
for pid, (future, task) in self._requests.items():
task.cancel()
if future.cancelled():
continue
future.set_exception(ConnectionError(
'Connection is lost before we had an answer on package id: {}.'
.format(pid)))
self.on_connection_lost(exc)
|
python
|
def connection_lost(self, exc):
'''
override asyncio.Protocol
'''
self._connected = False
logging.debug(
'Connection lost (address: {} port: {})'
.format(self.remote_ip, self.port))
for pid, (future, task) in self._requests.items():
task.cancel()
if future.cancelled():
continue
future.set_exception(ConnectionError(
'Connection is lost before we had an answer on package id: {}.'
.format(pid)))
self.on_connection_lost(exc)
|
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] |
override asyncio.Protocol
|
[
"override",
"asyncio",
".",
"Protocol"
] |
train
|
https://github.com/SiriDB/siridb-connector/blob/dff33c183899c3ee21c3eab6b90cf4668afef1b0/siridb/connector/lib/protocol.py#L108-L126
|
SiriDB/siridb-connector
|
siridb/connector/lib/protocol.py
|
_SiriDBProtocol.data_received
|
def data_received(self, data):
'''
override asyncio.Protocol
'''
self._buffered_data.extend(data)
while self._buffered_data:
size = len(self._buffered_data)
if self._data_package is None:
if size < DataPackage.struct_datapackage.size:
return None
self._data_package = DataPackage(self._buffered_data)
if size < self._data_package.length:
return None
try:
self._data_package.extract_data_from(self._buffered_data)
except KeyError as e:
logging.error('Unsupported package received: {}'.format(e))
except Exception as e:
logging.exception(e)
# empty the byte-array to recover from this error
self._buffered_data.clear()
else:
self._on_package_received()
self._data_package = None
|
python
|
def data_received(self, data):
'''
override asyncio.Protocol
'''
self._buffered_data.extend(data)
while self._buffered_data:
size = len(self._buffered_data)
if self._data_package is None:
if size < DataPackage.struct_datapackage.size:
return None
self._data_package = DataPackage(self._buffered_data)
if size < self._data_package.length:
return None
try:
self._data_package.extract_data_from(self._buffered_data)
except KeyError as e:
logging.error('Unsupported package received: {}'.format(e))
except Exception as e:
logging.exception(e)
# empty the byte-array to recover from this error
self._buffered_data.clear()
else:
self._on_package_received()
self._data_package = None
|
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override asyncio.Protocol
|
[
"override",
"asyncio",
".",
"Protocol"
] |
train
|
https://github.com/SiriDB/siridb-connector/blob/dff33c183899c3ee21c3eab6b90cf4668afef1b0/siridb/connector/lib/protocol.py#L128-L151
|
SiriDB/siridb-connector
|
siridb/connector/lib/protocol.py
|
_SiriDBInfoProtocol.connection_made
|
def connection_made(self, transport):
'''
override _SiriDBProtocol
'''
self.transport = transport
self.remote_ip, self.port = transport.get_extra_info('peername')[:2]
logging.debug(
'Connection made (address: {} port: {})'
.format(self.remote_ip, self.port))
self.future = self.send_package(
protomap.CPROTO_REQ_INFO,
data=None,
timeout=10)
|
python
|
def connection_made(self, transport):
'''
override _SiriDBProtocol
'''
self.transport = transport
self.remote_ip, self.port = transport.get_extra_info('peername')[:2]
logging.debug(
'Connection made (address: {} port: {})'
.format(self.remote_ip, self.port))
self.future = self.send_package(
protomap.CPROTO_REQ_INFO,
data=None,
timeout=10)
|
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",",
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"None",
",",
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"10",
")"
] |
override _SiriDBProtocol
|
[
"override",
"_SiriDBProtocol"
] |
train
|
https://github.com/SiriDB/siridb-connector/blob/dff33c183899c3ee21c3eab6b90cf4668afef1b0/siridb/connector/lib/protocol.py#L234-L249
|
SiriDB/siridb-connector
|
siridb/connector/lib/connection.py
|
SiriDBConnection._register_server
|
def _register_server(self, server, timeout=30):
'''Register a new SiriDB Server.
This method is used by the SiriDB manage tool and should not be used
otherwise. Full access rights are required for this request.
'''
result = self._loop.run_until_complete(
self._protocol.send_package(CPROTO_REQ_REGISTER_SERVER,
data=server,
timeout=timeout))
return result
|
python
|
def _register_server(self, server, timeout=30):
'''Register a new SiriDB Server.
This method is used by the SiriDB manage tool and should not be used
otherwise. Full access rights are required for this request.
'''
result = self._loop.run_until_complete(
self._protocol.send_package(CPROTO_REQ_REGISTER_SERVER,
data=server,
timeout=timeout))
return result
|
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")",
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] |
Register a new SiriDB Server.
This method is used by the SiriDB manage tool and should not be used
otherwise. Full access rights are required for this request.
|
[
"Register",
"a",
"new",
"SiriDB",
"Server",
"."
] |
train
|
https://github.com/SiriDB/siridb-connector/blob/dff33c183899c3ee21c3eab6b90cf4668afef1b0/siridb/connector/lib/connection.py#L65-L75
|
SiriDB/siridb-connector
|
siridb/connector/lib/connection.py
|
SiriDBConnection._get_file
|
def _get_file(self, fn, timeout=30):
'''Request a SiriDB configuration file.
This method is used by the SiriDB manage tool and should not be used
otherwise. Full access rights are required for this request.
'''
msg = FILE_MAP.get(fn, None)
if msg is None:
raise FileNotFoundError('Cannot get file {!r}. Available file '
'requests are: {}'
.format(fn, ', '.join(FILE_MAP.keys())))
result = self._loop.run_until_complete(
self._protocol.send_package(msg, timeout=timeout))
return result
|
python
|
def _get_file(self, fn, timeout=30):
'''Request a SiriDB configuration file.
This method is used by the SiriDB manage tool and should not be used
otherwise. Full access rights are required for this request.
'''
msg = FILE_MAP.get(fn, None)
if msg is None:
raise FileNotFoundError('Cannot get file {!r}. Available file '
'requests are: {}'
.format(fn, ', '.join(FILE_MAP.keys())))
result = self._loop.run_until_complete(
self._protocol.send_package(msg, timeout=timeout))
return result
|
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Request a SiriDB configuration file.
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|
[
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train
|
https://github.com/SiriDB/siridb-connector/blob/dff33c183899c3ee21c3eab6b90cf4668afef1b0/siridb/connector/lib/connection.py#L77-L90
|
transitland/mapzen-geohash
|
mzgeohash/geohash.py
|
_bits_to_float
|
def _bits_to_float(bits, lower=-90.0, middle=0.0, upper=90.0):
"""Convert GeoHash bits to a float."""
for i in bits:
if i:
lower = middle
else:
upper = middle
middle = (upper + lower) / 2
return middle
|
python
|
def _bits_to_float(bits, lower=-90.0, middle=0.0, upper=90.0):
"""Convert GeoHash bits to a float."""
for i in bits:
if i:
lower = middle
else:
upper = middle
middle = (upper + lower) / 2
return middle
|
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Convert GeoHash bits to a float.
|
[
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train
|
https://github.com/transitland/mapzen-geohash/blob/5264d16dc2df00ebc4aedaa78ca8c6ee3fd655ad/mzgeohash/geohash.py#L8-L16
|
transitland/mapzen-geohash
|
mzgeohash/geohash.py
|
_float_to_bits
|
def _float_to_bits(value, lower=-90.0, middle=0.0, upper=90.0, length=15):
"""Convert a float to a list of GeoHash bits."""
ret = []
for i in range(length):
if value >= middle:
lower = middle
ret.append(1)
else:
upper = middle
ret.append(0)
middle = (upper + lower) / 2
return ret
|
python
|
def _float_to_bits(value, lower=-90.0, middle=0.0, upper=90.0, length=15):
"""Convert a float to a list of GeoHash bits."""
ret = []
for i in range(length):
if value >= middle:
lower = middle
ret.append(1)
else:
upper = middle
ret.append(0)
middle = (upper + lower) / 2
return ret
|
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|
[
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train
|
https://github.com/transitland/mapzen-geohash/blob/5264d16dc2df00ebc4aedaa78ca8c6ee3fd655ad/mzgeohash/geohash.py#L18-L29
|
transitland/mapzen-geohash
|
mzgeohash/geohash.py
|
_geohash_to_bits
|
def _geohash_to_bits(value):
"""Convert a GeoHash to a list of GeoHash bits."""
b = map(BASE32MAP.get, value)
ret = []
for i in b:
out = []
for z in range(5):
out.append(i & 0b1)
i = i >> 1
ret += out[::-1]
return ret
|
python
|
def _geohash_to_bits(value):
"""Convert a GeoHash to a list of GeoHash bits."""
b = map(BASE32MAP.get, value)
ret = []
for i in b:
out = []
for z in range(5):
out.append(i & 0b1)
i = i >> 1
ret += out[::-1]
return ret
|
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train
|
https://github.com/transitland/mapzen-geohash/blob/5264d16dc2df00ebc4aedaa78ca8c6ee3fd655ad/mzgeohash/geohash.py#L31-L41
|
transitland/mapzen-geohash
|
mzgeohash/geohash.py
|
_bits_to_geohash
|
def _bits_to_geohash(value):
"""Convert a list of GeoHash bits to a GeoHash."""
ret = []
# Get 5 bits at a time
for i in (value[i:i+5] for i in xrange(0, len(value), 5)):
# Convert binary to integer
# Note: reverse here, the slice above doesn't work quite right in reverse.
total = sum([(bit*2**count) for count,bit in enumerate(i[::-1])])
ret.append(BASE32MAPR[total])
# Join the string and return
return "".join(ret)
|
python
|
def _bits_to_geohash(value):
"""Convert a list of GeoHash bits to a GeoHash."""
ret = []
# Get 5 bits at a time
for i in (value[i:i+5] for i in xrange(0, len(value), 5)):
# Convert binary to integer
# Note: reverse here, the slice above doesn't work quite right in reverse.
total = sum([(bit*2**count) for count,bit in enumerate(i[::-1])])
ret.append(BASE32MAPR[total])
# Join the string and return
return "".join(ret)
|
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Convert a list of GeoHash bits to a GeoHash.
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train
|
https://github.com/transitland/mapzen-geohash/blob/5264d16dc2df00ebc4aedaa78ca8c6ee3fd655ad/mzgeohash/geohash.py#L43-L53
|
transitland/mapzen-geohash
|
mzgeohash/geohash.py
|
decode
|
def decode(value):
"""Decode a geohash. Returns a (lon,lat) pair."""
assert value, "Invalid geohash: %s"%value
# Get the GeoHash bits
bits = _geohash_to_bits(value)
# Unzip the GeoHash bits.
lon = bits[0::2]
lat = bits[1::2]
# Convert to lat/lon
return (
_bits_to_float(lon, lower=-180.0, upper=180.0),
_bits_to_float(lat)
)
|
python
|
def decode(value):
"""Decode a geohash. Returns a (lon,lat) pair."""
assert value, "Invalid geohash: %s"%value
# Get the GeoHash bits
bits = _geohash_to_bits(value)
# Unzip the GeoHash bits.
lon = bits[0::2]
lat = bits[1::2]
# Convert to lat/lon
return (
_bits_to_float(lon, lower=-180.0, upper=180.0),
_bits_to_float(lat)
)
|
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|
[
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train
|
https://github.com/transitland/mapzen-geohash/blob/5264d16dc2df00ebc4aedaa78ca8c6ee3fd655ad/mzgeohash/geohash.py#L56-L68
|
transitland/mapzen-geohash
|
mzgeohash/geohash.py
|
encode
|
def encode(lonlat, length=12):
"""Encode a (lon,lat) pair to a GeoHash."""
assert len(lonlat) == 2, "Invalid lon/lat: %s"%lonlat
# Half the length for each component.
length /= 2
lon = _float_to_bits(lonlat[0], lower=-180.0, upper=180.0, length=length*5)
lat = _float_to_bits(lonlat[1], lower=-90.0, upper=90.0, length=length*5)
# Zip the GeoHash bits.
ret = []
for a,b in zip(lon,lat):
ret.append(a)
ret.append(b)
return _bits_to_geohash(ret)
|
python
|
def encode(lonlat, length=12):
"""Encode a (lon,lat) pair to a GeoHash."""
assert len(lonlat) == 2, "Invalid lon/lat: %s"%lonlat
# Half the length for each component.
length /= 2
lon = _float_to_bits(lonlat[0], lower=-180.0, upper=180.0, length=length*5)
lat = _float_to_bits(lonlat[1], lower=-90.0, upper=90.0, length=length*5)
# Zip the GeoHash bits.
ret = []
for a,b in zip(lon,lat):
ret.append(a)
ret.append(b)
return _bits_to_geohash(ret)
|
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train
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https://github.com/transitland/mapzen-geohash/blob/5264d16dc2df00ebc4aedaa78ca8c6ee3fd655ad/mzgeohash/geohash.py#L70-L82
|
transitland/mapzen-geohash
|
mzgeohash/geohash.py
|
adjacent
|
def adjacent(geohash, direction):
"""Return the adjacent geohash for a given direction."""
# Based on an MIT licensed implementation by Chris Veness from:
# http://www.movable-type.co.uk/scripts/geohash.html
assert direction in 'nsew', "Invalid direction: %s"%direction
assert geohash, "Invalid geohash: %s"%geohash
neighbor = {
'n': [ 'p0r21436x8zb9dcf5h7kjnmqesgutwvy', 'bc01fg45238967deuvhjyznpkmstqrwx' ],
's': [ '14365h7k9dcfesgujnmqp0r2twvyx8zb', '238967debc01fg45kmstqrwxuvhjyznp' ],
'e': [ 'bc01fg45238967deuvhjyznpkmstqrwx', 'p0r21436x8zb9dcf5h7kjnmqesgutwvy' ],
'w': [ '238967debc01fg45kmstqrwxuvhjyznp', '14365h7k9dcfesgujnmqp0r2twvyx8zb' ]
}
border = {
'n': [ 'prxz', 'bcfguvyz' ],
's': [ '028b', '0145hjnp' ],
'e': [ 'bcfguvyz', 'prxz' ],
'w': [ '0145hjnp', '028b' ]
}
last = geohash[-1]
parent = geohash[0:-1]
t = len(geohash) % 2
# Check for edge cases
if (last in border[direction][t]) and (parent):
parent = adjacent(parent, direction)
return parent + BASESEQUENCE[neighbor[direction][t].index(last)]
|
python
|
def adjacent(geohash, direction):
"""Return the adjacent geohash for a given direction."""
# Based on an MIT licensed implementation by Chris Veness from:
# http://www.movable-type.co.uk/scripts/geohash.html
assert direction in 'nsew', "Invalid direction: %s"%direction
assert geohash, "Invalid geohash: %s"%geohash
neighbor = {
'n': [ 'p0r21436x8zb9dcf5h7kjnmqesgutwvy', 'bc01fg45238967deuvhjyznpkmstqrwx' ],
's': [ '14365h7k9dcfesgujnmqp0r2twvyx8zb', '238967debc01fg45kmstqrwxuvhjyznp' ],
'e': [ 'bc01fg45238967deuvhjyznpkmstqrwx', 'p0r21436x8zb9dcf5h7kjnmqesgutwvy' ],
'w': [ '238967debc01fg45kmstqrwxuvhjyznp', '14365h7k9dcfesgujnmqp0r2twvyx8zb' ]
}
border = {
'n': [ 'prxz', 'bcfguvyz' ],
's': [ '028b', '0145hjnp' ],
'e': [ 'bcfguvyz', 'prxz' ],
'w': [ '0145hjnp', '028b' ]
}
last = geohash[-1]
parent = geohash[0:-1]
t = len(geohash) % 2
# Check for edge cases
if (last in border[direction][t]) and (parent):
parent = adjacent(parent, direction)
return parent + BASESEQUENCE[neighbor[direction][t].index(last)]
|
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Return the adjacent geohash for a given direction.
|
[
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train
|
https://github.com/transitland/mapzen-geohash/blob/5264d16dc2df00ebc4aedaa78ca8c6ee3fd655ad/mzgeohash/geohash.py#L84-L108
|
transitland/mapzen-geohash
|
mzgeohash/geohash.py
|
neighbors
|
def neighbors(geohash):
"""Return all neighboring geohashes."""
return {
'n': adjacent(geohash, 'n'),
'ne': adjacent(adjacent(geohash, 'n'), 'e'),
'e': adjacent(geohash, 'e'),
'se': adjacent(adjacent(geohash, 's'), 'e'),
's': adjacent(geohash, 's'),
'sw': adjacent(adjacent(geohash, 's'), 'w'),
'w': adjacent(geohash, 'w'),
'nw': adjacent(adjacent(geohash, 'n'), 'w'),
'c': geohash
}
|
python
|
def neighbors(geohash):
"""Return all neighboring geohashes."""
return {
'n': adjacent(geohash, 'n'),
'ne': adjacent(adjacent(geohash, 'n'), 'e'),
'e': adjacent(geohash, 'e'),
'se': adjacent(adjacent(geohash, 's'), 'e'),
's': adjacent(geohash, 's'),
'sw': adjacent(adjacent(geohash, 's'), 'w'),
'w': adjacent(geohash, 'w'),
'nw': adjacent(adjacent(geohash, 'n'), 'w'),
'c': geohash
}
|
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"'n'",
")",
",",
"'w'",
")",
",",
"'c'",
":",
"geohash",
"}"
] |
Return all neighboring geohashes.
|
[
"Return",
"all",
"neighboring",
"geohashes",
"."
] |
train
|
https://github.com/transitland/mapzen-geohash/blob/5264d16dc2df00ebc4aedaa78ca8c6ee3fd655ad/mzgeohash/geohash.py#L110-L122
|
SiLab-Bonn/pyBAR
|
pybar/run_manager.py
|
thunkify
|
def thunkify(thread_name=None, daemon=True, default_func=None):
'''Make a function immediately return a function of no args which, when called,
waits for the result, which will start being processed in another thread.
Taken from https://wiki.python.org/moin/PythonDecoratorLibrary.
'''
def actual_decorator(f):
@functools.wraps(f)
def thunked(*args, **kwargs):
result = [None]
exc = [False, None] # has exception?, exception info
# wait_event = threading.Event()
def worker_func():
try:
func_result = f(*args, **kwargs)
result[0] = func_result
except Exception:
exc[0] = True
exc[1] = sys.exc_info()
logging.error("%s has thrown an exception:\n%s", thread_name, traceback.format_exc())
# finally:
# wait_event.set()
worker_thread = Thread(target=worker_func, name=thread_name if thread_name else None)
worker_thread.daemon = daemon
def thunk(timeout=None):
# avoid blocking MainThread
start_time = time()
while True:
worker_thread.join(timeout=0.1)
if (timeout and timeout < time() - start_time) or not worker_thread.is_alive():
break
# worker_thread.join(timeout=timeout)
# wait_event.wait()
if worker_thread.is_alive():
if default_func is None:
return
else:
return default_func()
if exc[0]:
raise exc[1][0], exc[1][1], exc[1][2]
return result[0]
worker_thread.start()
# threading.Thread(target=worker_func, name=thread_name if thread_name else None).start()
return thunk
return thunked
return actual_decorator
|
python
|
def thunkify(thread_name=None, daemon=True, default_func=None):
'''Make a function immediately return a function of no args which, when called,
waits for the result, which will start being processed in another thread.
Taken from https://wiki.python.org/moin/PythonDecoratorLibrary.
'''
def actual_decorator(f):
@functools.wraps(f)
def thunked(*args, **kwargs):
result = [None]
exc = [False, None] # has exception?, exception info
# wait_event = threading.Event()
def worker_func():
try:
func_result = f(*args, **kwargs)
result[0] = func_result
except Exception:
exc[0] = True
exc[1] = sys.exc_info()
logging.error("%s has thrown an exception:\n%s", thread_name, traceback.format_exc())
# finally:
# wait_event.set()
worker_thread = Thread(target=worker_func, name=thread_name if thread_name else None)
worker_thread.daemon = daemon
def thunk(timeout=None):
# avoid blocking MainThread
start_time = time()
while True:
worker_thread.join(timeout=0.1)
if (timeout and timeout < time() - start_time) or not worker_thread.is_alive():
break
# worker_thread.join(timeout=timeout)
# wait_event.wait()
if worker_thread.is_alive():
if default_func is None:
return
else:
return default_func()
if exc[0]:
raise exc[1][0], exc[1][1], exc[1][2]
return result[0]
worker_thread.start()
# threading.Thread(target=worker_func, name=thread_name if thread_name else None).start()
return thunk
return thunked
return actual_decorator
|
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Make a function immediately return a function of no args which, when called,
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Taken from https://wiki.python.org/moin/PythonDecoratorLibrary.
|
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"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/run_manager.py#L362-L410
|
SiLab-Bonn/pyBAR
|
pybar/run_manager.py
|
set_event_when_keyboard_interrupt
|
def set_event_when_keyboard_interrupt(_lambda):
'''Decorator function that sets Threading.Event() when keyboard interrupt (Ctrl+C) was raised
Parameters
----------
_lambda : function
Lambda function that points to Threading.Event() object
Returns
-------
wrapper : function
Examples
--------
@set_event_when_keyboard_interrupt(lambda x: x.stop_thread_event)
def scan(self, **kwargs):
# some code
Note
----
Decorated functions cannot be derived.
'''
def wrapper(f):
@wraps(f)
def wrapped_f(self, *f_args, **f_kwargs):
try:
f(self, *f_args, **f_kwargs)
except KeyboardInterrupt:
_lambda(self).set()
# logging.info('Keyboard interrupt: setting %s' % _lambda(self).__name__)
return wrapped_f
return wrapper
|
python
|
def set_event_when_keyboard_interrupt(_lambda):
'''Decorator function that sets Threading.Event() when keyboard interrupt (Ctrl+C) was raised
Parameters
----------
_lambda : function
Lambda function that points to Threading.Event() object
Returns
-------
wrapper : function
Examples
--------
@set_event_when_keyboard_interrupt(lambda x: x.stop_thread_event)
def scan(self, **kwargs):
# some code
Note
----
Decorated functions cannot be derived.
'''
def wrapper(f):
@wraps(f)
def wrapped_f(self, *f_args, **f_kwargs):
try:
f(self, *f_args, **f_kwargs)
except KeyboardInterrupt:
_lambda(self).set()
# logging.info('Keyboard interrupt: setting %s' % _lambda(self).__name__)
return wrapped_f
return wrapper
|
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"return",
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] |
Decorator function that sets Threading.Event() when keyboard interrupt (Ctrl+C) was raised
Parameters
----------
_lambda : function
Lambda function that points to Threading.Event() object
Returns
-------
wrapper : function
Examples
--------
@set_event_when_keyboard_interrupt(lambda x: x.stop_thread_event)
def scan(self, **kwargs):
# some code
Note
----
Decorated functions cannot be derived.
|
[
"Decorator",
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"that",
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".",
"Event",
"()",
"when",
"keyboard",
"interrupt",
"(",
"Ctrl",
"+",
"C",
")",
"was",
"raised"
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/run_manager.py#L655-L686
|
SiLab-Bonn/pyBAR
|
pybar/run_manager.py
|
RunBase.run_id
|
def run_id(self):
'''Run name without whitespace
'''
s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', self.__class__.__name__)
return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()
|
python
|
def run_id(self):
'''Run name without whitespace
'''
s1 = re.sub('(.)([A-Z][a-z]+)', r'\1_\2', self.__class__.__name__)
return re.sub('([a-z0-9])([A-Z])', r'\1_\2', s1).lower()
|
[
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",",
"r'\\1_\\2'",
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")",
".",
"lower",
"(",
")"
] |
Run name without whitespace
|
[
"Run",
"name",
"without",
"whitespace"
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/run_manager.py#L83-L87
|
SiLab-Bonn/pyBAR
|
pybar/run_manager.py
|
RunBase.conf
|
def conf(self):
'''Configuration (namedtuple)
'''
conf = namedtuple('conf', field_names=self._conf.keys())
return conf(**self._conf)
|
python
|
def conf(self):
'''Configuration (namedtuple)
'''
conf = namedtuple('conf', field_names=self._conf.keys())
return conf(**self._conf)
|
[
"def",
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".",
"_conf",
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] |
Configuration (namedtuple)
|
[
"Configuration",
"(",
"namedtuple",
")"
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/run_manager.py#L90-L94
|
SiLab-Bonn/pyBAR
|
pybar/run_manager.py
|
RunBase.run_conf
|
def run_conf(self):
'''Run configuration (namedtuple)
'''
run_conf = namedtuple('run_conf', field_names=self._run_conf.keys())
return run_conf(**self._run_conf)
|
python
|
def run_conf(self):
'''Run configuration (namedtuple)
'''
run_conf = namedtuple('run_conf', field_names=self._run_conf.keys())
return run_conf(**self._run_conf)
|
[
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"_run_conf",
")"
] |
Run configuration (namedtuple)
|
[
"Run",
"configuration",
"(",
"namedtuple",
")"
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/run_manager.py#L97-L101
|
SiLab-Bonn/pyBAR
|
pybar/run_manager.py
|
RunBase.default_run_conf
|
def default_run_conf(self):
'''Default run configuration (namedtuple)
'''
default_run_conf = namedtuple('default_run_conf', field_names=self._default_run_conf.keys())
return default_run_conf(**self._default_run_conf)
|
python
|
def default_run_conf(self):
'''Default run configuration (namedtuple)
'''
default_run_conf = namedtuple('default_run_conf', field_names=self._default_run_conf.keys())
return default_run_conf(**self._default_run_conf)
|
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"return",
"default_run_conf",
"(",
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"_default_run_conf",
")"
] |
Default run configuration (namedtuple)
|
[
"Default",
"run",
"configuration",
"(",
"namedtuple",
")"
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/run_manager.py#L110-L114
|
SiLab-Bonn/pyBAR
|
pybar/run_manager.py
|
RunBase._init
|
def _init(self, run_conf, run_number=None):
'''Initialization before a new run.
'''
self.stop_run.clear()
self.abort_run.clear()
self._run_status = run_status.running
self._write_run_number(run_number)
self._init_run_conf(run_conf)
|
python
|
def _init(self, run_conf, run_number=None):
'''Initialization before a new run.
'''
self.stop_run.clear()
self.abort_run.clear()
self._run_status = run_status.running
self._write_run_number(run_number)
self._init_run_conf(run_conf)
|
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Initialization before a new run.
|
[
"Initialization",
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"a",
"new",
"run",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/run_manager.py#L188-L195
|
SiLab-Bonn/pyBAR
|
pybar/run_manager.py
|
RunBase.connect_cancel
|
def connect_cancel(self, functions):
'''Run given functions when a run is cancelled.
'''
self._cancel_functions = []
for func in functions:
if isinstance(func, basestring) and hasattr(self, func) and callable(getattr(self, func)):
self._cancel_functions.append(getattr(self, func))
elif callable(func):
self._cancel_functions.append(func)
else:
raise ValueError("Unknown function %s" % str(func))
|
python
|
def connect_cancel(self, functions):
'''Run given functions when a run is cancelled.
'''
self._cancel_functions = []
for func in functions:
if isinstance(func, basestring) and hasattr(self, func) and callable(getattr(self, func)):
self._cancel_functions.append(getattr(self, func))
elif callable(func):
self._cancel_functions.append(func)
else:
raise ValueError("Unknown function %s" % str(func))
|
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Run given functions when a run is cancelled.
|
[
"Run",
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"run",
"is",
"cancelled",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/run_manager.py#L248-L258
|
SiLab-Bonn/pyBAR
|
pybar/run_manager.py
|
RunBase.handle_cancel
|
def handle_cancel(self, **kwargs):
'''Cancelling a run.
'''
for func in self._cancel_functions:
f_args = getargspec(func)[0]
f_kwargs = {key: kwargs[key] for key in f_args if key in kwargs}
func(**f_kwargs)
|
python
|
def handle_cancel(self, **kwargs):
'''Cancelling a run.
'''
for func in self._cancel_functions:
f_args = getargspec(func)[0]
f_kwargs = {key: kwargs[key] for key in f_args if key in kwargs}
func(**f_kwargs)
|
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Cancelling a run.
|
[
"Cancelling",
"a",
"run",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/run_manager.py#L260-L266
|
SiLab-Bonn/pyBAR
|
pybar/run_manager.py
|
RunBase.stop
|
def stop(self, msg=None):
'''Stopping a run. Control for loops. Gentle stop/abort.
This event should provide a more gentle abort. The run should stop ASAP but the run is still considered complete.
'''
if not self.stop_run.is_set():
if msg:
logging.info('%s%s Stopping run...', msg, ('' if msg[-1] in punctuation else '.'))
else:
logging.info('Stopping run...')
self.stop_run.set()
|
python
|
def stop(self, msg=None):
'''Stopping a run. Control for loops. Gentle stop/abort.
This event should provide a more gentle abort. The run should stop ASAP but the run is still considered complete.
'''
if not self.stop_run.is_set():
if msg:
logging.info('%s%s Stopping run...', msg, ('' if msg[-1] in punctuation else '.'))
else:
logging.info('Stopping run...')
self.stop_run.set()
|
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Stopping a run. Control for loops. Gentle stop/abort.
This event should provide a more gentle abort. The run should stop ASAP but the run is still considered complete.
|
[
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".",
"Control",
"for",
"loops",
".",
"Gentle",
"stop",
"/",
"abort",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/run_manager.py#L268-L278
|
SiLab-Bonn/pyBAR
|
pybar/run_manager.py
|
RunBase.abort
|
def abort(self, msg=None):
'''Aborting a run. Control for loops. Immediate stop/abort.
The implementation should stop a run ASAP when this event is set. The run is considered incomplete.
'''
if not self.abort_run.is_set():
if msg:
logging.error('%s%s Aborting run...', msg, ('' if msg[-1] in punctuation else '.'))
else:
logging.error('Aborting run...')
self.abort_run.set()
self.stop_run.set()
|
python
|
def abort(self, msg=None):
'''Aborting a run. Control for loops. Immediate stop/abort.
The implementation should stop a run ASAP when this event is set. The run is considered incomplete.
'''
if not self.abort_run.is_set():
if msg:
logging.error('%s%s Aborting run...', msg, ('' if msg[-1] in punctuation else '.'))
else:
logging.error('Aborting run...')
self.abort_run.set()
self.stop_run.set()
|
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")",
"self",
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Aborting a run. Control for loops. Immediate stop/abort.
The implementation should stop a run ASAP when this event is set. The run is considered incomplete.
|
[
"Aborting",
"a",
"run",
".",
"Control",
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".",
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"/",
"abort",
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/run_manager.py#L280-L291
|
SiLab-Bonn/pyBAR
|
pybar/run_manager.py
|
RunManager.run_run
|
def run_run(self, run, conf=None, run_conf=None, use_thread=False, catch_exception=True):
'''Runs a run in another thread. Non-blocking.
Parameters
----------
run : class, object
Run class or object.
run_conf : str, dict, file
Specific configuration for the run.
use_thread : bool
If True, run run in thread and returns blocking function.
Returns
-------
If use_thread is True, returns function, which blocks until thread terminates, and which itself returns run status.
If use_thread is False, returns run status.
'''
if isinstance(conf, basestring) and os.path.isfile(conf):
logging.info('Updating configuration from file %s', os.path.abspath(conf))
elif conf is not None:
logging.info('Updating configuration')
conf = self.open_conf(conf)
self._conf.update(conf)
if isclass(run):
# instantiate the class
run = run(conf=self._conf)
local_run_conf = {}
# general parameters from conf
if 'run_conf' in self._conf:
logging.info('Updating run configuration using run_conf key from configuration')
local_run_conf.update(self._conf['run_conf'])
# check for class name, scan specific parameters from conf
if run.__class__.__name__ in self._conf:
logging.info('Updating run configuration using %s key from configuration' % (run.__class__.__name__,))
local_run_conf.update(self._conf[run.__class__.__name__])
if isinstance(run_conf, basestring) and os.path.isfile(run_conf):
logging.info('Updating run configuration from file %s', os.path.abspath(run_conf))
elif run_conf is not None:
logging.info('Updating run configuration')
run_conf = self.open_conf(run_conf)
# check for class name, scan specific parameters from conf
if run.__class__.__name__ in run_conf:
run_conf = run_conf[run.__class__.__name__]
# run_conf parameter has highest priority, updated last
local_run_conf.update(run_conf)
if use_thread:
self.current_run = run
@thunkify(thread_name='RunThread', daemon=True, default_func=self.current_run.get_run_status)
def run_run_in_thread():
return run.run(run_conf=local_run_conf)
signal.signal(signal.SIGINT, self._signal_handler)
logging.info('Press Ctrl-C to stop run')
return run_run_in_thread()
else:
self.current_run = run
status = run.run(run_conf=local_run_conf)
if not catch_exception and status != run_status.finished:
raise RuntimeError('Exception occurred. Please read the log.')
return status
|
python
|
def run_run(self, run, conf=None, run_conf=None, use_thread=False, catch_exception=True):
'''Runs a run in another thread. Non-blocking.
Parameters
----------
run : class, object
Run class or object.
run_conf : str, dict, file
Specific configuration for the run.
use_thread : bool
If True, run run in thread and returns blocking function.
Returns
-------
If use_thread is True, returns function, which blocks until thread terminates, and which itself returns run status.
If use_thread is False, returns run status.
'''
if isinstance(conf, basestring) and os.path.isfile(conf):
logging.info('Updating configuration from file %s', os.path.abspath(conf))
elif conf is not None:
logging.info('Updating configuration')
conf = self.open_conf(conf)
self._conf.update(conf)
if isclass(run):
# instantiate the class
run = run(conf=self._conf)
local_run_conf = {}
# general parameters from conf
if 'run_conf' in self._conf:
logging.info('Updating run configuration using run_conf key from configuration')
local_run_conf.update(self._conf['run_conf'])
# check for class name, scan specific parameters from conf
if run.__class__.__name__ in self._conf:
logging.info('Updating run configuration using %s key from configuration' % (run.__class__.__name__,))
local_run_conf.update(self._conf[run.__class__.__name__])
if isinstance(run_conf, basestring) and os.path.isfile(run_conf):
logging.info('Updating run configuration from file %s', os.path.abspath(run_conf))
elif run_conf is not None:
logging.info('Updating run configuration')
run_conf = self.open_conf(run_conf)
# check for class name, scan specific parameters from conf
if run.__class__.__name__ in run_conf:
run_conf = run_conf[run.__class__.__name__]
# run_conf parameter has highest priority, updated last
local_run_conf.update(run_conf)
if use_thread:
self.current_run = run
@thunkify(thread_name='RunThread', daemon=True, default_func=self.current_run.get_run_status)
def run_run_in_thread():
return run.run(run_conf=local_run_conf)
signal.signal(signal.SIGINT, self._signal_handler)
logging.info('Press Ctrl-C to stop run')
return run_run_in_thread()
else:
self.current_run = run
status = run.run(run_conf=local_run_conf)
if not catch_exception and status != run_status.finished:
raise RuntimeError('Exception occurred. Please read the log.')
return status
|
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Runs a run in another thread. Non-blocking.
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run : class, object
Run class or object.
run_conf : str, dict, file
Specific configuration for the run.
use_thread : bool
If True, run run in thread and returns blocking function.
Returns
-------
If use_thread is True, returns function, which blocks until thread terminates, and which itself returns run status.
If use_thread is False, returns run status.
|
[
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"another",
"thread",
".",
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"-",
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"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/run_manager.py#L513-L578
|
SiLab-Bonn/pyBAR
|
pybar/run_manager.py
|
RunManager.run_primlist
|
def run_primlist(self, primlist, skip_remaining=False):
'''Runs runs from a primlist.
Parameters
----------
primlist : string
Filename of primlist.
skip_remaining : bool
If True, skip remaining runs, if a run does not exit with status FINISHED.
Note
----
Primlist is a text file of the following format (comment line by adding '#'):
<module name (containing class) or class (in either case use dot notation)>; <scan parameter>=<value>; <another scan parameter>=<another value>
'''
runlist = self.open_primlist(primlist)
for index, run in enumerate(runlist):
logging.info('Progressing with run %i out of %i...', index + 1, len(runlist))
join = self.run_run(run, use_thread=True)
status = join()
if skip_remaining and not status == run_status.finished:
logging.error('Exited run %i with status %s: Skipping all remaining runs.', run.run_number, status)
break
|
python
|
def run_primlist(self, primlist, skip_remaining=False):
'''Runs runs from a primlist.
Parameters
----------
primlist : string
Filename of primlist.
skip_remaining : bool
If True, skip remaining runs, if a run does not exit with status FINISHED.
Note
----
Primlist is a text file of the following format (comment line by adding '#'):
<module name (containing class) or class (in either case use dot notation)>; <scan parameter>=<value>; <another scan parameter>=<another value>
'''
runlist = self.open_primlist(primlist)
for index, run in enumerate(runlist):
logging.info('Progressing with run %i out of %i...', index + 1, len(runlist))
join = self.run_run(run, use_thread=True)
status = join()
if skip_remaining and not status == run_status.finished:
logging.error('Exited run %i with status %s: Skipping all remaining runs.', run.run_number, status)
break
|
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Runs runs from a primlist.
Parameters
----------
primlist : string
Filename of primlist.
skip_remaining : bool
If True, skip remaining runs, if a run does not exit with status FINISHED.
Note
----
Primlist is a text file of the following format (comment line by adding '#'):
<module name (containing class) or class (in either case use dot notation)>; <scan parameter>=<value>; <another scan parameter>=<another value>
|
[
"Runs",
"runs",
"from",
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"primlist",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/run_manager.py#L580-L602
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis.py
|
analyze_beam_spot
|
def analyze_beam_spot(scan_base, combine_n_readouts=1000, chunk_size=10000000, plot_occupancy_hists=False, output_pdf=None, output_file=None):
''' Determines the mean x and y beam spot position as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts'). The occupancy is determined
for the given combined events and stored into a pdf file. At the end the beam x and y is plotted into a scatter plot with absolute positions in um.
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
'''
time_stamp = []
x = []
y = []
for data_file in scan_base:
with tb.open_file(data_file + '_interpreted.h5', mode="r+") as in_hit_file_h5:
# get data and data pointer
meta_data_array = in_hit_file_h5.root.meta_data[:]
hit_table = in_hit_file_h5.root.Hits
# determine the event ranges to analyze (timestamp_start, start_event_number, stop_event_number)
parameter_ranges = np.column_stack((analysis_utils.get_ranges_from_array(meta_data_array['timestamp_start'][::combine_n_readouts]), analysis_utils.get_ranges_from_array(meta_data_array['event_number'][::combine_n_readouts])))
# create a event_numer index (important)
analysis_utils.index_event_number(hit_table)
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_tot_hist = False
analyze_data.create_bcid_hist = False
analyze_data.histogram.set_no_scan_parameter()
# variables for read speed up
index = 0 # index where to start the read out, 0 at the beginning, increased during looping
best_chunk_size = chunk_size
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=hit_table.shape[0], term_width=80)
progress_bar.start()
# loop over the selected events
for parameter_index, parameter_range in enumerate(parameter_ranges):
logging.debug('Analyze time stamp ' + str(parameter_range[0]) + ' and data from events = [' + str(parameter_range[2]) + ',' + str(parameter_range[3]) + '[ ' + str(int(float(float(parameter_index) / float(len(parameter_ranges)) * 100.0))) + '%')
analyze_data.reset() # resets the data of the last analysis
# loop over the hits in the actual selected events with optimizations: determine best chunk size, start word index given
readout_hit_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
for hits, index in analysis_utils.data_aligned_at_events(hit_table, start_event_number=parameter_range[2], stop_event_number=parameter_range[3], start_index=index, chunk_size=best_chunk_size):
analyze_data.analyze_hits(hits) # analyze the selected hits in chunks
readout_hit_len += hits.shape[0]
progress_bar.update(index)
best_chunk_size = int(1.5 * readout_hit_len) if int(1.05 * readout_hit_len) < chunk_size else chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
# get and store results
occupancy_array = analyze_data.histogram.get_occupancy()
projection_x = np.sum(occupancy_array, axis=0).ravel()
projection_y = np.sum(occupancy_array, axis=1).ravel()
x.append(analysis_utils.get_mean_from_histogram(projection_x, bin_positions=range(0, 80)))
y.append(analysis_utils.get_mean_from_histogram(projection_y, bin_positions=range(0, 336)))
time_stamp.append(parameter_range[0])
if plot_occupancy_hists:
plotting.plot_occupancy(occupancy_array[:, :, 0], title='Occupancy for events between ' + time.strftime('%H:%M:%S', time.localtime(parameter_range[0])) + ' and ' + time.strftime('%H:%M:%S', time.localtime(parameter_range[1])), filename=output_pdf)
progress_bar.finish()
plotting.plot_scatter([i * 250 for i in x], [i * 50 for i in y], title='Mean beam position', x_label='x [um]', y_label='y [um]', marker_style='-o', filename=output_pdf)
if output_file:
with tb.open_file(output_file, mode="a") as out_file_h5:
rec_array = np.array(zip(time_stamp, x, y), dtype=[('time_stamp', float), ('x', float), ('y', float)])
try:
beam_spot_table = out_file_h5.create_table(out_file_h5.root, name='Beamspot', description=rec_array, title='Beam spot position', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
beam_spot_table[:] = rec_array
except tb.exceptions.NodeError:
logging.warning(output_file + ' has already a Beamspot note, do not overwrite existing.')
return time_stamp, x, y
|
python
|
def analyze_beam_spot(scan_base, combine_n_readouts=1000, chunk_size=10000000, plot_occupancy_hists=False, output_pdf=None, output_file=None):
''' Determines the mean x and y beam spot position as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts'). The occupancy is determined
for the given combined events and stored into a pdf file. At the end the beam x and y is plotted into a scatter plot with absolute positions in um.
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
'''
time_stamp = []
x = []
y = []
for data_file in scan_base:
with tb.open_file(data_file + '_interpreted.h5', mode="r+") as in_hit_file_h5:
# get data and data pointer
meta_data_array = in_hit_file_h5.root.meta_data[:]
hit_table = in_hit_file_h5.root.Hits
# determine the event ranges to analyze (timestamp_start, start_event_number, stop_event_number)
parameter_ranges = np.column_stack((analysis_utils.get_ranges_from_array(meta_data_array['timestamp_start'][::combine_n_readouts]), analysis_utils.get_ranges_from_array(meta_data_array['event_number'][::combine_n_readouts])))
# create a event_numer index (important)
analysis_utils.index_event_number(hit_table)
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_tot_hist = False
analyze_data.create_bcid_hist = False
analyze_data.histogram.set_no_scan_parameter()
# variables for read speed up
index = 0 # index where to start the read out, 0 at the beginning, increased during looping
best_chunk_size = chunk_size
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=hit_table.shape[0], term_width=80)
progress_bar.start()
# loop over the selected events
for parameter_index, parameter_range in enumerate(parameter_ranges):
logging.debug('Analyze time stamp ' + str(parameter_range[0]) + ' and data from events = [' + str(parameter_range[2]) + ',' + str(parameter_range[3]) + '[ ' + str(int(float(float(parameter_index) / float(len(parameter_ranges)) * 100.0))) + '%')
analyze_data.reset() # resets the data of the last analysis
# loop over the hits in the actual selected events with optimizations: determine best chunk size, start word index given
readout_hit_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
for hits, index in analysis_utils.data_aligned_at_events(hit_table, start_event_number=parameter_range[2], stop_event_number=parameter_range[3], start_index=index, chunk_size=best_chunk_size):
analyze_data.analyze_hits(hits) # analyze the selected hits in chunks
readout_hit_len += hits.shape[0]
progress_bar.update(index)
best_chunk_size = int(1.5 * readout_hit_len) if int(1.05 * readout_hit_len) < chunk_size else chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
# get and store results
occupancy_array = analyze_data.histogram.get_occupancy()
projection_x = np.sum(occupancy_array, axis=0).ravel()
projection_y = np.sum(occupancy_array, axis=1).ravel()
x.append(analysis_utils.get_mean_from_histogram(projection_x, bin_positions=range(0, 80)))
y.append(analysis_utils.get_mean_from_histogram(projection_y, bin_positions=range(0, 336)))
time_stamp.append(parameter_range[0])
if plot_occupancy_hists:
plotting.plot_occupancy(occupancy_array[:, :, 0], title='Occupancy for events between ' + time.strftime('%H:%M:%S', time.localtime(parameter_range[0])) + ' and ' + time.strftime('%H:%M:%S', time.localtime(parameter_range[1])), filename=output_pdf)
progress_bar.finish()
plotting.plot_scatter([i * 250 for i in x], [i * 50 for i in y], title='Mean beam position', x_label='x [um]', y_label='y [um]', marker_style='-o', filename=output_pdf)
if output_file:
with tb.open_file(output_file, mode="a") as out_file_h5:
rec_array = np.array(zip(time_stamp, x, y), dtype=[('time_stamp', float), ('x', float), ('y', float)])
try:
beam_spot_table = out_file_h5.create_table(out_file_h5.root, name='Beamspot', description=rec_array, title='Beam spot position', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
beam_spot_table[:] = rec_array
except tb.exceptions.NodeError:
logging.warning(output_file + ' has already a Beamspot note, do not overwrite existing.')
return time_stamp, x, y
|
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Determines the mean x and y beam spot position as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts'). The occupancy is determined
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Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis.py#L23-L99
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis.py
|
analyze_event_rate
|
def analyze_event_rate(scan_base, combine_n_readouts=1000, time_line_absolute=True, output_pdf=None, output_file=None):
''' Determines the number of events as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts'). The number of events is taken from the meta data info
and stored into a pdf file.
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
time_line_absolute: bool
if true the analysis uses absolute time stamps
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
'''
time_stamp = []
rate = []
start_time_set = False
for data_file in scan_base:
with tb.open_file(data_file + '_interpreted.h5', mode="r") as in_file_h5:
meta_data_array = in_file_h5.root.meta_data[:]
parameter_ranges = np.column_stack((analysis_utils.get_ranges_from_array(meta_data_array['timestamp_start'][::combine_n_readouts]), analysis_utils.get_ranges_from_array(meta_data_array['event_number'][::combine_n_readouts])))
if time_line_absolute:
time_stamp.extend(parameter_ranges[:-1, 0])
else:
if not start_time_set:
start_time = parameter_ranges[0, 0]
start_time_set = True
time_stamp.extend((parameter_ranges[:-1, 0] - start_time) / 60.0)
rate.extend((parameter_ranges[:-1, 3] - parameter_ranges[:-1, 2]) / (parameter_ranges[:-1, 1] - parameter_ranges[:-1, 0])) # d#Events / dt
if time_line_absolute:
plotting.plot_scatter_time(time_stamp, rate, title='Event rate [Hz]', marker_style='o', filename=output_pdf)
else:
plotting.plot_scatter(time_stamp, rate, title='Events per time', x_label='Progressed time [min.]', y_label='Events rate [Hz]', marker_style='o', filename=output_pdf)
if output_file:
with tb.open_file(output_file, mode="a") as out_file_h5:
rec_array = np.array(zip(time_stamp, rate), dtype=[('time_stamp', float), ('rate', float)]).view(np.recarray)
try:
rate_table = out_file_h5.create_table(out_file_h5.root, name='Eventrate', description=rec_array, title='Event rate', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
rate_table[:] = rec_array
except tb.exceptions.NodeError:
logging.warning(output_file + ' has already a Eventrate note, do not overwrite existing.')
return time_stamp, rate
|
python
|
def analyze_event_rate(scan_base, combine_n_readouts=1000, time_line_absolute=True, output_pdf=None, output_file=None):
''' Determines the number of events as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts'). The number of events is taken from the meta data info
and stored into a pdf file.
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
time_line_absolute: bool
if true the analysis uses absolute time stamps
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
'''
time_stamp = []
rate = []
start_time_set = False
for data_file in scan_base:
with tb.open_file(data_file + '_interpreted.h5', mode="r") as in_file_h5:
meta_data_array = in_file_h5.root.meta_data[:]
parameter_ranges = np.column_stack((analysis_utils.get_ranges_from_array(meta_data_array['timestamp_start'][::combine_n_readouts]), analysis_utils.get_ranges_from_array(meta_data_array['event_number'][::combine_n_readouts])))
if time_line_absolute:
time_stamp.extend(parameter_ranges[:-1, 0])
else:
if not start_time_set:
start_time = parameter_ranges[0, 0]
start_time_set = True
time_stamp.extend((parameter_ranges[:-1, 0] - start_time) / 60.0)
rate.extend((parameter_ranges[:-1, 3] - parameter_ranges[:-1, 2]) / (parameter_ranges[:-1, 1] - parameter_ranges[:-1, 0])) # d#Events / dt
if time_line_absolute:
plotting.plot_scatter_time(time_stamp, rate, title='Event rate [Hz]', marker_style='o', filename=output_pdf)
else:
plotting.plot_scatter(time_stamp, rate, title='Events per time', x_label='Progressed time [min.]', y_label='Events rate [Hz]', marker_style='o', filename=output_pdf)
if output_file:
with tb.open_file(output_file, mode="a") as out_file_h5:
rec_array = np.array(zip(time_stamp, rate), dtype=[('time_stamp', float), ('rate', float)]).view(np.recarray)
try:
rate_table = out_file_h5.create_table(out_file_h5.root, name='Eventrate', description=rec_array, title='Event rate', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
rate_table[:] = rec_array
except tb.exceptions.NodeError:
logging.warning(output_file + ' has already a Eventrate note, do not overwrite existing.')
return time_stamp, rate
|
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Determines the number of events as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts'). The number of events is taken from the meta data info
and stored into a pdf file.
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
time_line_absolute: bool
if true the analysis uses absolute time stamps
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis.py#L102-L147
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis.py
|
analyse_n_cluster_per_event
|
def analyse_n_cluster_per_event(scan_base, include_no_cluster=False, time_line_absolute=True, combine_n_readouts=1000, chunk_size=10000000, plot_n_cluster_hists=False, output_pdf=None, output_file=None):
''' Determines the number of cluster per event as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts').
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
include_no_cluster: bool
Set to true to also consider all events without any hit.
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
'''
time_stamp = []
n_cluster = []
start_time_set = False
for data_file in scan_base:
with tb.open_file(data_file + '_interpreted.h5', mode="r+") as in_cluster_file_h5:
# get data and data pointer
meta_data_array = in_cluster_file_h5.root.meta_data[:]
cluster_table = in_cluster_file_h5.root.Cluster
# determine the event ranges to analyze (timestamp_start, start_event_number, stop_event_number)
parameter_ranges = np.column_stack((analysis_utils.get_ranges_from_array(meta_data_array['timestamp_start'][::combine_n_readouts]), analysis_utils.get_ranges_from_array(meta_data_array['event_number'][::combine_n_readouts])))
# create a event_numer index (important for speed)
analysis_utils.index_event_number(cluster_table)
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_tot_hist = False
analyze_data.create_bcid_hist = False
# variables for read speed up
index = 0 # index where to start the read out, 0 at the beginning, increased during looping
best_chunk_size = chunk_size
total_cluster = cluster_table.shape[0]
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=total_cluster, term_width=80)
progress_bar.start()
# loop over the selected events
for parameter_index, parameter_range in enumerate(parameter_ranges):
logging.debug('Analyze time stamp ' + str(parameter_range[0]) + ' and data from events = [' + str(parameter_range[2]) + ',' + str(parameter_range[3]) + '[ ' + str(int(float(float(parameter_index) / float(len(parameter_ranges)) * 100.0))) + '%')
analyze_data.reset() # resets the data of the last analysis
# loop over the cluster in the actual selected events with optimizations: determine best chunk size, start word index given
readout_cluster_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
hist = None
for clusters, index in analysis_utils.data_aligned_at_events(cluster_table, start_event_number=parameter_range[2], stop_event_number=parameter_range[3], start_index=index, chunk_size=best_chunk_size):
n_cluster_per_event = analysis_utils.get_n_cluster_in_events(clusters['event_number'])[:, 1] # array with the number of cluster per event, cluster per event are at least 1
if hist is None:
hist = np.histogram(n_cluster_per_event, bins=10, range=(0, 10))[0]
else:
hist = np.add(hist, np.histogram(n_cluster_per_event, bins=10, range=(0, 10))[0])
if include_no_cluster and parameter_range[3] is not None: # happend for the last readout
hist[0] = (parameter_range[3] - parameter_range[2]) - len(n_cluster_per_event) # add the events without any cluster
readout_cluster_len += clusters.shape[0]
total_cluster -= len(clusters)
progress_bar.update(index)
best_chunk_size = int(1.5 * readout_cluster_len) if int(1.05 * readout_cluster_len) < chunk_size else chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
if plot_n_cluster_hists:
plotting.plot_1d_hist(hist, title='Number of cluster per event at ' + str(parameter_range[0]), x_axis_title='Number of cluster', y_axis_title='#', log_y=True, filename=output_pdf)
hist = hist.astype('f4') / np.sum(hist) # calculate fraction from total numbers
if time_line_absolute:
time_stamp.append(parameter_range[0])
else:
if not start_time_set:
start_time = parameter_ranges[0, 0]
start_time_set = True
time_stamp.append((parameter_range[0] - start_time) / 60.0)
n_cluster.append(hist)
progress_bar.finish()
if total_cluster != 0:
logging.warning('Not all clusters were selected during analysis. Analysis is therefore not exact')
if time_line_absolute:
plotting.plot_scatter_time(time_stamp, n_cluster, title='Number of cluster per event as a function of time', marker_style='o', filename=output_pdf, legend=('0 cluster', '1 cluster', '2 cluster', '3 cluster') if include_no_cluster else ('0 cluster not plotted', '1 cluster', '2 cluster', '3 cluster'))
else:
plotting.plot_scatter(time_stamp, n_cluster, title='Number of cluster per event as a function of time', x_label='time [min.]', marker_style='o', filename=output_pdf, legend=('0 cluster', '1 cluster', '2 cluster', '3 cluster') if include_no_cluster else ('0 cluster not plotted', '1 cluster', '2 cluster', '3 cluster'))
if output_file:
with tb.open_file(output_file, mode="a") as out_file_h5:
cluster_array = np.array(n_cluster)
rec_array = np.array(zip(time_stamp, cluster_array[:, 0], cluster_array[:, 1], cluster_array[:, 2], cluster_array[:, 3], cluster_array[:, 4], cluster_array[:, 5]), dtype=[('time_stamp', float), ('cluster_0', float), ('cluster_1', float), ('cluster_2', float), ('cluster_3', float), ('cluster_4', float), ('cluster_5', float)]).view(np.recarray)
try:
n_cluster_table = out_file_h5.create_table(out_file_h5.root, name='n_cluster', description=rec_array, title='Cluster per event', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
n_cluster_table[:] = rec_array
except tb.exceptions.NodeError:
logging.warning(output_file + ' has already a Beamspot note, do not overwrite existing.')
return time_stamp, n_cluster
|
python
|
def analyse_n_cluster_per_event(scan_base, include_no_cluster=False, time_line_absolute=True, combine_n_readouts=1000, chunk_size=10000000, plot_n_cluster_hists=False, output_pdf=None, output_file=None):
''' Determines the number of cluster per event as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts').
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
include_no_cluster: bool
Set to true to also consider all events without any hit.
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
'''
time_stamp = []
n_cluster = []
start_time_set = False
for data_file in scan_base:
with tb.open_file(data_file + '_interpreted.h5', mode="r+") as in_cluster_file_h5:
# get data and data pointer
meta_data_array = in_cluster_file_h5.root.meta_data[:]
cluster_table = in_cluster_file_h5.root.Cluster
# determine the event ranges to analyze (timestamp_start, start_event_number, stop_event_number)
parameter_ranges = np.column_stack((analysis_utils.get_ranges_from_array(meta_data_array['timestamp_start'][::combine_n_readouts]), analysis_utils.get_ranges_from_array(meta_data_array['event_number'][::combine_n_readouts])))
# create a event_numer index (important for speed)
analysis_utils.index_event_number(cluster_table)
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_tot_hist = False
analyze_data.create_bcid_hist = False
# variables for read speed up
index = 0 # index where to start the read out, 0 at the beginning, increased during looping
best_chunk_size = chunk_size
total_cluster = cluster_table.shape[0]
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=total_cluster, term_width=80)
progress_bar.start()
# loop over the selected events
for parameter_index, parameter_range in enumerate(parameter_ranges):
logging.debug('Analyze time stamp ' + str(parameter_range[0]) + ' and data from events = [' + str(parameter_range[2]) + ',' + str(parameter_range[3]) + '[ ' + str(int(float(float(parameter_index) / float(len(parameter_ranges)) * 100.0))) + '%')
analyze_data.reset() # resets the data of the last analysis
# loop over the cluster in the actual selected events with optimizations: determine best chunk size, start word index given
readout_cluster_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
hist = None
for clusters, index in analysis_utils.data_aligned_at_events(cluster_table, start_event_number=parameter_range[2], stop_event_number=parameter_range[3], start_index=index, chunk_size=best_chunk_size):
n_cluster_per_event = analysis_utils.get_n_cluster_in_events(clusters['event_number'])[:, 1] # array with the number of cluster per event, cluster per event are at least 1
if hist is None:
hist = np.histogram(n_cluster_per_event, bins=10, range=(0, 10))[0]
else:
hist = np.add(hist, np.histogram(n_cluster_per_event, bins=10, range=(0, 10))[0])
if include_no_cluster and parameter_range[3] is not None: # happend for the last readout
hist[0] = (parameter_range[3] - parameter_range[2]) - len(n_cluster_per_event) # add the events without any cluster
readout_cluster_len += clusters.shape[0]
total_cluster -= len(clusters)
progress_bar.update(index)
best_chunk_size = int(1.5 * readout_cluster_len) if int(1.05 * readout_cluster_len) < chunk_size else chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
if plot_n_cluster_hists:
plotting.plot_1d_hist(hist, title='Number of cluster per event at ' + str(parameter_range[0]), x_axis_title='Number of cluster', y_axis_title='#', log_y=True, filename=output_pdf)
hist = hist.astype('f4') / np.sum(hist) # calculate fraction from total numbers
if time_line_absolute:
time_stamp.append(parameter_range[0])
else:
if not start_time_set:
start_time = parameter_ranges[0, 0]
start_time_set = True
time_stamp.append((parameter_range[0] - start_time) / 60.0)
n_cluster.append(hist)
progress_bar.finish()
if total_cluster != 0:
logging.warning('Not all clusters were selected during analysis. Analysis is therefore not exact')
if time_line_absolute:
plotting.plot_scatter_time(time_stamp, n_cluster, title='Number of cluster per event as a function of time', marker_style='o', filename=output_pdf, legend=('0 cluster', '1 cluster', '2 cluster', '3 cluster') if include_no_cluster else ('0 cluster not plotted', '1 cluster', '2 cluster', '3 cluster'))
else:
plotting.plot_scatter(time_stamp, n_cluster, title='Number of cluster per event as a function of time', x_label='time [min.]', marker_style='o', filename=output_pdf, legend=('0 cluster', '1 cluster', '2 cluster', '3 cluster') if include_no_cluster else ('0 cluster not plotted', '1 cluster', '2 cluster', '3 cluster'))
if output_file:
with tb.open_file(output_file, mode="a") as out_file_h5:
cluster_array = np.array(n_cluster)
rec_array = np.array(zip(time_stamp, cluster_array[:, 0], cluster_array[:, 1], cluster_array[:, 2], cluster_array[:, 3], cluster_array[:, 4], cluster_array[:, 5]), dtype=[('time_stamp', float), ('cluster_0', float), ('cluster_1', float), ('cluster_2', float), ('cluster_3', float), ('cluster_4', float), ('cluster_5', float)]).view(np.recarray)
try:
n_cluster_table = out_file_h5.create_table(out_file_h5.root, name='n_cluster', description=rec_array, title='Cluster per event', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
n_cluster_table[:] = rec_array
except tb.exceptions.NodeError:
logging.warning(output_file + ' has already a Beamspot note, do not overwrite existing.')
return time_stamp, n_cluster
|
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Determines the number of cluster per event as a function of time. Therefore the data of a fixed number of read outs are combined ('combine_n_readouts').
Parameters
----------
scan_base: list of str
scan base names (e.g.: ['//data//SCC_50_fei4_self_trigger_scan_390', ]
include_no_cluster: bool
Set to true to also consider all events without any hit.
combine_n_readouts: int
the number of read outs to combine (e.g. 1000)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis.py#L150-L248
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis.py
|
select_hits_from_cluster_info
|
def select_hits_from_cluster_info(input_file_hits, output_file_hits, cluster_size_condition, n_cluster_condition, chunk_size=4000000):
''' Takes a hit table and stores only selected hits into a new table. The selection is done on an event base and events are selected if they have a certain number of cluster or cluster size.
To increase the analysis speed a event index for the input hit file is created first. Since a cluster hit table can be created to this way of hit selection is
not needed anymore.
Parameters
----------
input_file_hits: str
the input file name with hits
output_file_hits: str
the output file name for the hits
cluster_size_condition: str
the cluster size condition to select events (e.g.: 'cluster_size_condition <= 2')
n_cluster_condition: str
the number of cluster in a event ((e.g.: 'n_cluster_condition == 1')
'''
logging.info('Write hits of events from ' + str(input_file_hits) + ' with ' + cluster_size_condition + ' and ' + n_cluster_condition + ' into ' + str(output_file_hits))
with tb.open_file(input_file_hits, mode="r+") as in_hit_file_h5:
analysis_utils.index_event_number(in_hit_file_h5.root.Hits)
analysis_utils.index_event_number(in_hit_file_h5.root.Cluster)
with tb.open_file(output_file_hits, mode="w") as out_hit_file_h5:
hit_table_out = out_hit_file_h5.create_table(out_hit_file_h5.root, name='Hits', description=data_struct.HitInfoTable, title='hit_data', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
cluster_table = in_hit_file_h5.root.Cluster
last_word_number = 0
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=cluster_table.shape[0], term_width=80)
progress_bar.start()
for data, index in analysis_utils.data_aligned_at_events(cluster_table, chunk_size=chunk_size):
selected_events_1 = analysis_utils.get_events_with_cluster_size(event_number=data['event_number'], cluster_size=data['size'], condition=cluster_size_condition) # select the events with clusters of a certain size
selected_events_2 = analysis_utils.get_events_with_n_cluster(event_number=data['event_number'], condition=n_cluster_condition) # select the events with a certain cluster number
selected_events = analysis_utils.get_events_in_both_arrays(selected_events_1, selected_events_2) # select events with both conditions above
logging.debug('Selected ' + str(len(selected_events)) + ' events with ' + n_cluster_condition + ' and ' + cluster_size_condition)
last_word_number = analysis_utils.write_hits_in_events(hit_table_in=in_hit_file_h5.root.Hits, hit_table_out=hit_table_out, events=selected_events, start_hit_word=last_word_number) # write the hits of the selected events into a new table
progress_bar.update(index)
progress_bar.finish()
in_hit_file_h5.root.meta_data.copy(out_hit_file_h5.root)
|
python
|
def select_hits_from_cluster_info(input_file_hits, output_file_hits, cluster_size_condition, n_cluster_condition, chunk_size=4000000):
''' Takes a hit table and stores only selected hits into a new table. The selection is done on an event base and events are selected if they have a certain number of cluster or cluster size.
To increase the analysis speed a event index for the input hit file is created first. Since a cluster hit table can be created to this way of hit selection is
not needed anymore.
Parameters
----------
input_file_hits: str
the input file name with hits
output_file_hits: str
the output file name for the hits
cluster_size_condition: str
the cluster size condition to select events (e.g.: 'cluster_size_condition <= 2')
n_cluster_condition: str
the number of cluster in a event ((e.g.: 'n_cluster_condition == 1')
'''
logging.info('Write hits of events from ' + str(input_file_hits) + ' with ' + cluster_size_condition + ' and ' + n_cluster_condition + ' into ' + str(output_file_hits))
with tb.open_file(input_file_hits, mode="r+") as in_hit_file_h5:
analysis_utils.index_event_number(in_hit_file_h5.root.Hits)
analysis_utils.index_event_number(in_hit_file_h5.root.Cluster)
with tb.open_file(output_file_hits, mode="w") as out_hit_file_h5:
hit_table_out = out_hit_file_h5.create_table(out_hit_file_h5.root, name='Hits', description=data_struct.HitInfoTable, title='hit_data', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
cluster_table = in_hit_file_h5.root.Cluster
last_word_number = 0
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=cluster_table.shape[0], term_width=80)
progress_bar.start()
for data, index in analysis_utils.data_aligned_at_events(cluster_table, chunk_size=chunk_size):
selected_events_1 = analysis_utils.get_events_with_cluster_size(event_number=data['event_number'], cluster_size=data['size'], condition=cluster_size_condition) # select the events with clusters of a certain size
selected_events_2 = analysis_utils.get_events_with_n_cluster(event_number=data['event_number'], condition=n_cluster_condition) # select the events with a certain cluster number
selected_events = analysis_utils.get_events_in_both_arrays(selected_events_1, selected_events_2) # select events with both conditions above
logging.debug('Selected ' + str(len(selected_events)) + ' events with ' + n_cluster_condition + ' and ' + cluster_size_condition)
last_word_number = analysis_utils.write_hits_in_events(hit_table_in=in_hit_file_h5.root.Hits, hit_table_out=hit_table_out, events=selected_events, start_hit_word=last_word_number) # write the hits of the selected events into a new table
progress_bar.update(index)
progress_bar.finish()
in_hit_file_h5.root.meta_data.copy(out_hit_file_h5.root)
|
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input_file_hits: str
the input file name with hits
output_file_hits: str
the output file name for the hits
cluster_size_condition: str
the cluster size condition to select events (e.g.: 'cluster_size_condition <= 2')
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the number of cluster in a event ((e.g.: 'n_cluster_condition == 1')
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis.py#L251-L285
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis.py
|
select_hits
|
def select_hits(input_file_hits, output_file_hits, condition=None, cluster_size_condition=None, n_cluster_condition=None, chunk_size=5000000):
''' Takes a hit table and stores only selected hits into a new table. The selection of hits is done with a numexp string. Only if
this expression evaluates to true the hit is taken. One can also select hits from cluster conditions. This selection is done
on an event basis, meaning events are selected where the cluster condition is true and then hits of these events are taken.
Parameters
----------
input_file_hits: str
the input file name with hits
output_file_hits: str
the output file name for the hits
condition: str
Numexpr string to select hits (e.g.: '(relative_BCID == 6) & (column == row)')
All hit infos can be used (column, row, ...)
cluster_size_condition: int
Hit of events with the given cluster size are selected.
n_cluster_condition: int
Hit of events with the given cluster number are selected.
'''
logging.info('Write hits with ' + condition + ' into ' + str(output_file_hits))
if cluster_size_condition is None and n_cluster_condition is None: # no cluster cuts are done
with tb.open_file(input_file_hits, mode="r+") as in_hit_file_h5:
analysis_utils.index_event_number(in_hit_file_h5.root.Hits) # create event index for faster selection
with tb.open_file(output_file_hits, mode="w") as out_hit_file_h5:
hit_table_out = out_hit_file_h5.create_table(out_hit_file_h5.root, name='Hits', description=data_struct.HitInfoTable, title='hit_data', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
analysis_utils.write_hits_in_event_range(hit_table_in=in_hit_file_h5.root.Hits, hit_table_out=hit_table_out, condition=condition) # write the hits of the selected events into a new table
in_hit_file_h5.root.meta_data.copy(out_hit_file_h5.root) # copy meta_data note to new file
else:
with tb.open_file(input_file_hits, mode="r+") as in_hit_file_h5: # open file with hit/cluster data with r+ to be able to create index
analysis_utils.index_event_number(in_hit_file_h5.root.Hits) # create event index for faster selection
analysis_utils.index_event_number(in_hit_file_h5.root.Cluster) # create event index for faster selection
with tb.open_file(output_file_hits, mode="w") as out_hit_file_h5:
hit_table_out = out_hit_file_h5.create_table(out_hit_file_h5.root, name='Hits', description=data_struct.HitInfoTable, title='hit_data', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
cluster_table = in_hit_file_h5.root.Cluster
last_word_number = 0
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=cluster_table.shape[0], term_width=80)
progress_bar.start()
for data, index in analysis_utils.data_aligned_at_events(cluster_table, chunk_size=chunk_size):
if cluster_size_condition is not None:
selected_events = analysis_utils.get_events_with_cluster_size(event_number=data['event_number'], cluster_size=data['size'], condition='cluster_size == ' + str(cluster_size_condition)) # select the events with only 1 hit cluster
if n_cluster_condition is not None:
selected_events_2 = analysis_utils.get_events_with_n_cluster(event_number=data['event_number'], condition='n_cluster == ' + str(n_cluster_condition)) # select the events with only 1 cluster
selected_events = selected_events[analysis_utils.in1d_events(selected_events, selected_events_2)] # select events with the first two conditions above
elif n_cluster_condition is not None:
selected_events = analysis_utils.get_events_with_n_cluster(event_number=data['event_number'], condition='n_cluster == ' + str(n_cluster_condition))
else:
raise RuntimeError('Cannot understand cluster selection criterion')
last_word_number = analysis_utils.write_hits_in_events(hit_table_in=in_hit_file_h5.root.Hits, hit_table_out=hit_table_out, events=selected_events, start_hit_word=last_word_number, condition=condition, chunk_size=chunk_size) # write the hits of the selected events into a new table
progress_bar.update(index)
progress_bar.finish()
in_hit_file_h5.root.meta_data.copy(out_hit_file_h5.root)
|
python
|
def select_hits(input_file_hits, output_file_hits, condition=None, cluster_size_condition=None, n_cluster_condition=None, chunk_size=5000000):
''' Takes a hit table and stores only selected hits into a new table. The selection of hits is done with a numexp string. Only if
this expression evaluates to true the hit is taken. One can also select hits from cluster conditions. This selection is done
on an event basis, meaning events are selected where the cluster condition is true and then hits of these events are taken.
Parameters
----------
input_file_hits: str
the input file name with hits
output_file_hits: str
the output file name for the hits
condition: str
Numexpr string to select hits (e.g.: '(relative_BCID == 6) & (column == row)')
All hit infos can be used (column, row, ...)
cluster_size_condition: int
Hit of events with the given cluster size are selected.
n_cluster_condition: int
Hit of events with the given cluster number are selected.
'''
logging.info('Write hits with ' + condition + ' into ' + str(output_file_hits))
if cluster_size_condition is None and n_cluster_condition is None: # no cluster cuts are done
with tb.open_file(input_file_hits, mode="r+") as in_hit_file_h5:
analysis_utils.index_event_number(in_hit_file_h5.root.Hits) # create event index for faster selection
with tb.open_file(output_file_hits, mode="w") as out_hit_file_h5:
hit_table_out = out_hit_file_h5.create_table(out_hit_file_h5.root, name='Hits', description=data_struct.HitInfoTable, title='hit_data', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
analysis_utils.write_hits_in_event_range(hit_table_in=in_hit_file_h5.root.Hits, hit_table_out=hit_table_out, condition=condition) # write the hits of the selected events into a new table
in_hit_file_h5.root.meta_data.copy(out_hit_file_h5.root) # copy meta_data note to new file
else:
with tb.open_file(input_file_hits, mode="r+") as in_hit_file_h5: # open file with hit/cluster data with r+ to be able to create index
analysis_utils.index_event_number(in_hit_file_h5.root.Hits) # create event index for faster selection
analysis_utils.index_event_number(in_hit_file_h5.root.Cluster) # create event index for faster selection
with tb.open_file(output_file_hits, mode="w") as out_hit_file_h5:
hit_table_out = out_hit_file_h5.create_table(out_hit_file_h5.root, name='Hits', description=data_struct.HitInfoTable, title='hit_data', filters=tb.Filters(complib='blosc', complevel=5, fletcher32=False))
cluster_table = in_hit_file_h5.root.Cluster
last_word_number = 0
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=cluster_table.shape[0], term_width=80)
progress_bar.start()
for data, index in analysis_utils.data_aligned_at_events(cluster_table, chunk_size=chunk_size):
if cluster_size_condition is not None:
selected_events = analysis_utils.get_events_with_cluster_size(event_number=data['event_number'], cluster_size=data['size'], condition='cluster_size == ' + str(cluster_size_condition)) # select the events with only 1 hit cluster
if n_cluster_condition is not None:
selected_events_2 = analysis_utils.get_events_with_n_cluster(event_number=data['event_number'], condition='n_cluster == ' + str(n_cluster_condition)) # select the events with only 1 cluster
selected_events = selected_events[analysis_utils.in1d_events(selected_events, selected_events_2)] # select events with the first two conditions above
elif n_cluster_condition is not None:
selected_events = analysis_utils.get_events_with_n_cluster(event_number=data['event_number'], condition='n_cluster == ' + str(n_cluster_condition))
else:
raise RuntimeError('Cannot understand cluster selection criterion')
last_word_number = analysis_utils.write_hits_in_events(hit_table_in=in_hit_file_h5.root.Hits, hit_table_out=hit_table_out, events=selected_events, start_hit_word=last_word_number, condition=condition, chunk_size=chunk_size) # write the hits of the selected events into a new table
progress_bar.update(index)
progress_bar.finish()
in_hit_file_h5.root.meta_data.copy(out_hit_file_h5.root)
|
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Takes a hit table and stores only selected hits into a new table. The selection of hits is done with a numexp string. Only if
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on an event basis, meaning events are selected where the cluster condition is true and then hits of these events are taken.
Parameters
----------
input_file_hits: str
the input file name with hits
output_file_hits: str
the output file name for the hits
condition: str
Numexpr string to select hits (e.g.: '(relative_BCID == 6) & (column == row)')
All hit infos can be used (column, row, ...)
cluster_size_condition: int
Hit of events with the given cluster size are selected.
n_cluster_condition: int
Hit of events with the given cluster number are selected.
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis.py#L288-L338
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis.py
|
analyze_cluster_size_per_scan_parameter
|
def analyze_cluster_size_per_scan_parameter(input_file_hits, output_file_cluster_size, parameter='GDAC', max_chunk_size=10000000, overwrite_output_files=False, output_pdf=None):
''' This method takes multiple hit files and determines the cluster size for different scan parameter values of
Parameters
----------
input_files_hits: string
output_file_cluster_size: string
The data file with the results
parameter: string
The name of the parameter to separate the data into (e.g.: PlsrDAC)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
overwrite_output_files: bool
Set to true to overwrite the output file if it already exists
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen, if False nothing is printed
'''
logging.info('Analyze the cluster sizes for different ' + parameter + ' settings for ' + input_file_hits)
if os.path.isfile(output_file_cluster_size) and not overwrite_output_files: # skip analysis if already done
logging.info('Analyzed cluster size file ' + output_file_cluster_size + ' already exists. Skip cluster size analysis.')
else:
with tb.open_file(output_file_cluster_size, mode="w") as out_file_h5: # file to write the data into
filter_table = tb.Filters(complib='blosc', complevel=5, fletcher32=False) # compression of the written data
parameter_goup = out_file_h5.create_group(out_file_h5.root, parameter, title=parameter) # note to store the data
cluster_size_total = None # final array for the cluster size per GDAC
with tb.open_file(input_file_hits, mode="r+") as in_hit_file_h5: # open the actual hit file
meta_data_array = in_hit_file_h5.root.meta_data[:]
scan_parameter = analysis_utils.get_scan_parameter(meta_data_array) # get the scan parameters
if scan_parameter: # if a GDAC scan parameter was used analyze the cluster size per GDAC setting
scan_parameter_values = scan_parameter[parameter] # scan parameter settings used
if len(scan_parameter_values) == 1: # only analyze per scan step if there are more than one scan step
logging.warning('The file ' + str(input_file_hits) + ' has no different ' + str(parameter) + ' parameter values. Omit analysis.')
else:
logging.info('Analyze ' + input_file_hits + ' per scan parameter ' + parameter + ' for ' + str(len(scan_parameter_values)) + ' values from ' + str(np.amin(scan_parameter_values)) + ' to ' + str(np.amax(scan_parameter_values)))
event_numbers = analysis_utils.get_meta_data_at_scan_parameter(meta_data_array, parameter)['event_number'] # get the event numbers in meta_data where the scan parameter changes
parameter_ranges = np.column_stack((scan_parameter_values, analysis_utils.get_ranges_from_array(event_numbers)))
hit_table = in_hit_file_h5.root.Hits
analysis_utils.index_event_number(hit_table)
total_hits, total_hits_2, index = 0, 0, 0
chunk_size = max_chunk_size
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_cluster_size_hist = True
analyze_data.create_cluster_tot_hist = True
analyze_data.histogram.set_no_scan_parameter() # one has to tell histogram the # of scan parameters for correct occupancy hist allocation
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=hit_table.shape[0], term_width=80)
progress_bar.start()
for parameter_index, parameter_range in enumerate(parameter_ranges): # loop over the selected events
analyze_data.reset() # resets the data of the last analysis
logging.debug('Analyze GDAC = ' + str(parameter_range[0]) + ' ' + str(int(float(float(parameter_index) / float(len(parameter_ranges)) * 100.0))) + '%')
start_event_number = parameter_range[1]
stop_event_number = parameter_range[2]
logging.debug('Data from events = [' + str(start_event_number) + ',' + str(stop_event_number) + '[')
actual_parameter_group = out_file_h5.create_group(parameter_goup, name=parameter + '_' + str(parameter_range[0]), title=parameter + '_' + str(parameter_range[0]))
# loop over the hits in the actual selected events with optimizations: variable chunk size, start word index given
readout_hit_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
for hits, index in analysis_utils.data_aligned_at_events(hit_table, start_event_number=start_event_number, stop_event_number=stop_event_number, start_index=index, chunk_size=chunk_size):
total_hits += hits.shape[0]
analyze_data.analyze_hits(hits) # analyze the selected hits in chunks
readout_hit_len += hits.shape[0]
progress_bar.update(index)
chunk_size = int(1.05 * readout_hit_len) if int(1.05 * readout_hit_len) < max_chunk_size else max_chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
if chunk_size < 50: # limit the lower chunk size, there can always be a crazy event with more than 20 hits
chunk_size = 50
# get occupancy hist
occupancy = analyze_data.histogram.get_occupancy() # just check here if histogram is consistent
# store and plot cluster size hist
cluster_size_hist = analyze_data.clusterizer.get_cluster_size_hist()
cluster_size_hist_table = out_file_h5.create_carray(actual_parameter_group, name='HistClusterSize', title='Cluster Size Histogram', atom=tb.Atom.from_dtype(cluster_size_hist.dtype), shape=cluster_size_hist.shape, filters=filter_table)
cluster_size_hist_table[:] = cluster_size_hist
if output_pdf is not False:
plotting.plot_cluster_size(hist=cluster_size_hist, title='Cluster size (' + str(np.sum(cluster_size_hist)) + ' entries) for ' + parameter + ' = ' + str(scan_parameter_values[parameter_index]), filename=output_pdf)
if cluster_size_total is None: # true if no data was appended to the array yet
cluster_size_total = cluster_size_hist
else:
cluster_size_total = np.vstack([cluster_size_total, cluster_size_hist])
total_hits_2 += np.sum(occupancy)
progress_bar.finish()
if total_hits != total_hits_2:
logging.warning('Analysis shows inconsistent number of hits. Check needed!')
logging.info('Analyzed %d hits!', total_hits)
cluster_size_total_out = out_file_h5.create_carray(out_file_h5.root, name='AllHistClusterSize', title='All Cluster Size Histograms', atom=tb.Atom.from_dtype(cluster_size_total.dtype), shape=cluster_size_total.shape, filters=filter_table)
cluster_size_total_out[:] = cluster_size_total
|
python
|
def analyze_cluster_size_per_scan_parameter(input_file_hits, output_file_cluster_size, parameter='GDAC', max_chunk_size=10000000, overwrite_output_files=False, output_pdf=None):
''' This method takes multiple hit files and determines the cluster size for different scan parameter values of
Parameters
----------
input_files_hits: string
output_file_cluster_size: string
The data file with the results
parameter: string
The name of the parameter to separate the data into (e.g.: PlsrDAC)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
overwrite_output_files: bool
Set to true to overwrite the output file if it already exists
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen, if False nothing is printed
'''
logging.info('Analyze the cluster sizes for different ' + parameter + ' settings for ' + input_file_hits)
if os.path.isfile(output_file_cluster_size) and not overwrite_output_files: # skip analysis if already done
logging.info('Analyzed cluster size file ' + output_file_cluster_size + ' already exists. Skip cluster size analysis.')
else:
with tb.open_file(output_file_cluster_size, mode="w") as out_file_h5: # file to write the data into
filter_table = tb.Filters(complib='blosc', complevel=5, fletcher32=False) # compression of the written data
parameter_goup = out_file_h5.create_group(out_file_h5.root, parameter, title=parameter) # note to store the data
cluster_size_total = None # final array for the cluster size per GDAC
with tb.open_file(input_file_hits, mode="r+") as in_hit_file_h5: # open the actual hit file
meta_data_array = in_hit_file_h5.root.meta_data[:]
scan_parameter = analysis_utils.get_scan_parameter(meta_data_array) # get the scan parameters
if scan_parameter: # if a GDAC scan parameter was used analyze the cluster size per GDAC setting
scan_parameter_values = scan_parameter[parameter] # scan parameter settings used
if len(scan_parameter_values) == 1: # only analyze per scan step if there are more than one scan step
logging.warning('The file ' + str(input_file_hits) + ' has no different ' + str(parameter) + ' parameter values. Omit analysis.')
else:
logging.info('Analyze ' + input_file_hits + ' per scan parameter ' + parameter + ' for ' + str(len(scan_parameter_values)) + ' values from ' + str(np.amin(scan_parameter_values)) + ' to ' + str(np.amax(scan_parameter_values)))
event_numbers = analysis_utils.get_meta_data_at_scan_parameter(meta_data_array, parameter)['event_number'] # get the event numbers in meta_data where the scan parameter changes
parameter_ranges = np.column_stack((scan_parameter_values, analysis_utils.get_ranges_from_array(event_numbers)))
hit_table = in_hit_file_h5.root.Hits
analysis_utils.index_event_number(hit_table)
total_hits, total_hits_2, index = 0, 0, 0
chunk_size = max_chunk_size
# initialize the analysis and set settings
analyze_data = AnalyzeRawData()
analyze_data.create_cluster_size_hist = True
analyze_data.create_cluster_tot_hist = True
analyze_data.histogram.set_no_scan_parameter() # one has to tell histogram the # of scan parameters for correct occupancy hist allocation
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=hit_table.shape[0], term_width=80)
progress_bar.start()
for parameter_index, parameter_range in enumerate(parameter_ranges): # loop over the selected events
analyze_data.reset() # resets the data of the last analysis
logging.debug('Analyze GDAC = ' + str(parameter_range[0]) + ' ' + str(int(float(float(parameter_index) / float(len(parameter_ranges)) * 100.0))) + '%')
start_event_number = parameter_range[1]
stop_event_number = parameter_range[2]
logging.debug('Data from events = [' + str(start_event_number) + ',' + str(stop_event_number) + '[')
actual_parameter_group = out_file_h5.create_group(parameter_goup, name=parameter + '_' + str(parameter_range[0]), title=parameter + '_' + str(parameter_range[0]))
# loop over the hits in the actual selected events with optimizations: variable chunk size, start word index given
readout_hit_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
for hits, index in analysis_utils.data_aligned_at_events(hit_table, start_event_number=start_event_number, stop_event_number=stop_event_number, start_index=index, chunk_size=chunk_size):
total_hits += hits.shape[0]
analyze_data.analyze_hits(hits) # analyze the selected hits in chunks
readout_hit_len += hits.shape[0]
progress_bar.update(index)
chunk_size = int(1.05 * readout_hit_len) if int(1.05 * readout_hit_len) < max_chunk_size else max_chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
if chunk_size < 50: # limit the lower chunk size, there can always be a crazy event with more than 20 hits
chunk_size = 50
# get occupancy hist
occupancy = analyze_data.histogram.get_occupancy() # just check here if histogram is consistent
# store and plot cluster size hist
cluster_size_hist = analyze_data.clusterizer.get_cluster_size_hist()
cluster_size_hist_table = out_file_h5.create_carray(actual_parameter_group, name='HistClusterSize', title='Cluster Size Histogram', atom=tb.Atom.from_dtype(cluster_size_hist.dtype), shape=cluster_size_hist.shape, filters=filter_table)
cluster_size_hist_table[:] = cluster_size_hist
if output_pdf is not False:
plotting.plot_cluster_size(hist=cluster_size_hist, title='Cluster size (' + str(np.sum(cluster_size_hist)) + ' entries) for ' + parameter + ' = ' + str(scan_parameter_values[parameter_index]), filename=output_pdf)
if cluster_size_total is None: # true if no data was appended to the array yet
cluster_size_total = cluster_size_hist
else:
cluster_size_total = np.vstack([cluster_size_total, cluster_size_hist])
total_hits_2 += np.sum(occupancy)
progress_bar.finish()
if total_hits != total_hits_2:
logging.warning('Analysis shows inconsistent number of hits. Check needed!')
logging.info('Analyzed %d hits!', total_hits)
cluster_size_total_out = out_file_h5.create_carray(out_file_h5.root, name='AllHistClusterSize', title='All Cluster Size Histograms', atom=tb.Atom.from_dtype(cluster_size_total.dtype), shape=cluster_size_total.shape, filters=filter_table)
cluster_size_total_out[:] = cluster_size_total
|
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] |
This method takes multiple hit files and determines the cluster size for different scan parameter values of
Parameters
----------
input_files_hits: string
output_file_cluster_size: string
The data file with the results
parameter: string
The name of the parameter to separate the data into (e.g.: PlsrDAC)
max_chunk_size: int
the maximum chunk size used during read, if too big memory error occurs, if too small analysis takes longer
overwrite_output_files: bool
Set to true to overwrite the output file if it already exists
output_pdf: PdfPages
PdfPages file object, if none the plot is printed to screen, if False nothing is printed
|
[
"This",
"method",
"takes",
"multiple",
"hit",
"files",
"and",
"determines",
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"cluster",
"size",
"for",
"different",
"scan",
"parameter",
"values",
"of"
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis.py#L341-L425
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis.py
|
histogram_cluster_table
|
def histogram_cluster_table(analyzed_data_file, output_file, chunk_size=10000000):
'''Reads in the cluster info table in chunks and histograms the seed pixels into one occupancy array.
The 3rd dimension of the occupancy array is the number of different scan parameters used
Parameters
----------
analyzed_data_file : string
HDF5 filename of the file containing the cluster table. If a scan parameter is given in the meta data, the occupancy histogramming is done per scan parameter step.
Returns
-------
occupancy_array: numpy.array with dimensions (col, row, #scan_parameter)
'''
with tb.open_file(analyzed_data_file, mode="r") as in_file_h5:
with tb.open_file(output_file, mode="w") as out_file_h5:
histogram = PyDataHistograming()
histogram.create_occupancy_hist(True)
scan_parameters = None
event_number_indices = None
scan_parameter_indices = None
try:
meta_data = in_file_h5.root.meta_data[:]
scan_parameters = analysis_utils.get_unique_scan_parameter_combinations(meta_data)
if scan_parameters is not None:
scan_parameter_indices = np.array(range(0, len(scan_parameters)), dtype='u4')
event_number_indices = np.ascontiguousarray(scan_parameters['event_number']).astype(np.uint64)
histogram.add_meta_event_index(event_number_indices, array_length=len(scan_parameters['event_number']))
histogram.add_scan_parameter(scan_parameter_indices)
logging.info("Add %d different scan parameter(s) for analysis", len(scan_parameters))
else:
logging.info("No scan parameter data provided")
histogram.set_no_scan_parameter()
except tb.exceptions.NoSuchNodeError:
logging.info("No meta data provided, use no scan parameter")
histogram.set_no_scan_parameter()
logging.info('Histogram cluster seeds...')
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=in_file_h5.root.Cluster.shape[0], term_width=80)
progress_bar.start()
total_cluster = 0 # to check analysis
for cluster, index in analysis_utils.data_aligned_at_events(in_file_h5.root.Cluster, chunk_size=chunk_size):
total_cluster += len(cluster)
histogram.add_cluster_seed_hits(cluster, len(cluster))
progress_bar.update(index)
progress_bar.finish()
filter_table = tb.Filters(complib='blosc', complevel=5, fletcher32=False) # compression of the written data
occupancy_array = histogram.get_occupancy().T
occupancy_array_table = out_file_h5.create_carray(out_file_h5.root, name='HistOcc', title='Occupancy Histogram', atom=tb.Atom.from_dtype(occupancy_array.dtype), shape=occupancy_array.shape, filters=filter_table)
occupancy_array_table[:] = occupancy_array
if total_cluster != np.sum(occupancy_array):
logging.warning('Analysis shows inconsistent number of cluster used. Check needed!')
in_file_h5.root.meta_data.copy(out_file_h5.root)
|
python
|
def histogram_cluster_table(analyzed_data_file, output_file, chunk_size=10000000):
'''Reads in the cluster info table in chunks and histograms the seed pixels into one occupancy array.
The 3rd dimension of the occupancy array is the number of different scan parameters used
Parameters
----------
analyzed_data_file : string
HDF5 filename of the file containing the cluster table. If a scan parameter is given in the meta data, the occupancy histogramming is done per scan parameter step.
Returns
-------
occupancy_array: numpy.array with dimensions (col, row, #scan_parameter)
'''
with tb.open_file(analyzed_data_file, mode="r") as in_file_h5:
with tb.open_file(output_file, mode="w") as out_file_h5:
histogram = PyDataHistograming()
histogram.create_occupancy_hist(True)
scan_parameters = None
event_number_indices = None
scan_parameter_indices = None
try:
meta_data = in_file_h5.root.meta_data[:]
scan_parameters = analysis_utils.get_unique_scan_parameter_combinations(meta_data)
if scan_parameters is not None:
scan_parameter_indices = np.array(range(0, len(scan_parameters)), dtype='u4')
event_number_indices = np.ascontiguousarray(scan_parameters['event_number']).astype(np.uint64)
histogram.add_meta_event_index(event_number_indices, array_length=len(scan_parameters['event_number']))
histogram.add_scan_parameter(scan_parameter_indices)
logging.info("Add %d different scan parameter(s) for analysis", len(scan_parameters))
else:
logging.info("No scan parameter data provided")
histogram.set_no_scan_parameter()
except tb.exceptions.NoSuchNodeError:
logging.info("No meta data provided, use no scan parameter")
histogram.set_no_scan_parameter()
logging.info('Histogram cluster seeds...')
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=in_file_h5.root.Cluster.shape[0], term_width=80)
progress_bar.start()
total_cluster = 0 # to check analysis
for cluster, index in analysis_utils.data_aligned_at_events(in_file_h5.root.Cluster, chunk_size=chunk_size):
total_cluster += len(cluster)
histogram.add_cluster_seed_hits(cluster, len(cluster))
progress_bar.update(index)
progress_bar.finish()
filter_table = tb.Filters(complib='blosc', complevel=5, fletcher32=False) # compression of the written data
occupancy_array = histogram.get_occupancy().T
occupancy_array_table = out_file_h5.create_carray(out_file_h5.root, name='HistOcc', title='Occupancy Histogram', atom=tb.Atom.from_dtype(occupancy_array.dtype), shape=occupancy_array.shape, filters=filter_table)
occupancy_array_table[:] = occupancy_array
if total_cluster != np.sum(occupancy_array):
logging.warning('Analysis shows inconsistent number of cluster used. Check needed!')
in_file_h5.root.meta_data.copy(out_file_h5.root)
|
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Reads in the cluster info table in chunks and histograms the seed pixels into one occupancy array.
The 3rd dimension of the occupancy array is the number of different scan parameters used
Parameters
----------
analyzed_data_file : string
HDF5 filename of the file containing the cluster table. If a scan parameter is given in the meta data, the occupancy histogramming is done per scan parameter step.
Returns
-------
occupancy_array: numpy.array with dimensions (col, row, #scan_parameter)
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis.py#L428-L482
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis.py
|
analyze_hits_per_scan_parameter
|
def analyze_hits_per_scan_parameter(analyze_data, scan_parameters=None, chunk_size=50000):
'''Takes the hit table and analyzes the hits per scan parameter
Parameters
----------
analyze_data : analysis.analyze_raw_data.AnalyzeRawData object with an opened hit file (AnalyzeRawData.out_file_h5) or a
file name with the hit data given (AnalyzeRawData._analyzed_data_file)
scan_parameters : list of strings:
The names of the scan parameters to use
chunk_size : int:
The chunk size of one hit table read. The bigger the faster. Too big causes memory errors.
Returns
-------
yields the analysis.analyze_raw_data.AnalyzeRawData for each scan parameter
'''
if analyze_data.out_file_h5 is None or analyze_data.out_file_h5.isopen == 0:
in_hit_file_h5 = tb.open_file(analyze_data._analyzed_data_file, 'r+')
close_file = True
else:
in_hit_file_h5 = analyze_data.out_file_h5
close_file = False
meta_data = in_hit_file_h5.root.meta_data[:] # get the meta data table
try:
hit_table = in_hit_file_h5.root.Hits # get the hit table
except tb.NoSuchNodeError:
logging.error('analyze_hits_per_scan_parameter needs a hit table, but no hit table found.')
return
meta_data_table_at_scan_parameter = analysis_utils.get_unique_scan_parameter_combinations(meta_data, scan_parameters=scan_parameters)
parameter_values = analysis_utils.get_scan_parameters_table_from_meta_data(meta_data_table_at_scan_parameter, scan_parameters)
event_number_ranges = analysis_utils.get_ranges_from_array(meta_data_table_at_scan_parameter['event_number']) # get the event number ranges for the different scan parameter settings
analysis_utils.index_event_number(hit_table) # create a event_numer index to select the hits by their event number fast, no needed but important for speed up
# variables for read speed up
index = 0 # index where to start the read out of the hit table, 0 at the beginning, increased during looping
best_chunk_size = chunk_size # number of hits to copy to RAM during looping, the optimal chunk size is determined during looping
# loop over the selected events
for parameter_index, (start_event_number, stop_event_number) in enumerate(event_number_ranges):
logging.info('Analyze hits for ' + str(scan_parameters) + ' = ' + str(parameter_values[parameter_index]))
analyze_data.reset() # resets the front end data of the last analysis step but not the options
readout_hit_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
# loop over the hits in the actual selected events with optimizations: determine best chunk size, start word index given
for hits, index in analysis_utils.data_aligned_at_events(hit_table, start_event_number=start_event_number, stop_event_number=stop_event_number, start_index=index, chunk_size=best_chunk_size):
analyze_data.analyze_hits(hits, scan_parameter=False) # analyze the selected hits in chunks
readout_hit_len += hits.shape[0]
best_chunk_size = int(1.5 * readout_hit_len) if int(1.05 * readout_hit_len) < chunk_size and int(1.05 * readout_hit_len) > 1e3 else chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
file_name = " ".join(re.findall("[a-zA-Z0-9]+", str(scan_parameters))) + '_' + " ".join(re.findall("[a-zA-Z0-9]+", str(parameter_values[parameter_index])))
analyze_data._create_additional_hit_data(safe_to_file=False)
analyze_data._create_additional_cluster_data(safe_to_file=False)
yield analyze_data, file_name
if close_file:
in_hit_file_h5.close()
|
python
|
def analyze_hits_per_scan_parameter(analyze_data, scan_parameters=None, chunk_size=50000):
'''Takes the hit table and analyzes the hits per scan parameter
Parameters
----------
analyze_data : analysis.analyze_raw_data.AnalyzeRawData object with an opened hit file (AnalyzeRawData.out_file_h5) or a
file name with the hit data given (AnalyzeRawData._analyzed_data_file)
scan_parameters : list of strings:
The names of the scan parameters to use
chunk_size : int:
The chunk size of one hit table read. The bigger the faster. Too big causes memory errors.
Returns
-------
yields the analysis.analyze_raw_data.AnalyzeRawData for each scan parameter
'''
if analyze_data.out_file_h5 is None or analyze_data.out_file_h5.isopen == 0:
in_hit_file_h5 = tb.open_file(analyze_data._analyzed_data_file, 'r+')
close_file = True
else:
in_hit_file_h5 = analyze_data.out_file_h5
close_file = False
meta_data = in_hit_file_h5.root.meta_data[:] # get the meta data table
try:
hit_table = in_hit_file_h5.root.Hits # get the hit table
except tb.NoSuchNodeError:
logging.error('analyze_hits_per_scan_parameter needs a hit table, but no hit table found.')
return
meta_data_table_at_scan_parameter = analysis_utils.get_unique_scan_parameter_combinations(meta_data, scan_parameters=scan_parameters)
parameter_values = analysis_utils.get_scan_parameters_table_from_meta_data(meta_data_table_at_scan_parameter, scan_parameters)
event_number_ranges = analysis_utils.get_ranges_from_array(meta_data_table_at_scan_parameter['event_number']) # get the event number ranges for the different scan parameter settings
analysis_utils.index_event_number(hit_table) # create a event_numer index to select the hits by their event number fast, no needed but important for speed up
# variables for read speed up
index = 0 # index where to start the read out of the hit table, 0 at the beginning, increased during looping
best_chunk_size = chunk_size # number of hits to copy to RAM during looping, the optimal chunk size is determined during looping
# loop over the selected events
for parameter_index, (start_event_number, stop_event_number) in enumerate(event_number_ranges):
logging.info('Analyze hits for ' + str(scan_parameters) + ' = ' + str(parameter_values[parameter_index]))
analyze_data.reset() # resets the front end data of the last analysis step but not the options
readout_hit_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
# loop over the hits in the actual selected events with optimizations: determine best chunk size, start word index given
for hits, index in analysis_utils.data_aligned_at_events(hit_table, start_event_number=start_event_number, stop_event_number=stop_event_number, start_index=index, chunk_size=best_chunk_size):
analyze_data.analyze_hits(hits, scan_parameter=False) # analyze the selected hits in chunks
readout_hit_len += hits.shape[0]
best_chunk_size = int(1.5 * readout_hit_len) if int(1.05 * readout_hit_len) < chunk_size and int(1.05 * readout_hit_len) > 1e3 else chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
file_name = " ".join(re.findall("[a-zA-Z0-9]+", str(scan_parameters))) + '_' + " ".join(re.findall("[a-zA-Z0-9]+", str(parameter_values[parameter_index])))
analyze_data._create_additional_hit_data(safe_to_file=False)
analyze_data._create_additional_cluster_data(safe_to_file=False)
yield analyze_data, file_name
if close_file:
in_hit_file_h5.close()
|
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"(",
")"
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Takes the hit table and analyzes the hits per scan parameter
Parameters
----------
analyze_data : analysis.analyze_raw_data.AnalyzeRawData object with an opened hit file (AnalyzeRawData.out_file_h5) or a
file name with the hit data given (AnalyzeRawData._analyzed_data_file)
scan_parameters : list of strings:
The names of the scan parameters to use
chunk_size : int:
The chunk size of one hit table read. The bigger the faster. Too big causes memory errors.
Returns
-------
yields the analysis.analyze_raw_data.AnalyzeRawData for each scan parameter
|
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"hits",
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"parameter"
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis.py#L485-L541
|
SiLab-Bonn/pyBAR
|
pybar/scans/calibrate_plsr_dac_transient.py
|
interpret_data_from_tektronix
|
def interpret_data_from_tektronix(preamble, data):
''' Interprets raw data from Tektronix
returns: lists of x, y values in seconds/volt'''
# Y mode ("WFMPRE:PT_FMT"):
# Xn = XZEro + XINcr (n - PT_Off)
# Yn = YZEro + YMUlt (yn - YOFf)
voltage = np.array(data, dtype=np.float)
meta_data = preamble.split(',')[5].split(';')
time_unit = meta_data[3][1:-1]
XZEro = float(meta_data[5])
XINcr = float(meta_data[4])
PT_Off = float(meta_data[6])
voltage_unit = meta_data[7][1:-1]
YZEro = float(meta_data[10])
YMUlt = float(meta_data[8])
YOFf = float(meta_data[9])
time = XZEro + XINcr * (np.arange(0, voltage.size) - PT_Off)
voltage = YZEro + YMUlt * (voltage - YOFf)
return time, voltage, time_unit, voltage_unit
|
python
|
def interpret_data_from_tektronix(preamble, data):
''' Interprets raw data from Tektronix
returns: lists of x, y values in seconds/volt'''
# Y mode ("WFMPRE:PT_FMT"):
# Xn = XZEro + XINcr (n - PT_Off)
# Yn = YZEro + YMUlt (yn - YOFf)
voltage = np.array(data, dtype=np.float)
meta_data = preamble.split(',')[5].split(';')
time_unit = meta_data[3][1:-1]
XZEro = float(meta_data[5])
XINcr = float(meta_data[4])
PT_Off = float(meta_data[6])
voltage_unit = meta_data[7][1:-1]
YZEro = float(meta_data[10])
YMUlt = float(meta_data[8])
YOFf = float(meta_data[9])
time = XZEro + XINcr * (np.arange(0, voltage.size) - PT_Off)
voltage = YZEro + YMUlt * (voltage - YOFf)
return time, voltage, time_unit, voltage_unit
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/scans/calibrate_plsr_dac_transient.py#L24-L42
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register_utils.py
|
read_chip_sn
|
def read_chip_sn(self):
'''Reading Chip S/N
Note
----
Bits [MSB-LSB] | [15] | [14-6] | [5-0]
Content | reserved | wafer number | chip number
'''
commands = []
commands.extend(self.register.get_commands("ConfMode"))
self.register_utils.send_commands(commands)
with self.readout(fill_buffer=True, callback=None, errback=None):
if self.register.fei4b:
commands = []
self.register.set_global_register_value('Efuse_Sense', 1)
commands.extend(self.register.get_commands("WrRegister", name=['Efuse_Sense']))
commands.extend(self.register.get_commands("GlobalPulse", Width=0))
self.register.set_global_register_value('Efuse_Sense', 0)
commands.extend(self.register.get_commands("WrRegister", name=['Efuse_Sense']))
self.register_utils.send_commands(commands)
commands = []
self.register.set_global_register_value('Conf_AddrEnable', 1)
commands.extend(self.register.get_commands("WrRegister", name=['Conf_AddrEnable']))
chip_sn_address = self.register.get_global_register_attributes("addresses", name="Chip_SN")
commands.extend(self.register.get_commands("RdRegister", addresses=chip_sn_address))
self.register_utils.send_commands(commands)
data = self.read_data()
if data.shape[0] == 0:
logging.error('Chip S/N: No data')
return
read_values = []
for index, word in enumerate(np.nditer(data)):
fei4_data_word = FEI4Record(word, self.register.chip_flavor)
if fei4_data_word == 'AR':
fei4_next_data_word = FEI4Record(data[index + 1], self.register.chip_flavor)
if fei4_next_data_word == 'VR':
read_value = fei4_next_data_word['value']
read_values.append(read_value)
# commands = []
# commands.extend(self.register.get_commands("RunMode"))
# self.register_utils.send_commands(commands)
if len(read_values) == 0:
logging.error('No Chip S/N was found')
elif len(read_values) == 1:
logging.info('Chip S/N: %d', read_values[0])
else:
logging.warning('Ambiguous Chip S/N: %s', read_values)
|
python
|
def read_chip_sn(self):
'''Reading Chip S/N
Note
----
Bits [MSB-LSB] | [15] | [14-6] | [5-0]
Content | reserved | wafer number | chip number
'''
commands = []
commands.extend(self.register.get_commands("ConfMode"))
self.register_utils.send_commands(commands)
with self.readout(fill_buffer=True, callback=None, errback=None):
if self.register.fei4b:
commands = []
self.register.set_global_register_value('Efuse_Sense', 1)
commands.extend(self.register.get_commands("WrRegister", name=['Efuse_Sense']))
commands.extend(self.register.get_commands("GlobalPulse", Width=0))
self.register.set_global_register_value('Efuse_Sense', 0)
commands.extend(self.register.get_commands("WrRegister", name=['Efuse_Sense']))
self.register_utils.send_commands(commands)
commands = []
self.register.set_global_register_value('Conf_AddrEnable', 1)
commands.extend(self.register.get_commands("WrRegister", name=['Conf_AddrEnable']))
chip_sn_address = self.register.get_global_register_attributes("addresses", name="Chip_SN")
commands.extend(self.register.get_commands("RdRegister", addresses=chip_sn_address))
self.register_utils.send_commands(commands)
data = self.read_data()
if data.shape[0] == 0:
logging.error('Chip S/N: No data')
return
read_values = []
for index, word in enumerate(np.nditer(data)):
fei4_data_word = FEI4Record(word, self.register.chip_flavor)
if fei4_data_word == 'AR':
fei4_next_data_word = FEI4Record(data[index + 1], self.register.chip_flavor)
if fei4_next_data_word == 'VR':
read_value = fei4_next_data_word['value']
read_values.append(read_value)
# commands = []
# commands.extend(self.register.get_commands("RunMode"))
# self.register_utils.send_commands(commands)
if len(read_values) == 0:
logging.error('No Chip S/N was found')
elif len(read_values) == 1:
logging.info('Chip S/N: %d', read_values[0])
else:
logging.warning('Ambiguous Chip S/N: %s', read_values)
|
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Reading Chip S/N
Note
----
Bits [MSB-LSB] | [15] | [14-6] | [5-0]
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|
[
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register_utils.py#L264-L313
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register_utils.py
|
read_global_register
|
def read_global_register(self, name, overwrite_config=False):
'''The function reads the global register, interprets the data and returns the register value.
Parameters
----------
name : register name
overwrite_config : bool
The read values overwrite the config in RAM if true.
Returns
-------
register value
'''
self.register_utils.send_commands(self.register.get_commands("ConfMode"))
with self.readout(fill_buffer=True, callback=None, errback=None):
commands = []
commands.extend(self.register.get_commands("RdRegister", name=name))
self.register_utils.send_commands(commands)
data = self.read_data()
register_object = self.register.get_global_register_objects(name=[name])[0]
value = BitLogic(register_object['addresses'] * 16)
index = 0
vr_count = 0
for word in np.nditer(data):
fei4_data_word = FEI4Record(word, self.register.chip_flavor)
if fei4_data_word == 'AR':
address_value = fei4_data_word['address']
if address_value != register_object['address'] + index:
raise Exception('Unexpected address from Address Record: read: %d, expected: %d' % (address_value, register_object['address'] + index))
elif fei4_data_word == 'VR':
vr_count += 1
if vr_count >= 2:
raise RuntimeError("Read more than 2 value records")
read_value = BitLogic.from_value(fei4_data_word['value'], size=16)
if register_object['register_littleendian']:
read_value.reverse()
value[index * 16 + 15:index * 16] = read_value
index += 1
value = value[register_object['bitlength'] + register_object['offset'] - 1:register_object['offset']]
if register_object['littleendian']:
value.reverse()
value = value.tovalue()
if overwrite_config:
self.register.set_global_register(name, value)
return value
|
python
|
def read_global_register(self, name, overwrite_config=False):
'''The function reads the global register, interprets the data and returns the register value.
Parameters
----------
name : register name
overwrite_config : bool
The read values overwrite the config in RAM if true.
Returns
-------
register value
'''
self.register_utils.send_commands(self.register.get_commands("ConfMode"))
with self.readout(fill_buffer=True, callback=None, errback=None):
commands = []
commands.extend(self.register.get_commands("RdRegister", name=name))
self.register_utils.send_commands(commands)
data = self.read_data()
register_object = self.register.get_global_register_objects(name=[name])[0]
value = BitLogic(register_object['addresses'] * 16)
index = 0
vr_count = 0
for word in np.nditer(data):
fei4_data_word = FEI4Record(word, self.register.chip_flavor)
if fei4_data_word == 'AR':
address_value = fei4_data_word['address']
if address_value != register_object['address'] + index:
raise Exception('Unexpected address from Address Record: read: %d, expected: %d' % (address_value, register_object['address'] + index))
elif fei4_data_word == 'VR':
vr_count += 1
if vr_count >= 2:
raise RuntimeError("Read more than 2 value records")
read_value = BitLogic.from_value(fei4_data_word['value'], size=16)
if register_object['register_littleendian']:
read_value.reverse()
value[index * 16 + 15:index * 16] = read_value
index += 1
value = value[register_object['bitlength'] + register_object['offset'] - 1:register_object['offset']]
if register_object['littleendian']:
value.reverse()
value = value.tovalue()
if overwrite_config:
self.register.set_global_register(name, value)
return value
|
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register value
|
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train
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register_utils.py#L540-L586
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register_utils.py
|
read_pixel_register
|
def read_pixel_register(self, pix_regs=None, dcs=range(40), overwrite_config=False):
'''The function reads the pixel register, interprets the data and returns a masked numpy arrays with the data for the chosen pixel register.
Pixels without any data are masked.
Parameters
----------
pix_regs : iterable, string
List of pixel register to read (e.g. Enable, C_High, ...).
If None all are read: "EnableDigInj", "Imon", "Enable", "C_High", "C_Low", "TDAC", "FDAC"
dcs : iterable, int
List of double columns to read.
overwrite_config : bool
The read values overwrite the config in RAM if true.
Returns
-------
list of masked numpy.ndarrays
'''
if pix_regs is None:
pix_regs = ["EnableDigInj", "Imon", "Enable", "C_High", "C_Low", "TDAC", "FDAC"]
self.register_utils.send_commands(self.register.get_commands("ConfMode"))
result = []
for pix_reg in pix_regs:
pixel_data = np.ma.masked_array(np.zeros(shape=(80, 336), dtype=np.uint32), mask=True) # the result pixel array, only pixel with data are not masked
for dc in dcs:
with self.readout(fill_buffer=True, callback=None, errback=None):
self.register_utils.send_commands(self.register.get_commands("RdFrontEnd", name=[pix_reg], dcs=[dc]))
data = self.read_data()
interpret_pixel_data(data, dc, pixel_data, invert=False if pix_reg == "EnableDigInj" else True)
if overwrite_config:
self.register.set_pixel_register(pix_reg, pixel_data.data)
result.append(pixel_data)
return result
|
python
|
def read_pixel_register(self, pix_regs=None, dcs=range(40), overwrite_config=False):
'''The function reads the pixel register, interprets the data and returns a masked numpy arrays with the data for the chosen pixel register.
Pixels without any data are masked.
Parameters
----------
pix_regs : iterable, string
List of pixel register to read (e.g. Enable, C_High, ...).
If None all are read: "EnableDigInj", "Imon", "Enable", "C_High", "C_Low", "TDAC", "FDAC"
dcs : iterable, int
List of double columns to read.
overwrite_config : bool
The read values overwrite the config in RAM if true.
Returns
-------
list of masked numpy.ndarrays
'''
if pix_regs is None:
pix_regs = ["EnableDigInj", "Imon", "Enable", "C_High", "C_Low", "TDAC", "FDAC"]
self.register_utils.send_commands(self.register.get_commands("ConfMode"))
result = []
for pix_reg in pix_regs:
pixel_data = np.ma.masked_array(np.zeros(shape=(80, 336), dtype=np.uint32), mask=True) # the result pixel array, only pixel with data are not masked
for dc in dcs:
with self.readout(fill_buffer=True, callback=None, errback=None):
self.register_utils.send_commands(self.register.get_commands("RdFrontEnd", name=[pix_reg], dcs=[dc]))
data = self.read_data()
interpret_pixel_data(data, dc, pixel_data, invert=False if pix_reg == "EnableDigInj" else True)
if overwrite_config:
self.register.set_pixel_register(pix_reg, pixel_data.data)
result.append(pixel_data)
return result
|
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List of pixel register to read (e.g. Enable, C_High, ...).
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dcs : iterable, int
List of double columns to read.
overwrite_config : bool
The read values overwrite the config in RAM if true.
Returns
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list of masked numpy.ndarrays
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train
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register_utils.py#L598-L633
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register_utils.py
|
is_fe_ready
|
def is_fe_ready(self):
'''Get FEI4 status of module.
If FEI4 is not ready, resetting service records is necessary to bring the FEI4 to a defined state.
Returns
-------
value : bool
True if FEI4 is ready, False if the FEI4 was powered up recently and is not ready.
'''
with self.readout(fill_buffer=True, callback=None, errback=None):
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("RdRegister", address=[1]))
# commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
data = self.read_data()
if len(data) != 0:
return True if FEI4Record(data[-1], self.register.chip_flavor) == 'VR' else False
else:
return False
|
python
|
def is_fe_ready(self):
'''Get FEI4 status of module.
If FEI4 is not ready, resetting service records is necessary to bring the FEI4 to a defined state.
Returns
-------
value : bool
True if FEI4 is ready, False if the FEI4 was powered up recently and is not ready.
'''
with self.readout(fill_buffer=True, callback=None, errback=None):
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("RdRegister", address=[1]))
# commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
data = self.read_data()
if len(data) != 0:
return True if FEI4Record(data[-1], self.register.chip_flavor) == 'VR' else False
else:
return False
|
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train
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register_utils.py#L636-L657
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register_utils.py
|
invert_pixel_mask
|
def invert_pixel_mask(mask):
'''Invert pixel mask (0->1, 1(and greater)->0).
Parameters
----------
mask : array-like
Mask.
Returns
-------
inverted_mask : array-like
Inverted Mask.
'''
inverted_mask = np.ones(shape=(80, 336), dtype=np.dtype('>u1'))
inverted_mask[mask >= 1] = 0
return inverted_mask
|
python
|
def invert_pixel_mask(mask):
'''Invert pixel mask (0->1, 1(and greater)->0).
Parameters
----------
mask : array-like
Mask.
Returns
-------
inverted_mask : array-like
Inverted Mask.
'''
inverted_mask = np.ones(shape=(80, 336), dtype=np.dtype('>u1'))
inverted_mask[mask >= 1] = 0
return inverted_mask
|
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train
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register_utils.py#L660-L675
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register_utils.py
|
make_pixel_mask
|
def make_pixel_mask(steps, shift, default=0, value=1, enable_columns=None, mask=None):
'''Generate pixel mask.
Parameters
----------
steps : int
Number of mask steps, e.g. steps=3 (every third pixel is enabled), steps=336 (one pixel per column), steps=672 (one pixel per double column).
shift : int
Shift mask by given value to the bottom (towards higher row numbers). From 0 to (steps - 1).
default : int
Value of pixels that are not selected by the mask.
value : int
Value of pixels that are selected by the mask.
enable_columns : list
List of columns where the shift mask will be applied. List elements can range from 1 to 80.
mask : array_like
Additional mask. Must be convertible to an array of booleans with the same shape as mask array. True indicates a masked (i.e. invalid) data. Masked pixels will be set to default value.
Returns
-------
mask_array : numpy.ndarray
Mask array.
Usage
-----
shift_mask = 'enable'
steps = 3 # three step mask
for mask_step in range(steps):
commands = []
commands.extend(self.register.get_commands("ConfMode"))
mask_array = make_pixel_mask(steps=steps, step=mask_step)
self.register.set_pixel_register_value(shift_mask, mask_array)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=True, name=shift_mask))
self.register_utils.send_commands(commands)
# do something here
'''
shape = (80, 336)
# value = np.zeros(dimension, dtype = np.uint8)
mask_array = np.full(shape, default, dtype=np.uint8)
# FE columns and rows are starting from 1
if enable_columns:
odd_columns = [odd - 1 for odd in enable_columns if odd % 2 != 0]
even_columns = [even - 1 for even in enable_columns if even % 2 == 0]
else:
odd_columns = range(0, 80, 2)
even_columns = range(1, 80, 2)
odd_rows = np.arange(shift % steps, 336, steps)
even_row_offset = ((steps // 2) + shift) % steps # // integer devision
even_rows = np.arange(even_row_offset, 336, steps)
if odd_columns:
odd_col_rows = itertools.product(odd_columns, odd_rows) # get any combination of column and row, no for loop needed
for odd_col_row in odd_col_rows:
mask_array[odd_col_row[0], odd_col_row[1]] = value # advanced indexing
if even_columns:
even_col_rows = itertools.product(even_columns, even_rows)
for even_col_row in even_col_rows:
mask_array[even_col_row[0], even_col_row[1]] = value
if mask is not None:
mask_array = np.ma.array(mask_array, mask=mask, fill_value=default)
mask_array = mask_array.filled()
return mask_array
|
python
|
def make_pixel_mask(steps, shift, default=0, value=1, enable_columns=None, mask=None):
'''Generate pixel mask.
Parameters
----------
steps : int
Number of mask steps, e.g. steps=3 (every third pixel is enabled), steps=336 (one pixel per column), steps=672 (one pixel per double column).
shift : int
Shift mask by given value to the bottom (towards higher row numbers). From 0 to (steps - 1).
default : int
Value of pixels that are not selected by the mask.
value : int
Value of pixels that are selected by the mask.
enable_columns : list
List of columns where the shift mask will be applied. List elements can range from 1 to 80.
mask : array_like
Additional mask. Must be convertible to an array of booleans with the same shape as mask array. True indicates a masked (i.e. invalid) data. Masked pixels will be set to default value.
Returns
-------
mask_array : numpy.ndarray
Mask array.
Usage
-----
shift_mask = 'enable'
steps = 3 # three step mask
for mask_step in range(steps):
commands = []
commands.extend(self.register.get_commands("ConfMode"))
mask_array = make_pixel_mask(steps=steps, step=mask_step)
self.register.set_pixel_register_value(shift_mask, mask_array)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=True, name=shift_mask))
self.register_utils.send_commands(commands)
# do something here
'''
shape = (80, 336)
# value = np.zeros(dimension, dtype = np.uint8)
mask_array = np.full(shape, default, dtype=np.uint8)
# FE columns and rows are starting from 1
if enable_columns:
odd_columns = [odd - 1 for odd in enable_columns if odd % 2 != 0]
even_columns = [even - 1 for even in enable_columns if even % 2 == 0]
else:
odd_columns = range(0, 80, 2)
even_columns = range(1, 80, 2)
odd_rows = np.arange(shift % steps, 336, steps)
even_row_offset = ((steps // 2) + shift) % steps # // integer devision
even_rows = np.arange(even_row_offset, 336, steps)
if odd_columns:
odd_col_rows = itertools.product(odd_columns, odd_rows) # get any combination of column and row, no for loop needed
for odd_col_row in odd_col_rows:
mask_array[odd_col_row[0], odd_col_row[1]] = value # advanced indexing
if even_columns:
even_col_rows = itertools.product(even_columns, even_rows)
for even_col_row in even_col_rows:
mask_array[even_col_row[0], even_col_row[1]] = value
if mask is not None:
mask_array = np.ma.array(mask_array, mask=mask, fill_value=default)
mask_array = mask_array.filled()
return mask_array
|
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Generate pixel mask.
Parameters
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steps : int
Number of mask steps, e.g. steps=3 (every third pixel is enabled), steps=336 (one pixel per column), steps=672 (one pixel per double column).
shift : int
Shift mask by given value to the bottom (towards higher row numbers). From 0 to (steps - 1).
default : int
Value of pixels that are not selected by the mask.
value : int
Value of pixels that are selected by the mask.
enable_columns : list
List of columns where the shift mask will be applied. List elements can range from 1 to 80.
mask : array_like
Additional mask. Must be convertible to an array of booleans with the same shape as mask array. True indicates a masked (i.e. invalid) data. Masked pixels will be set to default value.
Returns
-------
mask_array : numpy.ndarray
Mask array.
Usage
-----
shift_mask = 'enable'
steps = 3 # three step mask
for mask_step in range(steps):
commands = []
commands.extend(self.register.get_commands("ConfMode"))
mask_array = make_pixel_mask(steps=steps, step=mask_step)
self.register.set_pixel_register_value(shift_mask, mask_array)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=True, name=shift_mask))
self.register_utils.send_commands(commands)
# do something here
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register_utils.py#L678-L738
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register_utils.py
|
make_pixel_mask_from_col_row
|
def make_pixel_mask_from_col_row(column, row, default=0, value=1):
'''Generate mask from column and row lists
Parameters
----------
column : iterable, int
List of colums values.
row : iterable, int
List of row values.
default : int
Value of pixels that are not selected by the mask.
value : int
Value of pixels that are selected by the mask.
Returns
-------
mask : numpy.ndarray
'''
# FE columns and rows start from 1
col_array = np.array(column) - 1
row_array = np.array(row) - 1
if np.any(col_array >= 80) or np.any(col_array < 0) or np.any(row_array >= 336) or np.any(row_array < 0):
raise ValueError('Column and/or row out of range')
shape = (80, 336)
mask = np.full(shape, default, dtype=np.uint8)
mask[col_array, row_array] = value # advanced indexing
return mask
|
python
|
def make_pixel_mask_from_col_row(column, row, default=0, value=1):
'''Generate mask from column and row lists
Parameters
----------
column : iterable, int
List of colums values.
row : iterable, int
List of row values.
default : int
Value of pixels that are not selected by the mask.
value : int
Value of pixels that are selected by the mask.
Returns
-------
mask : numpy.ndarray
'''
# FE columns and rows start from 1
col_array = np.array(column) - 1
row_array = np.array(row) - 1
if np.any(col_array >= 80) or np.any(col_array < 0) or np.any(row_array >= 336) or np.any(row_array < 0):
raise ValueError('Column and/or row out of range')
shape = (80, 336)
mask = np.full(shape, default, dtype=np.uint8)
mask[col_array, row_array] = value # advanced indexing
return mask
|
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Generate mask from column and row lists
Parameters
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List of colums values.
row : iterable, int
List of row values.
default : int
Value of pixels that are not selected by the mask.
value : int
Value of pixels that are selected by the mask.
Returns
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mask : numpy.ndarray
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register_utils.py#L741-L767
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register_utils.py
|
make_box_pixel_mask_from_col_row
|
def make_box_pixel_mask_from_col_row(column, row, default=0, value=1):
'''Generate box shaped mask from column and row lists. Takes the minimum and maximum value from each list.
Parameters
----------
column : iterable, int
List of colums values.
row : iterable, int
List of row values.
default : int
Value of pixels that are not selected by the mask.
value : int
Value of pixels that are selected by the mask.
Returns
-------
numpy.ndarray
'''
# FE columns and rows start from 1
col_array = np.array(column) - 1
row_array = np.array(row) - 1
if np.any(col_array >= 80) or np.any(col_array < 0) or np.any(row_array >= 336) or np.any(row_array < 0):
raise ValueError('Column and/or row out of range')
shape = (80, 336)
mask = np.full(shape, default, dtype=np.uint8)
if column and row:
mask[col_array.min():col_array.max() + 1, row_array.min():row_array.max() + 1] = value # advanced indexing
return mask
|
python
|
def make_box_pixel_mask_from_col_row(column, row, default=0, value=1):
'''Generate box shaped mask from column and row lists. Takes the minimum and maximum value from each list.
Parameters
----------
column : iterable, int
List of colums values.
row : iterable, int
List of row values.
default : int
Value of pixels that are not selected by the mask.
value : int
Value of pixels that are selected by the mask.
Returns
-------
numpy.ndarray
'''
# FE columns and rows start from 1
col_array = np.array(column) - 1
row_array = np.array(row) - 1
if np.any(col_array >= 80) or np.any(col_array < 0) or np.any(row_array >= 336) or np.any(row_array < 0):
raise ValueError('Column and/or row out of range')
shape = (80, 336)
mask = np.full(shape, default, dtype=np.uint8)
if column and row:
mask[col_array.min():col_array.max() + 1, row_array.min():row_array.max() + 1] = value # advanced indexing
return mask
|
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Parameters
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List of colums values.
row : iterable, int
List of row values.
default : int
Value of pixels that are not selected by the mask.
value : int
Value of pixels that are selected by the mask.
Returns
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numpy.ndarray
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register_utils.py#L770-L797
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register_utils.py
|
make_xtalk_mask
|
def make_xtalk_mask(mask):
"""
Generate xtalk mask (row - 1, row + 1) from pixel mask.
Parameters
----------
mask : ndarray
Pixel mask.
Returns
-------
ndarray
Xtalk mask.
Example
-------
Input:
[[1 0 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 1 ... 0 1 0 0 0 0 0 1 0 0]
...
[1 0 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 1 ... 0 1 0 0 0 0 0 1 0 0]]
Output:
[[0 1 0 0 0 1 0 1 0 0 ... 0 0 0 1 0 1 0 0 0 1]
[0 0 1 0 1 0 0 0 1 0 ... 1 0 1 0 0 0 1 0 1 0]
...
[0 1 0 0 0 1 0 1 0 0 ... 0 0 0 1 0 1 0 0 0 1]
[0 0 1 0 1 0 0 0 1 0 ... 1 0 1 0 0 0 1 0 1 0]]
"""
col, row = mask.nonzero()
row_plus_one = row + 1
del_index = np.where(row_plus_one > 335)
row_plus_one = np.delete(row_plus_one, del_index)
col_plus_one = np.delete(col.copy(), del_index)
row_minus_one = row - 1
del_index = np.where(row_minus_one > 335)
row_minus_one = np.delete(row_minus_one, del_index)
col_minus_one = np.delete(col.copy(), del_index)
col = np.concatenate((col_plus_one, col_minus_one))
row = np.concatenate((row_plus_one, row_minus_one))
return make_pixel_mask_from_col_row(col + 1, row + 1)
|
python
|
def make_xtalk_mask(mask):
"""
Generate xtalk mask (row - 1, row + 1) from pixel mask.
Parameters
----------
mask : ndarray
Pixel mask.
Returns
-------
ndarray
Xtalk mask.
Example
-------
Input:
[[1 0 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 1 ... 0 1 0 0 0 0 0 1 0 0]
...
[1 0 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 1 ... 0 1 0 0 0 0 0 1 0 0]]
Output:
[[0 1 0 0 0 1 0 1 0 0 ... 0 0 0 1 0 1 0 0 0 1]
[0 0 1 0 1 0 0 0 1 0 ... 1 0 1 0 0 0 1 0 1 0]
...
[0 1 0 0 0 1 0 1 0 0 ... 0 0 0 1 0 1 0 0 0 1]
[0 0 1 0 1 0 0 0 1 0 ... 1 0 1 0 0 0 1 0 1 0]]
"""
col, row = mask.nonzero()
row_plus_one = row + 1
del_index = np.where(row_plus_one > 335)
row_plus_one = np.delete(row_plus_one, del_index)
col_plus_one = np.delete(col.copy(), del_index)
row_minus_one = row - 1
del_index = np.where(row_minus_one > 335)
row_minus_one = np.delete(row_minus_one, del_index)
col_minus_one = np.delete(col.copy(), del_index)
col = np.concatenate((col_plus_one, col_minus_one))
row = np.concatenate((row_plus_one, row_minus_one))
return make_pixel_mask_from_col_row(col + 1, row + 1)
|
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Generate xtalk mask (row - 1, row + 1) from pixel mask.
Parameters
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mask : ndarray
Pixel mask.
Returns
-------
ndarray
Xtalk mask.
Example
-------
Input:
[[1 0 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 1 ... 0 1 0 0 0 0 0 1 0 0]
...
[1 0 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 1 ... 0 1 0 0 0 0 0 1 0 0]]
Output:
[[0 1 0 0 0 1 0 1 0 0 ... 0 0 0 1 0 1 0 0 0 1]
[0 0 1 0 1 0 0 0 1 0 ... 1 0 1 0 0 0 1 0 1 0]
...
[0 1 0 0 0 1 0 1 0 0 ... 0 0 0 1 0 1 0 0 0 1]
[0 0 1 0 1 0 0 0 1 0 ... 1 0 1 0 0 0 1 0 1 0]]
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register_utils.py#L800-L841
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register_utils.py
|
make_checkerboard_mask
|
def make_checkerboard_mask(column_distance, row_distance, column_offset=0, row_offset=0, default=0, value=1):
"""
Generate chessboard/checkerboard mask.
Parameters
----------
column_distance : int
Column distance of the enabled pixels.
row_distance : int
Row distance of the enabled pixels.
column_offset : int
Additional column offset which shifts the columns by the given amount.
column_offset : int
Additional row offset which shifts the rows by the given amount.
Returns
-------
ndarray
Chessboard mask.
Example
-------
Input:
column_distance : 6
row_distance : 2
Output:
[[1 0 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 1 ... 0 1 0 0 0 0 0 1 0 0]
...
[0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 1 ... 0 1 0 0 0 0 0 1 0 0]
[0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0]]
"""
col_shape = (336,)
col = np.full(col_shape, fill_value=default, dtype=np.uint8)
col[::row_distance] = value
shape = (80, 336)
chessboard_mask = np.full(shape, fill_value=default, dtype=np.uint8)
chessboard_mask[column_offset::column_distance * 2] = np.roll(col, row_offset)
chessboard_mask[column_distance + column_offset::column_distance * 2] = np.roll(col, row_distance / 2 + row_offset)
return chessboard_mask
|
python
|
def make_checkerboard_mask(column_distance, row_distance, column_offset=0, row_offset=0, default=0, value=1):
"""
Generate chessboard/checkerboard mask.
Parameters
----------
column_distance : int
Column distance of the enabled pixels.
row_distance : int
Row distance of the enabled pixels.
column_offset : int
Additional column offset which shifts the columns by the given amount.
column_offset : int
Additional row offset which shifts the rows by the given amount.
Returns
-------
ndarray
Chessboard mask.
Example
-------
Input:
column_distance : 6
row_distance : 2
Output:
[[1 0 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 1 ... 0 1 0 0 0 0 0 1 0 0]
...
[0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 1 ... 0 1 0 0 0 0 0 1 0 0]
[0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0]]
"""
col_shape = (336,)
col = np.full(col_shape, fill_value=default, dtype=np.uint8)
col[::row_distance] = value
shape = (80, 336)
chessboard_mask = np.full(shape, fill_value=default, dtype=np.uint8)
chessboard_mask[column_offset::column_distance * 2] = np.roll(col, row_offset)
chessboard_mask[column_distance + column_offset::column_distance * 2] = np.roll(col, row_distance / 2 + row_offset)
return chessboard_mask
|
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Generate chessboard/checkerboard mask.
Parameters
----------
column_distance : int
Column distance of the enabled pixels.
row_distance : int
Row distance of the enabled pixels.
column_offset : int
Additional column offset which shifts the columns by the given amount.
column_offset : int
Additional row offset which shifts the rows by the given amount.
Returns
-------
ndarray
Chessboard mask.
Example
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Input:
column_distance : 6
row_distance : 2
Output:
[[1 0 0 0 0 0 1 0 0 0 ... 0 0 0 0 1 0 0 0 0 0]
[0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0]
[0 0 0 1 0 0 0 0 0 1 ... 0 1 0 0 0 0 0 1 0 0]
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[0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0]]
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register_utils.py#L844-L886
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register_utils.py
|
scan_loop
|
def scan_loop(self, command, repeat_command=100, use_delay=True, additional_delay=0, mask_steps=3, enable_mask_steps=None, enable_double_columns=None, same_mask_for_all_dc=False, fast_dc_loop=True, bol_function=None, eol_function=None, digital_injection=False, enable_shift_masks=None, disable_shift_masks=None, restore_shift_masks=True, mask=None, double_column_correction=False):
'''Implementation of the scan loops (mask shifting, loop over double columns, repeatedly sending any arbitrary command).
Parameters
----------
command : BitVector
(FEI4) command that will be sent out serially.
repeat_command : int
The number of repetitions command will be sent out each mask step.
use_delay : bool
Add additional delay to the command (append zeros). This helps to avoid FE data errors because of sending to many commands to the FE chip.
additional_delay: int
Additional delay to increase the command-to-command delay (in number of clock cycles / 25ns).
mask_steps : int
Number of mask steps (from 1 to 672).
enable_mask_steps : list, tuple
List of mask steps which will be applied. Default is all mask steps. From 0 to (mask-1). A value equal None or empty list will select all mask steps.
enable_double_columns : list, tuple
List of double columns which will be enabled during scan. Default is all double columns. From 0 to 39 (double columns counted from zero). A value equal None or empty list will select all double columns.
same_mask_for_all_dc : bool
Use same mask for all double columns. This will only affect all shift masks (see enable_shift_masks). Enabling this is in general a good idea since all double columns will have the same configuration and the scan speed can increased by an order of magnitude.
fast_dc_loop : bool
If True, optimize double column (DC) loop to save time. Note that bol_function and eol_function cannot do register operations, if True.
bol_function : function
Begin of loop function that will be called each time before sending command. Argument is a function pointer (without braces) or functor.
eol_function : function
End of loop function that will be called each time after sending command. Argument is a function pointer (without braces) or functor.
digital_injection : bool
Enables digital injection. C_High and C_Low will be disabled.
enable_shift_masks : list, tuple
List of enable pixel masks which will be shifted during scan. Mask set to 1 for selected pixels else 0. None will select "Enable", "C_High", "C_Low".
disable_shift_masks : list, tuple
List of disable pixel masks which will be shifted during scan. Mask set to 0 for selected pixels else 1. None will disable no mask.
restore_shift_masks : bool
Writing the initial (restored) FE pixel configuration into FE after finishing the scan loop.
mask : array-like
Additional mask. Must be convertible to an array of booleans with the same shape as mask array. True indicates a masked pixel. Masked pixels will be disabled during shifting of the enable shift masks, and enabled during shifting disable shift mask.
double_column_correction : str, bool, list, tuple
Enables double column PlsrDAC correction. If value is a filename (string) or list/tuple, the default PlsrDAC correction will be overwritten. First line of the file must be a Python list ([0, 0, ...])
'''
if not isinstance(command, bitarray):
raise TypeError
if enable_shift_masks is None:
enable_shift_masks = ["Enable", "C_High", "C_Low"]
if disable_shift_masks is None:
disable_shift_masks = []
# get PlsrDAC correction
if isinstance(double_column_correction, basestring): # from file
with open(double_column_correction) as fp:
plsr_dac_correction = list(literal_eval(fp.readline().strip()))
elif isinstance(double_column_correction, (list, tuple)): # from list/tuple
plsr_dac_correction = list(double_column_correction)
else: # default
if "C_High".lower() in map(lambda x: x.lower(), enable_shift_masks) and "C_Low".lower() in map(lambda x: x.lower(), enable_shift_masks):
plsr_dac_correction = self.register.calibration_parameters['Pulser_Corr_C_Inj_High']
elif "C_High".lower() in map(lambda x: x.lower(), enable_shift_masks):
plsr_dac_correction = self.register.calibration_parameters['Pulser_Corr_C_Inj_Med']
elif "C_Low".lower() in map(lambda x: x.lower(), enable_shift_masks):
plsr_dac_correction = self.register.calibration_parameters['Pulser_Corr_C_Inj_Low']
# initial PlsrDAC value for PlsrDAC correction
initial_plsr_dac = self.register.get_global_register_value("PlsrDAC")
# create restore point
restore_point_name = str(self.run_number) + '_' + self.run_id + '_scan_loop'
with self.register.restored(name=restore_point_name):
# pre-calculate often used commands
conf_mode_command = self.register.get_commands("ConfMode")[0]
run_mode_command = self.register.get_commands("RunMode")[0]
if use_delay:
delay = self.register.get_commands("zeros", length=additional_delay + calculate_wait_cycles(mask_steps))[0]
scan_loop_command = command + delay
else:
scan_loop_command = command
def enable_columns(dc):
if digital_injection:
return [dc * 2 + 1, dc * 2 + 2]
else: # analog injection
if dc == 0:
return [1]
elif dc == 39:
return [78, 79, 80]
else:
return [dc * 2, dc * 2 + 1]
def write_double_columns(dc):
if digital_injection:
return [dc]
else: # analog injection
if dc == 0:
return [0]
elif dc == 39:
return [38, 39]
else:
return [dc - 1, dc]
def get_dc_address_command(dc):
commands = []
commands.append(conf_mode_command)
self.register.set_global_register_value("Colpr_Addr", dc)
commands.append(self.register.get_commands("WrRegister", name=["Colpr_Addr"])[0])
if double_column_correction:
self.register.set_global_register_value("PlsrDAC", initial_plsr_dac + int(round(plsr_dac_correction[dc])))
commands.append(self.register.get_commands("WrRegister", name=["PlsrDAC"])[0])
commands.append(run_mode_command)
return self.register_utils.concatenate_commands(commands, byte_padding=True)
if not enable_mask_steps:
enable_mask_steps = range(mask_steps)
if not enable_double_columns:
enable_double_columns = range(40)
# preparing for scan
commands = []
commands.append(conf_mode_command)
if digital_injection is True:
# check if C_High and/or C_Low is in enable_shift_mask and/or disable_shift_mask
if "C_High".lower() in map(lambda x: x.lower(), enable_shift_masks) or "C_High".lower() in map(lambda x: x.lower(), disable_shift_masks):
raise ValueError('C_High must not be shift mask when using digital injection')
if "C_Low".lower() in map(lambda x: x.lower(), enable_shift_masks) or "C_Low".lower() in map(lambda x: x.lower(), disable_shift_masks):
raise ValueError('C_Low must not be shift mask when using digital injection')
# turn off all injection capacitors by default
self.register.set_pixel_register_value("C_High", 0)
self.register.set_pixel_register_value("C_Low", 0)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=True, name=["C_Low", "C_High"], joint_write=True))
self.register.set_global_register_value("DIGHITIN_SEL", 1)
# self.register.set_global_register_value("CalEn", 1) # for GlobalPulse instead Cal-Command
else:
self.register.set_global_register_value("DIGHITIN_SEL", 0)
# setting EnableDigInj to 0 not necessary since DIGHITIN_SEL is turned off
# self.register.set_pixel_register_value("EnableDigInj", 0)
# plotting registers
# plt.clf()
# plt.imshow(curr_en_mask.T, interpolation='nearest', aspect="auto")
# plt.pcolor(curr_en_mask.T)
# plt.colorbar()
# plt.savefig('mask_step' + str(mask_step) + '.pdf')
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL"]))
self.register_utils.send_commands(commands)
for mask_step in enable_mask_steps:
if self.abort_run.is_set():
break
commands = []
commands.append(conf_mode_command)
if same_mask_for_all_dc: # generate and write first mask step
if disable_shift_masks:
curr_dis_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, default=1, value=0, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_dis_mask), disable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False if mask is not None else True, name=disable_shift_masks, joint_write=True))
if enable_shift_masks:
curr_en_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_en_mask), enable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False if mask is not None else True, name=enable_shift_masks, joint_write=True))
if digital_injection is True: # write EnableDigInj last
# write DIGHITIN_SEL since after mask writing it is disabled
self.register.set_global_register_value("DIGHITIN_SEL", 1)
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL"]))
else: # set masks to default values
if disable_shift_masks:
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, 1), disable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=True, name=disable_shift_masks, joint_write=True))
if enable_shift_masks:
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, 0), enable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=True, name=enable_shift_masks, joint_write=True))
if digital_injection is True: # write EnableDigInj last
# write DIGHITIN_SEL since after mask writing it is disabled
self.register.set_global_register_value("DIGHITIN_SEL", 1)
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL"]))
self.register_utils.send_commands(commands)
logging.info('%d injection(s): mask step %d %s', repeat_command, mask_step, ('[%d - %d]' % (enable_mask_steps[0], enable_mask_steps[-1])) if len(enable_mask_steps) > 1 else ('[%d]' % enable_mask_steps[0]))
if same_mask_for_all_dc:
if fast_dc_loop: # fast DC loop with optimized pixel register writing
# set repeat, should be 1 by default when arriving here
self.dut['TX']['CMD_REPEAT'] = repeat_command
# get DC command for the first DC in the list, DC command is byte padded
# fill CMD memory with DC command and scan loop command, inside the loop only overwrite DC command
dc_address_command = get_dc_address_command(enable_double_columns[0])
self.dut['TX']['START_SEQUENCE_LENGTH'] = len(dc_address_command)
self.register_utils.set_command(command=self.register_utils.concatenate_commands((dc_address_command, scan_loop_command), byte_padding=False))
for index, dc in enumerate(enable_double_columns):
if self.abort_run.is_set():
break
if index != 0: # full command is already set before loop
# get DC command before wait to save some time
dc_address_command = get_dc_address_command(dc)
self.register_utils.wait_for_command()
if eol_function:
eol_function() # do this after command has finished
# only set command after FPGA is ready
# overwrite only the DC command in CMD memory
self.register_utils.set_command(dc_address_command, set_length=False) # do not set length here, because it was already set up before the loop
if bol_function:
bol_function()
self.dut['TX']['START']
# wait here before we go on because we just jumped out of the loop
self.register_utils.wait_for_command()
if eol_function:
eol_function()
self.dut['TX']['START_SEQUENCE_LENGTH'] = 0
else: # the slow DC loop allows writing commands inside bol and eol functions
for index, dc in enumerate(enable_double_columns):
if self.abort_run.is_set():
break
dc_address_command = get_dc_address_command(dc)
self.register_utils.send_command(dc_address_command)
if bol_function:
bol_function()
self.register_utils.send_command(scan_loop_command, repeat=repeat_command)
if eol_function:
eol_function()
else:
if fast_dc_loop: # fast DC loop with optimized pixel register writing
dc = enable_double_columns[0]
ec = enable_columns(dc)
dcs = write_double_columns(dc)
commands = []
commands.append(conf_mode_command)
if disable_shift_masks:
curr_dis_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, default=1, value=0, enable_columns=ec, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_dis_mask), disable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, dcs=dcs, name=disable_shift_masks, joint_write=True))
if enable_shift_masks:
curr_en_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, enable_columns=ec, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_en_mask), enable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, dcs=dcs, name=enable_shift_masks, joint_write=True))
if digital_injection is True:
self.register.set_global_register_value("DIGHITIN_SEL", 1)
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL"]))
self.register_utils.send_commands(commands)
dc_address_command = get_dc_address_command(dc)
self.dut['TX']['START_SEQUENCE_LENGTH'] = len(dc_address_command)
self.dut['TX']['CMD_REPEAT'] = repeat_command
self.register_utils.set_command(command=self.register_utils.concatenate_commands((dc_address_command, scan_loop_command), byte_padding=False))
for index, dc in enumerate(enable_double_columns):
if self.abort_run.is_set():
break
if index != 0: # full command is already set before loop
ec = enable_columns(dc)
dcs = write_double_columns(dc)
dcs.extend(write_double_columns(enable_double_columns[index - 1]))
commands = []
commands.append(conf_mode_command)
if disable_shift_masks:
curr_dis_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, default=1, value=0, enable_columns=ec, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_dis_mask), disable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, dcs=dcs, name=disable_shift_masks, joint_write=True))
if enable_shift_masks:
curr_en_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, enable_columns=ec, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_en_mask), enable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, dcs=dcs, name=enable_shift_masks, joint_write=True))
if digital_injection is True:
self.register.set_global_register_value("DIGHITIN_SEL", 1)
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL"]))
dc_address_command = get_dc_address_command(dc)
self.register_utils.wait_for_command()
if eol_function:
eol_function() # do this after command has finished
self.register_utils.send_commands(commands)
self.dut['TX']['START_SEQUENCE_LENGTH'] = len(dc_address_command)
self.dut['TX']['CMD_REPEAT'] = repeat_command
self.register_utils.set_command(command=self.register_utils.concatenate_commands((dc_address_command, scan_loop_command), byte_padding=False))
if bol_function:
bol_function()
self.dut['TX']['START']
self.register_utils.wait_for_command()
if eol_function:
eol_function()
self.dut['TX']['START_SEQUENCE_LENGTH'] = 0
else:
for index, dc in enumerate(enable_double_columns):
if self.abort_run.is_set():
break
ec = enable_columns(dc)
dcs = write_double_columns(dc)
if index != 0:
dcs.extend(write_double_columns(enable_double_columns[index - 1]))
commands = []
commands.append(conf_mode_command)
if disable_shift_masks:
curr_dis_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, default=1, value=0, enable_columns=ec, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_dis_mask), disable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, dcs=dcs, name=disable_shift_masks, joint_write=True))
if enable_shift_masks:
curr_en_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, enable_columns=ec, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_en_mask), enable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, dcs=dcs, name=enable_shift_masks, joint_write=True))
if digital_injection is True:
self.register.set_global_register_value("DIGHITIN_SEL", 1)
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL"]))
self.register_utils.send_commands(commands)
dc_address_command = get_dc_address_command(dc)
self.register_utils.send_command(dc_address_command)
if bol_function:
bol_function()
self.register_utils.send_command(scan_loop_command, repeat=repeat_command)
if eol_function:
eol_function()
commands = []
commands.extend(self.register.get_commands("ConfMode"))
# write registers that were changed in scan_loop()
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL", "Colpr_Addr", "PlsrDAC"]))
if restore_shift_masks:
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL", "Colpr_Addr", "PlsrDAC"]))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name=disable_shift_masks))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name=enable_shift_masks))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="EnableDigInj"))
# commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
|
python
|
def scan_loop(self, command, repeat_command=100, use_delay=True, additional_delay=0, mask_steps=3, enable_mask_steps=None, enable_double_columns=None, same_mask_for_all_dc=False, fast_dc_loop=True, bol_function=None, eol_function=None, digital_injection=False, enable_shift_masks=None, disable_shift_masks=None, restore_shift_masks=True, mask=None, double_column_correction=False):
'''Implementation of the scan loops (mask shifting, loop over double columns, repeatedly sending any arbitrary command).
Parameters
----------
command : BitVector
(FEI4) command that will be sent out serially.
repeat_command : int
The number of repetitions command will be sent out each mask step.
use_delay : bool
Add additional delay to the command (append zeros). This helps to avoid FE data errors because of sending to many commands to the FE chip.
additional_delay: int
Additional delay to increase the command-to-command delay (in number of clock cycles / 25ns).
mask_steps : int
Number of mask steps (from 1 to 672).
enable_mask_steps : list, tuple
List of mask steps which will be applied. Default is all mask steps. From 0 to (mask-1). A value equal None or empty list will select all mask steps.
enable_double_columns : list, tuple
List of double columns which will be enabled during scan. Default is all double columns. From 0 to 39 (double columns counted from zero). A value equal None or empty list will select all double columns.
same_mask_for_all_dc : bool
Use same mask for all double columns. This will only affect all shift masks (see enable_shift_masks). Enabling this is in general a good idea since all double columns will have the same configuration and the scan speed can increased by an order of magnitude.
fast_dc_loop : bool
If True, optimize double column (DC) loop to save time. Note that bol_function and eol_function cannot do register operations, if True.
bol_function : function
Begin of loop function that will be called each time before sending command. Argument is a function pointer (without braces) or functor.
eol_function : function
End of loop function that will be called each time after sending command. Argument is a function pointer (without braces) or functor.
digital_injection : bool
Enables digital injection. C_High and C_Low will be disabled.
enable_shift_masks : list, tuple
List of enable pixel masks which will be shifted during scan. Mask set to 1 for selected pixels else 0. None will select "Enable", "C_High", "C_Low".
disable_shift_masks : list, tuple
List of disable pixel masks which will be shifted during scan. Mask set to 0 for selected pixels else 1. None will disable no mask.
restore_shift_masks : bool
Writing the initial (restored) FE pixel configuration into FE after finishing the scan loop.
mask : array-like
Additional mask. Must be convertible to an array of booleans with the same shape as mask array. True indicates a masked pixel. Masked pixels will be disabled during shifting of the enable shift masks, and enabled during shifting disable shift mask.
double_column_correction : str, bool, list, tuple
Enables double column PlsrDAC correction. If value is a filename (string) or list/tuple, the default PlsrDAC correction will be overwritten. First line of the file must be a Python list ([0, 0, ...])
'''
if not isinstance(command, bitarray):
raise TypeError
if enable_shift_masks is None:
enable_shift_masks = ["Enable", "C_High", "C_Low"]
if disable_shift_masks is None:
disable_shift_masks = []
# get PlsrDAC correction
if isinstance(double_column_correction, basestring): # from file
with open(double_column_correction) as fp:
plsr_dac_correction = list(literal_eval(fp.readline().strip()))
elif isinstance(double_column_correction, (list, tuple)): # from list/tuple
plsr_dac_correction = list(double_column_correction)
else: # default
if "C_High".lower() in map(lambda x: x.lower(), enable_shift_masks) and "C_Low".lower() in map(lambda x: x.lower(), enable_shift_masks):
plsr_dac_correction = self.register.calibration_parameters['Pulser_Corr_C_Inj_High']
elif "C_High".lower() in map(lambda x: x.lower(), enable_shift_masks):
plsr_dac_correction = self.register.calibration_parameters['Pulser_Corr_C_Inj_Med']
elif "C_Low".lower() in map(lambda x: x.lower(), enable_shift_masks):
plsr_dac_correction = self.register.calibration_parameters['Pulser_Corr_C_Inj_Low']
# initial PlsrDAC value for PlsrDAC correction
initial_plsr_dac = self.register.get_global_register_value("PlsrDAC")
# create restore point
restore_point_name = str(self.run_number) + '_' + self.run_id + '_scan_loop'
with self.register.restored(name=restore_point_name):
# pre-calculate often used commands
conf_mode_command = self.register.get_commands("ConfMode")[0]
run_mode_command = self.register.get_commands("RunMode")[0]
if use_delay:
delay = self.register.get_commands("zeros", length=additional_delay + calculate_wait_cycles(mask_steps))[0]
scan_loop_command = command + delay
else:
scan_loop_command = command
def enable_columns(dc):
if digital_injection:
return [dc * 2 + 1, dc * 2 + 2]
else: # analog injection
if dc == 0:
return [1]
elif dc == 39:
return [78, 79, 80]
else:
return [dc * 2, dc * 2 + 1]
def write_double_columns(dc):
if digital_injection:
return [dc]
else: # analog injection
if dc == 0:
return [0]
elif dc == 39:
return [38, 39]
else:
return [dc - 1, dc]
def get_dc_address_command(dc):
commands = []
commands.append(conf_mode_command)
self.register.set_global_register_value("Colpr_Addr", dc)
commands.append(self.register.get_commands("WrRegister", name=["Colpr_Addr"])[0])
if double_column_correction:
self.register.set_global_register_value("PlsrDAC", initial_plsr_dac + int(round(plsr_dac_correction[dc])))
commands.append(self.register.get_commands("WrRegister", name=["PlsrDAC"])[0])
commands.append(run_mode_command)
return self.register_utils.concatenate_commands(commands, byte_padding=True)
if not enable_mask_steps:
enable_mask_steps = range(mask_steps)
if not enable_double_columns:
enable_double_columns = range(40)
# preparing for scan
commands = []
commands.append(conf_mode_command)
if digital_injection is True:
# check if C_High and/or C_Low is in enable_shift_mask and/or disable_shift_mask
if "C_High".lower() in map(lambda x: x.lower(), enable_shift_masks) or "C_High".lower() in map(lambda x: x.lower(), disable_shift_masks):
raise ValueError('C_High must not be shift mask when using digital injection')
if "C_Low".lower() in map(lambda x: x.lower(), enable_shift_masks) or "C_Low".lower() in map(lambda x: x.lower(), disable_shift_masks):
raise ValueError('C_Low must not be shift mask when using digital injection')
# turn off all injection capacitors by default
self.register.set_pixel_register_value("C_High", 0)
self.register.set_pixel_register_value("C_Low", 0)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=True, name=["C_Low", "C_High"], joint_write=True))
self.register.set_global_register_value("DIGHITIN_SEL", 1)
# self.register.set_global_register_value("CalEn", 1) # for GlobalPulse instead Cal-Command
else:
self.register.set_global_register_value("DIGHITIN_SEL", 0)
# setting EnableDigInj to 0 not necessary since DIGHITIN_SEL is turned off
# self.register.set_pixel_register_value("EnableDigInj", 0)
# plotting registers
# plt.clf()
# plt.imshow(curr_en_mask.T, interpolation='nearest', aspect="auto")
# plt.pcolor(curr_en_mask.T)
# plt.colorbar()
# plt.savefig('mask_step' + str(mask_step) + '.pdf')
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL"]))
self.register_utils.send_commands(commands)
for mask_step in enable_mask_steps:
if self.abort_run.is_set():
break
commands = []
commands.append(conf_mode_command)
if same_mask_for_all_dc: # generate and write first mask step
if disable_shift_masks:
curr_dis_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, default=1, value=0, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_dis_mask), disable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False if mask is not None else True, name=disable_shift_masks, joint_write=True))
if enable_shift_masks:
curr_en_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_en_mask), enable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False if mask is not None else True, name=enable_shift_masks, joint_write=True))
if digital_injection is True: # write EnableDigInj last
# write DIGHITIN_SEL since after mask writing it is disabled
self.register.set_global_register_value("DIGHITIN_SEL", 1)
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL"]))
else: # set masks to default values
if disable_shift_masks:
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, 1), disable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=True, name=disable_shift_masks, joint_write=True))
if enable_shift_masks:
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, 0), enable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=True, name=enable_shift_masks, joint_write=True))
if digital_injection is True: # write EnableDigInj last
# write DIGHITIN_SEL since after mask writing it is disabled
self.register.set_global_register_value("DIGHITIN_SEL", 1)
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL"]))
self.register_utils.send_commands(commands)
logging.info('%d injection(s): mask step %d %s', repeat_command, mask_step, ('[%d - %d]' % (enable_mask_steps[0], enable_mask_steps[-1])) if len(enable_mask_steps) > 1 else ('[%d]' % enable_mask_steps[0]))
if same_mask_for_all_dc:
if fast_dc_loop: # fast DC loop with optimized pixel register writing
# set repeat, should be 1 by default when arriving here
self.dut['TX']['CMD_REPEAT'] = repeat_command
# get DC command for the first DC in the list, DC command is byte padded
# fill CMD memory with DC command and scan loop command, inside the loop only overwrite DC command
dc_address_command = get_dc_address_command(enable_double_columns[0])
self.dut['TX']['START_SEQUENCE_LENGTH'] = len(dc_address_command)
self.register_utils.set_command(command=self.register_utils.concatenate_commands((dc_address_command, scan_loop_command), byte_padding=False))
for index, dc in enumerate(enable_double_columns):
if self.abort_run.is_set():
break
if index != 0: # full command is already set before loop
# get DC command before wait to save some time
dc_address_command = get_dc_address_command(dc)
self.register_utils.wait_for_command()
if eol_function:
eol_function() # do this after command has finished
# only set command after FPGA is ready
# overwrite only the DC command in CMD memory
self.register_utils.set_command(dc_address_command, set_length=False) # do not set length here, because it was already set up before the loop
if bol_function:
bol_function()
self.dut['TX']['START']
# wait here before we go on because we just jumped out of the loop
self.register_utils.wait_for_command()
if eol_function:
eol_function()
self.dut['TX']['START_SEQUENCE_LENGTH'] = 0
else: # the slow DC loop allows writing commands inside bol and eol functions
for index, dc in enumerate(enable_double_columns):
if self.abort_run.is_set():
break
dc_address_command = get_dc_address_command(dc)
self.register_utils.send_command(dc_address_command)
if bol_function:
bol_function()
self.register_utils.send_command(scan_loop_command, repeat=repeat_command)
if eol_function:
eol_function()
else:
if fast_dc_loop: # fast DC loop with optimized pixel register writing
dc = enable_double_columns[0]
ec = enable_columns(dc)
dcs = write_double_columns(dc)
commands = []
commands.append(conf_mode_command)
if disable_shift_masks:
curr_dis_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, default=1, value=0, enable_columns=ec, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_dis_mask), disable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, dcs=dcs, name=disable_shift_masks, joint_write=True))
if enable_shift_masks:
curr_en_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, enable_columns=ec, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_en_mask), enable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, dcs=dcs, name=enable_shift_masks, joint_write=True))
if digital_injection is True:
self.register.set_global_register_value("DIGHITIN_SEL", 1)
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL"]))
self.register_utils.send_commands(commands)
dc_address_command = get_dc_address_command(dc)
self.dut['TX']['START_SEQUENCE_LENGTH'] = len(dc_address_command)
self.dut['TX']['CMD_REPEAT'] = repeat_command
self.register_utils.set_command(command=self.register_utils.concatenate_commands((dc_address_command, scan_loop_command), byte_padding=False))
for index, dc in enumerate(enable_double_columns):
if self.abort_run.is_set():
break
if index != 0: # full command is already set before loop
ec = enable_columns(dc)
dcs = write_double_columns(dc)
dcs.extend(write_double_columns(enable_double_columns[index - 1]))
commands = []
commands.append(conf_mode_command)
if disable_shift_masks:
curr_dis_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, default=1, value=0, enable_columns=ec, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_dis_mask), disable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, dcs=dcs, name=disable_shift_masks, joint_write=True))
if enable_shift_masks:
curr_en_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, enable_columns=ec, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_en_mask), enable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, dcs=dcs, name=enable_shift_masks, joint_write=True))
if digital_injection is True:
self.register.set_global_register_value("DIGHITIN_SEL", 1)
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL"]))
dc_address_command = get_dc_address_command(dc)
self.register_utils.wait_for_command()
if eol_function:
eol_function() # do this after command has finished
self.register_utils.send_commands(commands)
self.dut['TX']['START_SEQUENCE_LENGTH'] = len(dc_address_command)
self.dut['TX']['CMD_REPEAT'] = repeat_command
self.register_utils.set_command(command=self.register_utils.concatenate_commands((dc_address_command, scan_loop_command), byte_padding=False))
if bol_function:
bol_function()
self.dut['TX']['START']
self.register_utils.wait_for_command()
if eol_function:
eol_function()
self.dut['TX']['START_SEQUENCE_LENGTH'] = 0
else:
for index, dc in enumerate(enable_double_columns):
if self.abort_run.is_set():
break
ec = enable_columns(dc)
dcs = write_double_columns(dc)
if index != 0:
dcs.extend(write_double_columns(enable_double_columns[index - 1]))
commands = []
commands.append(conf_mode_command)
if disable_shift_masks:
curr_dis_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, default=1, value=0, enable_columns=ec, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_dis_mask), disable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, dcs=dcs, name=disable_shift_masks, joint_write=True))
if enable_shift_masks:
curr_en_mask = make_pixel_mask(steps=mask_steps, shift=mask_step, enable_columns=ec, mask=mask)
map(lambda mask_name: self.register.set_pixel_register_value(mask_name, curr_en_mask), enable_shift_masks)
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, dcs=dcs, name=enable_shift_masks, joint_write=True))
if digital_injection is True:
self.register.set_global_register_value("DIGHITIN_SEL", 1)
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL"]))
self.register_utils.send_commands(commands)
dc_address_command = get_dc_address_command(dc)
self.register_utils.send_command(dc_address_command)
if bol_function:
bol_function()
self.register_utils.send_command(scan_loop_command, repeat=repeat_command)
if eol_function:
eol_function()
commands = []
commands.extend(self.register.get_commands("ConfMode"))
# write registers that were changed in scan_loop()
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL", "Colpr_Addr", "PlsrDAC"]))
if restore_shift_masks:
commands = []
commands.extend(self.register.get_commands("ConfMode"))
commands.extend(self.register.get_commands("WrRegister", name=["DIGHITIN_SEL", "Colpr_Addr", "PlsrDAC"]))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name=disable_shift_masks))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name=enable_shift_masks))
commands.extend(self.register.get_commands("WrFrontEnd", same_mask_for_all_dc=False, name="EnableDigInj"))
# commands.extend(self.register.get_commands("RunMode"))
self.register_utils.send_commands(commands)
|
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] |
Implementation of the scan loops (mask shifting, loop over double columns, repeatedly sending any arbitrary command).
Parameters
----------
command : BitVector
(FEI4) command that will be sent out serially.
repeat_command : int
The number of repetitions command will be sent out each mask step.
use_delay : bool
Add additional delay to the command (append zeros). This helps to avoid FE data errors because of sending to many commands to the FE chip.
additional_delay: int
Additional delay to increase the command-to-command delay (in number of clock cycles / 25ns).
mask_steps : int
Number of mask steps (from 1 to 672).
enable_mask_steps : list, tuple
List of mask steps which will be applied. Default is all mask steps. From 0 to (mask-1). A value equal None or empty list will select all mask steps.
enable_double_columns : list, tuple
List of double columns which will be enabled during scan. Default is all double columns. From 0 to 39 (double columns counted from zero). A value equal None or empty list will select all double columns.
same_mask_for_all_dc : bool
Use same mask for all double columns. This will only affect all shift masks (see enable_shift_masks). Enabling this is in general a good idea since all double columns will have the same configuration and the scan speed can increased by an order of magnitude.
fast_dc_loop : bool
If True, optimize double column (DC) loop to save time. Note that bol_function and eol_function cannot do register operations, if True.
bol_function : function
Begin of loop function that will be called each time before sending command. Argument is a function pointer (without braces) or functor.
eol_function : function
End of loop function that will be called each time after sending command. Argument is a function pointer (without braces) or functor.
digital_injection : bool
Enables digital injection. C_High and C_Low will be disabled.
enable_shift_masks : list, tuple
List of enable pixel masks which will be shifted during scan. Mask set to 1 for selected pixels else 0. None will select "Enable", "C_High", "C_Low".
disable_shift_masks : list, tuple
List of disable pixel masks which will be shifted during scan. Mask set to 0 for selected pixels else 1. None will disable no mask.
restore_shift_masks : bool
Writing the initial (restored) FE pixel configuration into FE after finishing the scan loop.
mask : array-like
Additional mask. Must be convertible to an array of booleans with the same shape as mask array. True indicates a masked pixel. Masked pixels will be disabled during shifting of the enable shift masks, and enabled during shifting disable shift mask.
double_column_correction : str, bool, list, tuple
Enables double column PlsrDAC correction. If value is a filename (string) or list/tuple, the default PlsrDAC correction will be overwritten. First line of the file must be a Python list ([0, 0, ...])
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register_utils.py#L913-L1252
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register_utils.py
|
FEI4RegisterUtils.reset_service_records
|
def reset_service_records(self):
'''Resetting Service Records
This will reset Service Record counters. This will also bring back alive some FE where the output FIFO is stuck (no data is coming out in run mode).
This should be only issued after power up and in the case of a stuck FIFO, otherwise the BCID counter starts jumping.
'''
logging.info('Resetting Service Records')
commands = []
commands.extend(self.register.get_commands("ConfMode"))
self.register.set_global_register_value('ReadErrorReq', 1)
commands.extend(self.register.get_commands("WrRegister", name=['ReadErrorReq']))
commands.extend(self.register.get_commands("GlobalPulse", Width=0))
self.register.set_global_register_value('ReadErrorReq', 0)
commands.extend(self.register.get_commands("WrRegister", name=['ReadErrorReq']))
commands.extend(self.register.get_commands("RunMode"))
commands.extend(self.register.get_commands("ConfMode"))
self.send_commands(commands)
|
python
|
def reset_service_records(self):
'''Resetting Service Records
This will reset Service Record counters. This will also bring back alive some FE where the output FIFO is stuck (no data is coming out in run mode).
This should be only issued after power up and in the case of a stuck FIFO, otherwise the BCID counter starts jumping.
'''
logging.info('Resetting Service Records')
commands = []
commands.extend(self.register.get_commands("ConfMode"))
self.register.set_global_register_value('ReadErrorReq', 1)
commands.extend(self.register.get_commands("WrRegister", name=['ReadErrorReq']))
commands.extend(self.register.get_commands("GlobalPulse", Width=0))
self.register.set_global_register_value('ReadErrorReq', 0)
commands.extend(self.register.get_commands("WrRegister", name=['ReadErrorReq']))
commands.extend(self.register.get_commands("RunMode"))
commands.extend(self.register.get_commands("ConfMode"))
self.send_commands(commands)
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register_utils.py#L164-L180
|
SiLab-Bonn/pyBAR
|
pybar/fei4/register_utils.py
|
FEI4RegisterUtils.reset_bunch_counter
|
def reset_bunch_counter(self):
'''Resetting Bunch Counter
'''
logging.info('Resetting Bunch Counter')
commands = []
commands.extend(self.register.get_commands("RunMode"))
commands.extend(self.register.get_commands("BCR"))
self.send_commands(commands)
time.sleep(0.1)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
self.send_commands(commands)
|
python
|
def reset_bunch_counter(self):
'''Resetting Bunch Counter
'''
logging.info('Resetting Bunch Counter')
commands = []
commands.extend(self.register.get_commands("RunMode"))
commands.extend(self.register.get_commands("BCR"))
self.send_commands(commands)
time.sleep(0.1)
commands = []
commands.extend(self.register.get_commands("ConfMode"))
self.send_commands(commands)
|
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Resetting Bunch Counter
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[
"Resetting",
"Bunch",
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] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/fei4/register_utils.py#L182-L193
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
generate_threshold_mask
|
def generate_threshold_mask(hist):
'''Masking array elements when equal 0.0 or greater than 10 times the median
Parameters
----------
hist : array_like
Input data.
Returns
-------
masked array
Returns copy of the array with masked elements.
'''
masked_array = np.ma.masked_values(hist, 0)
masked_array = np.ma.masked_greater(masked_array, 10 * np.ma.median(hist))
logging.info('Masking %d pixel(s)', np.ma.count_masked(masked_array))
return np.ma.getmaskarray(masked_array)
|
python
|
def generate_threshold_mask(hist):
'''Masking array elements when equal 0.0 or greater than 10 times the median
Parameters
----------
hist : array_like
Input data.
Returns
-------
masked array
Returns copy of the array with masked elements.
'''
masked_array = np.ma.masked_values(hist, 0)
masked_array = np.ma.masked_greater(masked_array, 10 * np.ma.median(hist))
logging.info('Masking %d pixel(s)', np.ma.count_masked(masked_array))
return np.ma.getmaskarray(masked_array)
|
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train
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L50-L66
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
unique_row
|
def unique_row(array, use_columns=None, selected_columns_only=False):
'''Takes a numpy array and returns the array reduced to unique rows. If columns are defined only these columns are taken to define a unique row.
The returned array can have all columns of the original array or only the columns defined in use_columns.
Parameters
----------
array : numpy.ndarray
use_columns : list
Index of columns to be used to define a unique row
selected_columns_only : bool
If true only the columns defined in use_columns are returned
Returns
-------
numpy.ndarray
'''
if array.dtype.names is None: # normal array has no named dtype
if use_columns is not None:
a_cut = array[:, use_columns]
else:
a_cut = array
if len(use_columns) > 1:
b = np.ascontiguousarray(a_cut).view(np.dtype((np.void, a_cut.dtype.itemsize * a_cut.shape[1])))
else:
b = np.ascontiguousarray(a_cut)
_, index = np.unique(b, return_index=True)
if not selected_columns_only:
return array[np.sort(index)] # sort to preserve order
else:
return a_cut[np.sort(index)] # sort to preserve order
else: # names for dtype founnd --> array is recarray
names = list(array.dtype.names)
if use_columns is not None:
new_names = [names[i] for i in use_columns]
else:
new_names = names
a_cut, index = np.unique(array[new_names], return_index=True)
if not selected_columns_only:
return array[np.sort(index)] # sort to preserve order
else:
return array[np.sort(index)][new_names]
|
python
|
def unique_row(array, use_columns=None, selected_columns_only=False):
'''Takes a numpy array and returns the array reduced to unique rows. If columns are defined only these columns are taken to define a unique row.
The returned array can have all columns of the original array or only the columns defined in use_columns.
Parameters
----------
array : numpy.ndarray
use_columns : list
Index of columns to be used to define a unique row
selected_columns_only : bool
If true only the columns defined in use_columns are returned
Returns
-------
numpy.ndarray
'''
if array.dtype.names is None: # normal array has no named dtype
if use_columns is not None:
a_cut = array[:, use_columns]
else:
a_cut = array
if len(use_columns) > 1:
b = np.ascontiguousarray(a_cut).view(np.dtype((np.void, a_cut.dtype.itemsize * a_cut.shape[1])))
else:
b = np.ascontiguousarray(a_cut)
_, index = np.unique(b, return_index=True)
if not selected_columns_only:
return array[np.sort(index)] # sort to preserve order
else:
return a_cut[np.sort(index)] # sort to preserve order
else: # names for dtype founnd --> array is recarray
names = list(array.dtype.names)
if use_columns is not None:
new_names = [names[i] for i in use_columns]
else:
new_names = names
a_cut, index = np.unique(array[new_names], return_index=True)
if not selected_columns_only:
return array[np.sort(index)] # sort to preserve order
else:
return array[np.sort(index)][new_names]
|
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train
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L69-L108
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_ranges_from_array
|
def get_ranges_from_array(arr, append_last=True):
'''Takes an array and calculates ranges [start, stop[. The last range end is none to keep the same length.
Parameters
----------
arr : array like
append_last: bool
If True, append item with a pair of last array item and None.
Returns
-------
numpy.array
The array formed by pairs of values by the given array.
Example
-------
>>> a = np.array((1,2,3,4))
>>> get_ranges_from_array(a, append_last=True)
array([[1, 2],
[2, 3],
[3, 4],
[4, None]])
>>> get_ranges_from_array(a, append_last=False)
array([[1, 2],
[2, 3],
[3, 4]])
'''
right = arr[1:]
if append_last:
left = arr[:]
right = np.append(right, None)
else:
left = arr[:-1]
return np.column_stack((left, right))
|
python
|
def get_ranges_from_array(arr, append_last=True):
'''Takes an array and calculates ranges [start, stop[. The last range end is none to keep the same length.
Parameters
----------
arr : array like
append_last: bool
If True, append item with a pair of last array item and None.
Returns
-------
numpy.array
The array formed by pairs of values by the given array.
Example
-------
>>> a = np.array((1,2,3,4))
>>> get_ranges_from_array(a, append_last=True)
array([[1, 2],
[2, 3],
[3, 4],
[4, None]])
>>> get_ranges_from_array(a, append_last=False)
array([[1, 2],
[2, 3],
[3, 4]])
'''
right = arr[1:]
if append_last:
left = arr[:]
right = np.append(right, None)
else:
left = arr[:-1]
return np.column_stack((left, right))
|
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Returns
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numpy.array
The array formed by pairs of values by the given array.
Example
-------
>>> a = np.array((1,2,3,4))
>>> get_ranges_from_array(a, append_last=True)
array([[1, 2],
[2, 3],
[3, 4],
[4, None]])
>>> get_ranges_from_array(a, append_last=False)
array([[1, 2],
[2, 3],
[3, 4]])
|
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"same",
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L111-L144
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
in1d_sorted
|
def in1d_sorted(ar1, ar2):
"""
Does the same than np.in1d but uses the fact that ar1 and ar2 are sorted. Is therefore much faster.
"""
if ar1.shape[0] == 0 or ar2.shape[0] == 0: # check for empty arrays to avoid crash
return []
inds = ar2.searchsorted(ar1)
inds[inds == len(ar2)] = 0
return ar2[inds] == ar1
|
python
|
def in1d_sorted(ar1, ar2):
"""
Does the same than np.in1d but uses the fact that ar1 and ar2 are sorted. Is therefore much faster.
"""
if ar1.shape[0] == 0 or ar2.shape[0] == 0: # check for empty arrays to avoid crash
return []
inds = ar2.searchsorted(ar1)
inds[inds == len(ar2)] = 0
return ar2[inds] == ar1
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L159-L168
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
central_difference
|
def central_difference(x, y):
'''Returns the dy/dx(x) via central difference method
Parameters
----------
x : array like
y : array like
Returns
-------
dy/dx : array like
'''
if (len(x) != len(y)):
raise ValueError("x, y must have the same length")
z1 = np.hstack((y[0], y[:-1]))
z2 = np.hstack((y[1:], y[-1]))
dx1 = np.hstack((0, np.diff(x)))
dx2 = np.hstack((np.diff(x), 0))
return (z2 - z1) / (dx2 + dx1)
|
python
|
def central_difference(x, y):
'''Returns the dy/dx(x) via central difference method
Parameters
----------
x : array like
y : array like
Returns
-------
dy/dx : array like
'''
if (len(x) != len(y)):
raise ValueError("x, y must have the same length")
z1 = np.hstack((y[0], y[:-1]))
z2 = np.hstack((y[1:], y[-1]))
dx1 = np.hstack((0, np.diff(x)))
dx2 = np.hstack((np.diff(x), 0))
return (z2 - z1) / (dx2 + dx1)
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L171-L189
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_profile_histogram
|
def get_profile_histogram(x, y, n_bins=100):
'''Takes 2D point data (x,y) and creates a profile histogram similar to the TProfile in ROOT. It calculates
the y mean for every bin at the bin center and gives the y mean error as error bars.
Parameters
----------
x : array like
data x positions
y : array like
data y positions
n_bins : int
the number of bins used to create the histogram
'''
if len(x) != len(y):
raise ValueError('x and y dimensions have to be the same')
y = y.astype(np.float32)
n, bin_edges = np.histogram(x, bins=n_bins) # needed to calculate the number of points per bin
sy = np.histogram(x, bins=n_bins, weights=y)[0] # the sum of the bin values
sy2 = np.histogram(x, bins=n_bins, weights=y * y)[0] # the quadratic sum of the bin values
bin_centers = (bin_edges[1:] + bin_edges[:-1]) / 2 # calculate the bin center for all bins
mean = sy / n # calculate the mean of all bins
std = np.sqrt((sy2 / n - mean * mean)) # TODO: not understood, need check if this is really the standard deviation
std_mean = std / np.sqrt((n - 1))
mean[np.isnan(mean)] = 0.
std_mean[np.isnan(std_mean)] = 0.
return bin_centers, mean, std_mean
|
python
|
def get_profile_histogram(x, y, n_bins=100):
'''Takes 2D point data (x,y) and creates a profile histogram similar to the TProfile in ROOT. It calculates
the y mean for every bin at the bin center and gives the y mean error as error bars.
Parameters
----------
x : array like
data x positions
y : array like
data y positions
n_bins : int
the number of bins used to create the histogram
'''
if len(x) != len(y):
raise ValueError('x and y dimensions have to be the same')
y = y.astype(np.float32)
n, bin_edges = np.histogram(x, bins=n_bins) # needed to calculate the number of points per bin
sy = np.histogram(x, bins=n_bins, weights=y)[0] # the sum of the bin values
sy2 = np.histogram(x, bins=n_bins, weights=y * y)[0] # the quadratic sum of the bin values
bin_centers = (bin_edges[1:] + bin_edges[:-1]) / 2 # calculate the bin center for all bins
mean = sy / n # calculate the mean of all bins
std = np.sqrt((sy2 / n - mean * mean)) # TODO: not understood, need check if this is really the standard deviation
std_mean = std / np.sqrt((n - 1))
mean[np.isnan(mean)] = 0.
std_mean[np.isnan(std_mean)] = 0.
return bin_centers, mean, std_mean
|
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data x positions
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|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L192-L217
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_rate_normalization
|
def get_rate_normalization(hit_file, parameter, reference='event', cluster_file=None, plot=False, chunk_size=500000):
''' Takes different hit files (hit_files), extracts the number of events or the scan time (reference) per scan parameter (parameter)
and returns an array with a normalization factor. This normalization factor has the length of the number of different parameters.
If a cluster_file is specified also the number of cluster per event are used to create the normalization factor.
Parameters
----------
hit_files : string
parameter : string
reference : string
plot : bool
Returns
-------
numpy.ndarray
'''
logging.info('Calculate the rate normalization')
with tb.open_file(hit_file, mode="r+") as in_hit_file_h5: # open the hit file
meta_data = in_hit_file_h5.root.meta_data[:]
scan_parameter = get_scan_parameter(meta_data)[parameter]
event_numbers = get_meta_data_at_scan_parameter(meta_data, parameter)['event_number'] # get the event numbers in meta_data where the scan parameter changes
event_range = get_ranges_from_array(event_numbers)
normalization_rate = []
normalization_multiplicity = []
try:
event_range[-1, 1] = in_hit_file_h5.root.Hits[-1]['event_number'] + 1
except tb.NoSuchNodeError:
logging.error('Cannot find hits table')
return
# calculate rate normalization from the event rate for triggered data / measurement time for self triggered data for each scan parameter
if reference == 'event':
n_events = event_range[:, 1] - event_range[:, 0] # number of events for every parameter setting
normalization_rate.extend(n_events)
elif reference == 'time':
time_start = get_meta_data_at_scan_parameter(meta_data, parameter)['timestamp_start']
time_spend = np.diff(time_start)
time_spend = np.append(time_spend, meta_data[-1]['timestamp_stop'] - time_start[-1]) # TODO: needs check, add last missing entry
normalization_rate.extend(time_spend)
else:
raise NotImplementedError('The normalization reference ' + reference + ' is not implemented')
if cluster_file: # calculate the rate normalization from the mean number of hits per event per scan parameter, needed for beam data since a beam since the multiplicity is rarely constant
cluster_table = in_hit_file_h5.root.Cluster
index_event_number(cluster_table)
index = 0 # index where to start the read out, 0 at the beginning, increased during looping, variable for read speed up
best_chunk_size = chunk_size # variable for read speed up
total_cluster = 0
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=cluster_table.shape[0], term_width=80)
progress_bar.start()
for start_event, stop_event in event_range: # loop over the selected events
readout_cluster_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
n_cluster_per_event = None
for clusters, index in data_aligned_at_events(cluster_table, start_event_number=start_event, stop_event_number=stop_event, start_index=index, chunk_size=best_chunk_size):
if n_cluster_per_event is None:
n_cluster_per_event = analysis_utils.get_n_cluster_in_events(clusters['event_number'])[:, 1] # array with the number of cluster per event, cluster per event are at least 1
else:
n_cluster_per_event = np.append(n_cluster_per_event, analysis_utils.get_n_cluster_in_events(clusters['event_number'])[:, 1])
readout_cluster_len += clusters.shape[0]
total_cluster += clusters.shape[0]
progress_bar.update(index)
best_chunk_size = int(1.5 * readout_cluster_len) if int(1.05 * readout_cluster_len) < chunk_size else chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
normalization_multiplicity.append(np.mean(n_cluster_per_event))
progress_bar.finish()
if total_cluster != cluster_table.shape[0]:
logging.warning('Analysis shows inconsistent number of cluster (%d != %d). Check needed!', total_cluster, cluster_table.shape[0])
if plot:
x = scan_parameter
if reference == 'event':
plotting.plot_scatter(x, normalization_rate, title='Events per ' + parameter + ' setting', x_label=parameter, y_label='# events', log_x=True, filename=os.path.splitext(hit_file)[0] + '_n_event_normalization.pdf')
elif reference == 'time':
plotting.plot_scatter(x, normalization_rate, title='Measuring time per GDAC setting', x_label=parameter, y_label='time [s]', log_x=True, filename=os.path.splitext(hit_file)[0] + '_time_normalization.pdf')
if cluster_file:
plotting.plot_scatter(x, normalization_multiplicity, title='Mean number of particles per event', x_label=parameter, y_label='number of hits per event', log_x=True, filename=os.path.splitext(hit_file)[0] + '_n_particles_normalization.pdf')
if cluster_file:
normalization_rate = np.array(normalization_rate)
normalization_multiplicity = np.array(normalization_multiplicity)
return np.amax(normalization_rate * normalization_multiplicity).astype('f16') / (normalization_rate * normalization_multiplicity)
return np.amax(np.array(normalization_rate)).astype('f16') / np.array(normalization_rate)
|
python
|
def get_rate_normalization(hit_file, parameter, reference='event', cluster_file=None, plot=False, chunk_size=500000):
''' Takes different hit files (hit_files), extracts the number of events or the scan time (reference) per scan parameter (parameter)
and returns an array with a normalization factor. This normalization factor has the length of the number of different parameters.
If a cluster_file is specified also the number of cluster per event are used to create the normalization factor.
Parameters
----------
hit_files : string
parameter : string
reference : string
plot : bool
Returns
-------
numpy.ndarray
'''
logging.info('Calculate the rate normalization')
with tb.open_file(hit_file, mode="r+") as in_hit_file_h5: # open the hit file
meta_data = in_hit_file_h5.root.meta_data[:]
scan_parameter = get_scan_parameter(meta_data)[parameter]
event_numbers = get_meta_data_at_scan_parameter(meta_data, parameter)['event_number'] # get the event numbers in meta_data where the scan parameter changes
event_range = get_ranges_from_array(event_numbers)
normalization_rate = []
normalization_multiplicity = []
try:
event_range[-1, 1] = in_hit_file_h5.root.Hits[-1]['event_number'] + 1
except tb.NoSuchNodeError:
logging.error('Cannot find hits table')
return
# calculate rate normalization from the event rate for triggered data / measurement time for self triggered data for each scan parameter
if reference == 'event':
n_events = event_range[:, 1] - event_range[:, 0] # number of events for every parameter setting
normalization_rate.extend(n_events)
elif reference == 'time':
time_start = get_meta_data_at_scan_parameter(meta_data, parameter)['timestamp_start']
time_spend = np.diff(time_start)
time_spend = np.append(time_spend, meta_data[-1]['timestamp_stop'] - time_start[-1]) # TODO: needs check, add last missing entry
normalization_rate.extend(time_spend)
else:
raise NotImplementedError('The normalization reference ' + reference + ' is not implemented')
if cluster_file: # calculate the rate normalization from the mean number of hits per event per scan parameter, needed for beam data since a beam since the multiplicity is rarely constant
cluster_table = in_hit_file_h5.root.Cluster
index_event_number(cluster_table)
index = 0 # index where to start the read out, 0 at the beginning, increased during looping, variable for read speed up
best_chunk_size = chunk_size # variable for read speed up
total_cluster = 0
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=cluster_table.shape[0], term_width=80)
progress_bar.start()
for start_event, stop_event in event_range: # loop over the selected events
readout_cluster_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
n_cluster_per_event = None
for clusters, index in data_aligned_at_events(cluster_table, start_event_number=start_event, stop_event_number=stop_event, start_index=index, chunk_size=best_chunk_size):
if n_cluster_per_event is None:
n_cluster_per_event = analysis_utils.get_n_cluster_in_events(clusters['event_number'])[:, 1] # array with the number of cluster per event, cluster per event are at least 1
else:
n_cluster_per_event = np.append(n_cluster_per_event, analysis_utils.get_n_cluster_in_events(clusters['event_number'])[:, 1])
readout_cluster_len += clusters.shape[0]
total_cluster += clusters.shape[0]
progress_bar.update(index)
best_chunk_size = int(1.5 * readout_cluster_len) if int(1.05 * readout_cluster_len) < chunk_size else chunk_size # to increase the readout speed, estimated the number of hits for one read instruction
normalization_multiplicity.append(np.mean(n_cluster_per_event))
progress_bar.finish()
if total_cluster != cluster_table.shape[0]:
logging.warning('Analysis shows inconsistent number of cluster (%d != %d). Check needed!', total_cluster, cluster_table.shape[0])
if plot:
x = scan_parameter
if reference == 'event':
plotting.plot_scatter(x, normalization_rate, title='Events per ' + parameter + ' setting', x_label=parameter, y_label='# events', log_x=True, filename=os.path.splitext(hit_file)[0] + '_n_event_normalization.pdf')
elif reference == 'time':
plotting.plot_scatter(x, normalization_rate, title='Measuring time per GDAC setting', x_label=parameter, y_label='time [s]', log_x=True, filename=os.path.splitext(hit_file)[0] + '_time_normalization.pdf')
if cluster_file:
plotting.plot_scatter(x, normalization_multiplicity, title='Mean number of particles per event', x_label=parameter, y_label='number of hits per event', log_x=True, filename=os.path.splitext(hit_file)[0] + '_n_particles_normalization.pdf')
if cluster_file:
normalization_rate = np.array(normalization_rate)
normalization_multiplicity = np.array(normalization_multiplicity)
return np.amax(normalization_rate * normalization_multiplicity).astype('f16') / (normalization_rate * normalization_multiplicity)
return np.amax(np.array(normalization_rate)).astype('f16') / np.array(normalization_rate)
|
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Takes different hit files (hit_files), extracts the number of events or the scan time (reference) per scan parameter (parameter)
and returns an array with a normalization factor. This normalization factor has the length of the number of different parameters.
If a cluster_file is specified also the number of cluster per event are used to create the normalization factor.
Parameters
----------
hit_files : string
parameter : string
reference : string
plot : bool
Returns
-------
numpy.ndarray
|
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train
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L220-L300
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_parameter_value_from_file_names
|
def get_parameter_value_from_file_names(files, parameters=None, unique=False, sort=True):
"""
Takes a list of files, searches for the parameter name in the file name and returns a ordered dict with the file name
in the first dimension and the corresponding parameter value in the second.
The file names can be sorted by the parameter value, otherwise the order is kept. If unique is true every parameter is unique and
mapped to the file name that occurred last in the files list.
Parameters
----------
files : list of strings
parameter : string or list of strings
unique : bool
sort : bool
Returns
-------
collections.OrderedDict
"""
# unique=False
logging.debug('Get the parameter: ' + str(parameters) + ' values from the file names of ' + str(len(files)) + ' files')
files_dict = collections.OrderedDict()
if parameters is None: # special case, no parameter defined
return files_dict
if isinstance(parameters, basestring):
parameters = (parameters, )
search_string = '_'.join(parameters)
for _ in parameters:
search_string += r'_(-?\d+)'
result = {}
for one_file in files:
parameter_values = re.findall(search_string, one_file)
if parameter_values:
if isinstance(parameter_values[0], tuple):
parameter_values = list(reduce(lambda t1, t2: t1 + t2, parameter_values))
parameter_values = [[int(i), ] for i in parameter_values] # convert string value to list with int
files_dict[one_file] = dict(zip(parameters, parameter_values))
if unique: # reduce to the files with different scan parameters
for key, value in files_dict.items():
if value not in result.values():
result[key] = value
else:
result[one_file] = files_dict[one_file]
return collections.OrderedDict(sorted(result.iteritems(), key=itemgetter(1)) if sort else files_dict)
|
python
|
def get_parameter_value_from_file_names(files, parameters=None, unique=False, sort=True):
"""
Takes a list of files, searches for the parameter name in the file name and returns a ordered dict with the file name
in the first dimension and the corresponding parameter value in the second.
The file names can be sorted by the parameter value, otherwise the order is kept. If unique is true every parameter is unique and
mapped to the file name that occurred last in the files list.
Parameters
----------
files : list of strings
parameter : string or list of strings
unique : bool
sort : bool
Returns
-------
collections.OrderedDict
"""
# unique=False
logging.debug('Get the parameter: ' + str(parameters) + ' values from the file names of ' + str(len(files)) + ' files')
files_dict = collections.OrderedDict()
if parameters is None: # special case, no parameter defined
return files_dict
if isinstance(parameters, basestring):
parameters = (parameters, )
search_string = '_'.join(parameters)
for _ in parameters:
search_string += r'_(-?\d+)'
result = {}
for one_file in files:
parameter_values = re.findall(search_string, one_file)
if parameter_values:
if isinstance(parameter_values[0], tuple):
parameter_values = list(reduce(lambda t1, t2: t1 + t2, parameter_values))
parameter_values = [[int(i), ] for i in parameter_values] # convert string value to list with int
files_dict[one_file] = dict(zip(parameters, parameter_values))
if unique: # reduce to the files with different scan parameters
for key, value in files_dict.items():
if value not in result.values():
result[key] = value
else:
result[one_file] = files_dict[one_file]
return collections.OrderedDict(sorted(result.iteritems(), key=itemgetter(1)) if sort else files_dict)
|
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Takes a list of files, searches for the parameter name in the file name and returns a ordered dict with the file name
in the first dimension and the corresponding parameter value in the second.
The file names can be sorted by the parameter value, otherwise the order is kept. If unique is true every parameter is unique and
mapped to the file name that occurred last in the files list.
Parameters
----------
files : list of strings
parameter : string or list of strings
unique : bool
sort : bool
Returns
-------
collections.OrderedDict
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L361-L404
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_data_file_names_from_scan_base
|
def get_data_file_names_from_scan_base(scan_base, filter_str=['_analyzed.h5', '_interpreted.h5', '_cut.h5', '_result.h5', '_hists.h5'], sort_by_time=True, meta_data_v2=True):
"""
Generate a list of .h5 files which have a similar file name.
Parameters
----------
scan_base : list, string
List of string or string of the scan base names. The scan_base will be used to search for files containing the string. The .h5 file extension will be added automatically.
filter : list, string
List of string or string which are used to filter the returned filenames. File names containing filter_str in the file name will not be returned. Use None to disable filter.
sort_by_time : bool
If True, return file name list sorted from oldest to newest. The time from meta table will be used to sort the files.
meta_data_v2 : bool
True for new (v2) meta data format, False for the old (v1) format.
Returns
-------
data_files : list
List of file names matching the obove conditions.
"""
data_files = []
if scan_base is None:
return data_files
if isinstance(scan_base, basestring):
scan_base = [scan_base]
for scan_base_str in scan_base:
if '.h5' == os.path.splitext(scan_base_str)[1]:
data_files.append(scan_base_str)
else:
data_files.extend(glob.glob(scan_base_str + '*.h5'))
if filter_str:
if isinstance(filter_str, basestring):
filter_str = [filter_str]
data_files = filter(lambda data_file: not any([(True if x in data_file else False) for x in filter_str]), data_files)
if sort_by_time and len(data_files) > 1:
f_list = {}
for data_file in data_files:
with tb.open_file(data_file, mode="r") as h5_file:
try:
meta_data = h5_file.root.meta_data
except tb.NoSuchNodeError:
logging.warning("File %s is missing meta_data" % h5_file.filename)
else:
try:
if meta_data_v2:
timestamp = meta_data[0]["timestamp_start"]
else:
timestamp = meta_data[0]["timestamp"]
except IndexError:
logging.info("File %s has empty meta_data" % h5_file.filename)
else:
f_list[data_file] = timestamp
data_files = list(sorted(f_list, key=f_list.__getitem__, reverse=False))
return data_files
|
python
|
def get_data_file_names_from_scan_base(scan_base, filter_str=['_analyzed.h5', '_interpreted.h5', '_cut.h5', '_result.h5', '_hists.h5'], sort_by_time=True, meta_data_v2=True):
"""
Generate a list of .h5 files which have a similar file name.
Parameters
----------
scan_base : list, string
List of string or string of the scan base names. The scan_base will be used to search for files containing the string. The .h5 file extension will be added automatically.
filter : list, string
List of string or string which are used to filter the returned filenames. File names containing filter_str in the file name will not be returned. Use None to disable filter.
sort_by_time : bool
If True, return file name list sorted from oldest to newest. The time from meta table will be used to sort the files.
meta_data_v2 : bool
True for new (v2) meta data format, False for the old (v1) format.
Returns
-------
data_files : list
List of file names matching the obove conditions.
"""
data_files = []
if scan_base is None:
return data_files
if isinstance(scan_base, basestring):
scan_base = [scan_base]
for scan_base_str in scan_base:
if '.h5' == os.path.splitext(scan_base_str)[1]:
data_files.append(scan_base_str)
else:
data_files.extend(glob.glob(scan_base_str + '*.h5'))
if filter_str:
if isinstance(filter_str, basestring):
filter_str = [filter_str]
data_files = filter(lambda data_file: not any([(True if x in data_file else False) for x in filter_str]), data_files)
if sort_by_time and len(data_files) > 1:
f_list = {}
for data_file in data_files:
with tb.open_file(data_file, mode="r") as h5_file:
try:
meta_data = h5_file.root.meta_data
except tb.NoSuchNodeError:
logging.warning("File %s is missing meta_data" % h5_file.filename)
else:
try:
if meta_data_v2:
timestamp = meta_data[0]["timestamp_start"]
else:
timestamp = meta_data[0]["timestamp"]
except IndexError:
logging.info("File %s has empty meta_data" % h5_file.filename)
else:
f_list[data_file] = timestamp
data_files = list(sorted(f_list, key=f_list.__getitem__, reverse=False))
return data_files
|
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Generate a list of .h5 files which have a similar file name.
Parameters
----------
scan_base : list, string
List of string or string of the scan base names. The scan_base will be used to search for files containing the string. The .h5 file extension will be added automatically.
filter : list, string
List of string or string which are used to filter the returned filenames. File names containing filter_str in the file name will not be returned. Use None to disable filter.
sort_by_time : bool
If True, return file name list sorted from oldest to newest. The time from meta table will be used to sort the files.
meta_data_v2 : bool
True for new (v2) meta data format, False for the old (v1) format.
Returns
-------
data_files : list
List of file names matching the obove conditions.
|
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] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L407-L462
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_parameter_from_files
|
def get_parameter_from_files(files, parameters=None, unique=False, sort=True):
''' Takes a list of files, searches for the parameter name in the file name and in the file.
Returns a ordered dict with the file name in the first dimension and the corresponding parameter values in the second.
If a scan parameter appears in the file name and in the file the first parameter setting has to be in the file name, otherwise a warning is shown.
The file names can be sorted by the first parameter value of each file.
Parameters
----------
files : string, list of strings
parameters : string, list of strings
unique : boolean
If set only one file per scan parameter value is used.
sort : boolean
Returns
-------
collections.OrderedDict
'''
logging.debug('Get the parameter ' + str(parameters) + ' values from ' + str(len(files)) + ' files')
files_dict = collections.OrderedDict()
if isinstance(files, basestring):
files = (files, )
if isinstance(parameters, basestring):
parameters = (parameters, )
parameter_values_from_file_names_dict = get_parameter_value_from_file_names(files, parameters, unique=unique, sort=sort) # get the parameter from the file name
for file_name in files:
with tb.open_file(file_name, mode="r") as in_file_h5: # open the actual file
scan_parameter_values = collections.OrderedDict()
try:
scan_parameters = in_file_h5.root.scan_parameters[:] # get the scan parameters from the scan parameter table
if parameters is None:
parameters = get_scan_parameter_names(scan_parameters)
for parameter in parameters:
try:
scan_parameter_values[parameter] = np.unique(scan_parameters[parameter]).tolist() # different scan parameter values used
except ValueError: # the scan parameter does not exists
pass
except tb.NoSuchNodeError: # scan parameter table does not exist
try:
scan_parameters = get_scan_parameter(in_file_h5.root.meta_data[:]) # get the scan parameters from the meta data
if scan_parameters:
try:
scan_parameter_values = np.unique(scan_parameters[parameters]).tolist() # different scan parameter values used
except ValueError: # the scan parameter does not exists
pass
except tb.NoSuchNodeError: # meta data table does not exist
pass
if not scan_parameter_values: # if no scan parameter values could be set from file take the parameter found in the file name
try:
scan_parameter_values = parameter_values_from_file_names_dict[file_name]
except KeyError: # no scan parameter found at all, neither in the file name nor in the file
scan_parameter_values = None
else: # use the parameter given in the file and cross check if it matches the file name parameter if these is given
try:
for key, value in scan_parameter_values.items():
if value and value[0] != parameter_values_from_file_names_dict[file_name][key][0]: # parameter value exists: check if the first value is the file name value
logging.warning('Parameter values in the file name and in the file differ. Take ' + str(key) + ' parameters ' + str(value) + ' found in %s.', file_name)
except KeyError: # parameter does not exists in the file name
pass
except IndexError:
raise IncompleteInputError('Something wrong check!')
if unique and scan_parameter_values is not None:
existing = False
for parameter in scan_parameter_values: # loop to determine if any value of any scan parameter exists already
all_par_values = [values[parameter] for values in files_dict.values()]
if any(x in [scan_parameter_values[parameter]] for x in all_par_values):
existing = True
break
if not existing:
files_dict[file_name] = scan_parameter_values
else:
logging.warning('Scan parameter value(s) from %s exists already, do not add to result', file_name)
else:
files_dict[file_name] = scan_parameter_values
return collections.OrderedDict(sorted(files_dict.iteritems(), key=itemgetter(1)) if sort else files_dict)
|
python
|
def get_parameter_from_files(files, parameters=None, unique=False, sort=True):
''' Takes a list of files, searches for the parameter name in the file name and in the file.
Returns a ordered dict with the file name in the first dimension and the corresponding parameter values in the second.
If a scan parameter appears in the file name and in the file the first parameter setting has to be in the file name, otherwise a warning is shown.
The file names can be sorted by the first parameter value of each file.
Parameters
----------
files : string, list of strings
parameters : string, list of strings
unique : boolean
If set only one file per scan parameter value is used.
sort : boolean
Returns
-------
collections.OrderedDict
'''
logging.debug('Get the parameter ' + str(parameters) + ' values from ' + str(len(files)) + ' files')
files_dict = collections.OrderedDict()
if isinstance(files, basestring):
files = (files, )
if isinstance(parameters, basestring):
parameters = (parameters, )
parameter_values_from_file_names_dict = get_parameter_value_from_file_names(files, parameters, unique=unique, sort=sort) # get the parameter from the file name
for file_name in files:
with tb.open_file(file_name, mode="r") as in_file_h5: # open the actual file
scan_parameter_values = collections.OrderedDict()
try:
scan_parameters = in_file_h5.root.scan_parameters[:] # get the scan parameters from the scan parameter table
if parameters is None:
parameters = get_scan_parameter_names(scan_parameters)
for parameter in parameters:
try:
scan_parameter_values[parameter] = np.unique(scan_parameters[parameter]).tolist() # different scan parameter values used
except ValueError: # the scan parameter does not exists
pass
except tb.NoSuchNodeError: # scan parameter table does not exist
try:
scan_parameters = get_scan_parameter(in_file_h5.root.meta_data[:]) # get the scan parameters from the meta data
if scan_parameters:
try:
scan_parameter_values = np.unique(scan_parameters[parameters]).tolist() # different scan parameter values used
except ValueError: # the scan parameter does not exists
pass
except tb.NoSuchNodeError: # meta data table does not exist
pass
if not scan_parameter_values: # if no scan parameter values could be set from file take the parameter found in the file name
try:
scan_parameter_values = parameter_values_from_file_names_dict[file_name]
except KeyError: # no scan parameter found at all, neither in the file name nor in the file
scan_parameter_values = None
else: # use the parameter given in the file and cross check if it matches the file name parameter if these is given
try:
for key, value in scan_parameter_values.items():
if value and value[0] != parameter_values_from_file_names_dict[file_name][key][0]: # parameter value exists: check if the first value is the file name value
logging.warning('Parameter values in the file name and in the file differ. Take ' + str(key) + ' parameters ' + str(value) + ' found in %s.', file_name)
except KeyError: # parameter does not exists in the file name
pass
except IndexError:
raise IncompleteInputError('Something wrong check!')
if unique and scan_parameter_values is not None:
existing = False
for parameter in scan_parameter_values: # loop to determine if any value of any scan parameter exists already
all_par_values = [values[parameter] for values in files_dict.values()]
if any(x in [scan_parameter_values[parameter]] for x in all_par_values):
existing = True
break
if not existing:
files_dict[file_name] = scan_parameter_values
else:
logging.warning('Scan parameter value(s) from %s exists already, do not add to result', file_name)
else:
files_dict[file_name] = scan_parameter_values
return collections.OrderedDict(sorted(files_dict.iteritems(), key=itemgetter(1)) if sort else files_dict)
|
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collections.OrderedDict
|
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L480-L555
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
check_parameter_similarity
|
def check_parameter_similarity(files_dict):
"""
Checks if the parameter names of all files are similar. Takes the dictionary from get_parameter_from_files output as input.
"""
try:
parameter_names = files_dict.itervalues().next().keys() # get the parameter names of the first file, to check if these are the same in the other files
except AttributeError: # if there is no parameter at all
if any(i is not None for i in files_dict.itervalues()): # check if there is also no parameter for the other files
return False
else:
return True
if any(parameter_names != i.keys() for i in files_dict.itervalues()):
return False
return True
|
python
|
def check_parameter_similarity(files_dict):
"""
Checks if the parameter names of all files are similar. Takes the dictionary from get_parameter_from_files output as input.
"""
try:
parameter_names = files_dict.itervalues().next().keys() # get the parameter names of the first file, to check if these are the same in the other files
except AttributeError: # if there is no parameter at all
if any(i is not None for i in files_dict.itervalues()): # check if there is also no parameter for the other files
return False
else:
return True
if any(parameter_names != i.keys() for i in files_dict.itervalues()):
return False
return True
|
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train
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L558-L572
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
combine_meta_data
|
def combine_meta_data(files_dict, meta_data_v2=True):
"""
Takes the dict of hdf5 files and combines their meta data tables into one new numpy record array.
Parameters
----------
meta_data_v2 : bool
True for new (v2) meta data format, False for the old (v1) format.
"""
if len(files_dict) > 10:
logging.info("Combine the meta data from %d files", len(files_dict))
# determine total length needed for the new combined array, thats the fastest way to combine arrays
total_length = 0 # the total length of the new table
for file_name in files_dict.iterkeys():
with tb.open_file(file_name, mode="r") as in_file_h5: # open the actual file
total_length += in_file_h5.root.meta_data.shape[0]
if meta_data_v2:
meta_data_combined = np.empty((total_length, ), dtype=[
('index_start', np.uint32),
('index_stop', np.uint32),
('data_length', np.uint32),
('timestamp_start', np.float64),
('timestamp_stop', np.float64),
('error', np.uint32)])
else:
meta_data_combined = np.empty((total_length, ), dtype=[
('start_index', np.uint32),
('stop_index', np.uint32),
('length', np.uint32),
('timestamp', np.float64),
('error', np.uint32)])
if len(files_dict) > 10:
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=total_length, term_width=80)
progress_bar.start()
index = 0
# fill actual result array
for file_name in files_dict.iterkeys():
with tb.open_file(file_name, mode="r") as in_file_h5: # open the actual file
array_length = in_file_h5.root.meta_data.shape[0]
meta_data_combined[index:index + array_length] = in_file_h5.root.meta_data[:]
index += array_length
if len(files_dict) > 10:
progress_bar.update(index)
if len(files_dict) > 10:
progress_bar.finish()
return meta_data_combined
|
python
|
def combine_meta_data(files_dict, meta_data_v2=True):
"""
Takes the dict of hdf5 files and combines their meta data tables into one new numpy record array.
Parameters
----------
meta_data_v2 : bool
True for new (v2) meta data format, False for the old (v1) format.
"""
if len(files_dict) > 10:
logging.info("Combine the meta data from %d files", len(files_dict))
# determine total length needed for the new combined array, thats the fastest way to combine arrays
total_length = 0 # the total length of the new table
for file_name in files_dict.iterkeys():
with tb.open_file(file_name, mode="r") as in_file_h5: # open the actual file
total_length += in_file_h5.root.meta_data.shape[0]
if meta_data_v2:
meta_data_combined = np.empty((total_length, ), dtype=[
('index_start', np.uint32),
('index_stop', np.uint32),
('data_length', np.uint32),
('timestamp_start', np.float64),
('timestamp_stop', np.float64),
('error', np.uint32)])
else:
meta_data_combined = np.empty((total_length, ), dtype=[
('start_index', np.uint32),
('stop_index', np.uint32),
('length', np.uint32),
('timestamp', np.float64),
('error', np.uint32)])
if len(files_dict) > 10:
progress_bar = progressbar.ProgressBar(widgets=['', progressbar.Percentage(), ' ', progressbar.Bar(marker='*', left='|', right='|'), ' ', progressbar.AdaptiveETA()], maxval=total_length, term_width=80)
progress_bar.start()
index = 0
# fill actual result array
for file_name in files_dict.iterkeys():
with tb.open_file(file_name, mode="r") as in_file_h5: # open the actual file
array_length = in_file_h5.root.meta_data.shape[0]
meta_data_combined[index:index + array_length] = in_file_h5.root.meta_data[:]
index += array_length
if len(files_dict) > 10:
progress_bar.update(index)
if len(files_dict) > 10:
progress_bar.finish()
return meta_data_combined
|
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Parameters
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True for new (v2) meta data format, False for the old (v1) format.
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L575-L624
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
smooth_differentiation
|
def smooth_differentiation(x, y, weigths=None, order=5, smoothness=3, derivation=1):
'''Returns the dy/dx(x) with the fit and differentiation of a spline curve
Parameters
----------
x : array like
y : array like
Returns
-------
dy/dx : array like
'''
if (len(x) != len(y)):
raise ValueError("x, y must have the same length")
f = splrep(x, y, w=weigths, k=order, s=smoothness) # spline function
return splev(x, f, der=derivation)
|
python
|
def smooth_differentiation(x, y, weigths=None, order=5, smoothness=3, derivation=1):
'''Returns the dy/dx(x) with the fit and differentiation of a spline curve
Parameters
----------
x : array like
y : array like
Returns
-------
dy/dx : array like
'''
if (len(x) != len(y)):
raise ValueError("x, y must have the same length")
f = splrep(x, y, w=weigths, k=order, s=smoothness) # spline function
return splev(x, f, der=derivation)
|
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train
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L627-L642
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
reduce_sorted_to_intersect
|
def reduce_sorted_to_intersect(ar1, ar2):
"""
Takes two sorted arrays and return the intersection ar1 in ar2, ar2 in ar1.
Parameters
----------
ar1 : (M,) array_like
Input array.
ar2 : array_like
Input array.
Returns
-------
ar1, ar1 : ndarray, ndarray
The intersection values.
"""
# Ravel both arrays, behavior for the first array could be different
ar1 = np.asarray(ar1).ravel()
ar2 = np.asarray(ar2).ravel()
# get min max values of the arrays
ar1_biggest_value = ar1[-1]
ar1_smallest_value = ar1[0]
ar2_biggest_value = ar2[-1]
ar2_smallest_value = ar2[0]
if ar1_biggest_value < ar2_smallest_value or ar1_smallest_value > ar2_biggest_value: # special case, no intersection at all
return ar1[0:0], ar2[0:0]
# get min/max indices with values that are also in the other array
min_index_ar1 = np.argmin(ar1 < ar2_smallest_value)
max_index_ar1 = np.argmax(ar1 > ar2_biggest_value)
min_index_ar2 = np.argmin(ar2 < ar1_smallest_value)
max_index_ar2 = np.argmax(ar2 > ar1_biggest_value)
if min_index_ar1 < 0:
min_index_ar1 = 0
if min_index_ar2 < 0:
min_index_ar2 = 0
if max_index_ar1 == 0 or max_index_ar1 > ar1.shape[0]:
max_index_ar1 = ar1.shape[0]
if max_index_ar2 == 0 or max_index_ar2 > ar2.shape[0]:
max_index_ar2 = ar2.shape[0]
# reduce the data
return ar1[min_index_ar1:max_index_ar1], ar2[min_index_ar2:max_index_ar2]
|
python
|
def reduce_sorted_to_intersect(ar1, ar2):
"""
Takes two sorted arrays and return the intersection ar1 in ar2, ar2 in ar1.
Parameters
----------
ar1 : (M,) array_like
Input array.
ar2 : array_like
Input array.
Returns
-------
ar1, ar1 : ndarray, ndarray
The intersection values.
"""
# Ravel both arrays, behavior for the first array could be different
ar1 = np.asarray(ar1).ravel()
ar2 = np.asarray(ar2).ravel()
# get min max values of the arrays
ar1_biggest_value = ar1[-1]
ar1_smallest_value = ar1[0]
ar2_biggest_value = ar2[-1]
ar2_smallest_value = ar2[0]
if ar1_biggest_value < ar2_smallest_value or ar1_smallest_value > ar2_biggest_value: # special case, no intersection at all
return ar1[0:0], ar2[0:0]
# get min/max indices with values that are also in the other array
min_index_ar1 = np.argmin(ar1 < ar2_smallest_value)
max_index_ar1 = np.argmax(ar1 > ar2_biggest_value)
min_index_ar2 = np.argmin(ar2 < ar1_smallest_value)
max_index_ar2 = np.argmax(ar2 > ar1_biggest_value)
if min_index_ar1 < 0:
min_index_ar1 = 0
if min_index_ar2 < 0:
min_index_ar2 = 0
if max_index_ar1 == 0 or max_index_ar1 > ar1.shape[0]:
max_index_ar1 = ar1.shape[0]
if max_index_ar2 == 0 or max_index_ar2 > ar2.shape[0]:
max_index_ar2 = ar2.shape[0]
# reduce the data
return ar1[min_index_ar1:max_index_ar1], ar2[min_index_ar2:max_index_ar2]
|
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Input array.
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Input array.
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The intersection values.
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L645-L691
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_not_unique_values
|
def get_not_unique_values(array):
'''Returns the values that appear at least twice in array.
Parameters
----------
array : array like
Returns
-------
numpy.array
'''
s = np.sort(array, axis=None)
s = s[s[1:] == s[:-1]]
return np.unique(s)
|
python
|
def get_not_unique_values(array):
'''Returns the values that appear at least twice in array.
Parameters
----------
array : array like
Returns
-------
numpy.array
'''
s = np.sort(array, axis=None)
s = s[s[1:] == s[:-1]]
return np.unique(s)
|
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Returns the values that appear at least twice in array.
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L694-L707
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_meta_data_index_at_scan_parameter
|
def get_meta_data_index_at_scan_parameter(meta_data_array, scan_parameter_name):
'''Takes the analyzed meta_data table and returns the indices where the scan parameter changes
Parameters
----------
meta_data_array : numpy.recordarray
scan_parameter_name : string
Returns
-------
numpy.ndarray:
first dimension: scan parameter value
second dimension: index where scan parameter value was used first
'''
scan_parameter_values = meta_data_array[scan_parameter_name]
diff = np.concatenate(([1], np.diff(scan_parameter_values)))
idx = np.concatenate((np.where(diff)[0], [len(scan_parameter_values)]))
index = np.empty(len(idx) - 1, dtype={'names': [scan_parameter_name, 'index'], 'formats': ['u4', 'u4']})
index[scan_parameter_name] = scan_parameter_values[idx[:-1]]
index['index'] = idx[:-1]
return index
|
python
|
def get_meta_data_index_at_scan_parameter(meta_data_array, scan_parameter_name):
'''Takes the analyzed meta_data table and returns the indices where the scan parameter changes
Parameters
----------
meta_data_array : numpy.recordarray
scan_parameter_name : string
Returns
-------
numpy.ndarray:
first dimension: scan parameter value
second dimension: index where scan parameter value was used first
'''
scan_parameter_values = meta_data_array[scan_parameter_name]
diff = np.concatenate(([1], np.diff(scan_parameter_values)))
idx = np.concatenate((np.where(diff)[0], [len(scan_parameter_values)]))
index = np.empty(len(idx) - 1, dtype={'names': [scan_parameter_name, 'index'], 'formats': ['u4', 'u4']})
index[scan_parameter_name] = scan_parameter_values[idx[:-1]]
index['index'] = idx[:-1]
return index
|
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Takes the analyzed meta_data table and returns the indices where the scan parameter changes
Parameters
----------
meta_data_array : numpy.recordarray
scan_parameter_name : string
Returns
-------
numpy.ndarray:
first dimension: scan parameter value
second dimension: index where scan parameter value was used first
|
[
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L710-L730
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
select_hits
|
def select_hits(hits_array, condition=None):
'''Selects the hits with condition.
E.g.: condition = 'rel_BCID == 7 & event_number < 1000'
Parameters
----------
hits_array : numpy.array
condition : string
A condition that is applied to the hits in numexpr. Only if the expression evaluates to True the hit is taken.
Returns
-------
numpy.array
hit array with the selceted hits
'''
if condition is None:
return hits_array
for variable in set(re.findall(r'[a-zA-Z_]+', condition)):
exec(variable + ' = hits_array[\'' + variable + '\']')
return hits_array[ne.evaluate(condition)]
|
python
|
def select_hits(hits_array, condition=None):
'''Selects the hits with condition.
E.g.: condition = 'rel_BCID == 7 & event_number < 1000'
Parameters
----------
hits_array : numpy.array
condition : string
A condition that is applied to the hits in numexpr. Only if the expression evaluates to True the hit is taken.
Returns
-------
numpy.array
hit array with the selceted hits
'''
if condition is None:
return hits_array
for variable in set(re.findall(r'[a-zA-Z_]+', condition)):
exec(variable + ' = hits_array[\'' + variable + '\']')
return hits_array[ne.evaluate(condition)]
|
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Selects the hits with condition.
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A condition that is applied to the hits in numexpr. Only if the expression evaluates to True the hit is taken.
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-------
numpy.array
hit array with the selceted hits
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L749-L770
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_hits_in_events
|
def get_hits_in_events(hits_array, events, assume_sorted=True, condition=None):
'''Selects the hits that occurred in events and optional selection criterion.
If a event range can be defined use the get_data_in_event_range function. It is much faster.
Parameters
----------
hits_array : numpy.array
events : array
assume_sorted : bool
Is true if the events to select are sorted from low to high value. Increases speed by 35%.
condition : string
A condition that is applied to the hits in numexpr. Only if the expression evaluates to True the hit is taken.
Returns
-------
numpy.array
hit array with the hits in events.
'''
logging.debug("Calculate hits that exists in the given %d events." % len(events))
if assume_sorted:
events, _ = reduce_sorted_to_intersect(events, hits_array['event_number']) # reduce the event number range to the max min event number of the given hits to save time
if events.shape[0] == 0: # if there is not a single selected hit
return hits_array[0:0]
try:
if assume_sorted:
selection = analysis_utils.in1d_events(hits_array['event_number'], events)
else:
logging.warning('Events are usually sorted. Are you sure you want this?')
selection = np.in1d(hits_array['event_number'], events)
if condition is None:
hits_in_events = hits_array[selection]
else:
# bad hack to be able to use numexpr
for variable in set(re.findall(r'[a-zA-Z_]+', condition)):
exec(variable + ' = hits_array[\'' + variable + '\']')
hits_in_events = hits_array[ne.evaluate(condition + ' & selection')]
except MemoryError:
logging.error('There are too many hits to do in RAM operations. Consider decreasing chunk size and use the write_hits_in_events function instead.')
raise MemoryError
return hits_in_events
|
python
|
def get_hits_in_events(hits_array, events, assume_sorted=True, condition=None):
'''Selects the hits that occurred in events and optional selection criterion.
If a event range can be defined use the get_data_in_event_range function. It is much faster.
Parameters
----------
hits_array : numpy.array
events : array
assume_sorted : bool
Is true if the events to select are sorted from low to high value. Increases speed by 35%.
condition : string
A condition that is applied to the hits in numexpr. Only if the expression evaluates to True the hit is taken.
Returns
-------
numpy.array
hit array with the hits in events.
'''
logging.debug("Calculate hits that exists in the given %d events." % len(events))
if assume_sorted:
events, _ = reduce_sorted_to_intersect(events, hits_array['event_number']) # reduce the event number range to the max min event number of the given hits to save time
if events.shape[0] == 0: # if there is not a single selected hit
return hits_array[0:0]
try:
if assume_sorted:
selection = analysis_utils.in1d_events(hits_array['event_number'], events)
else:
logging.warning('Events are usually sorted. Are you sure you want this?')
selection = np.in1d(hits_array['event_number'], events)
if condition is None:
hits_in_events = hits_array[selection]
else:
# bad hack to be able to use numexpr
for variable in set(re.findall(r'[a-zA-Z_]+', condition)):
exec(variable + ' = hits_array[\'' + variable + '\']')
hits_in_events = hits_array[ne.evaluate(condition + ' & selection')]
except MemoryError:
logging.error('There are too many hits to do in RAM operations. Consider decreasing chunk size and use the write_hits_in_events function instead.')
raise MemoryError
return hits_in_events
|
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Selects the hits that occurred in events and optional selection criterion.
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Parameters
----------
hits_array : numpy.array
events : array
assume_sorted : bool
Is true if the events to select are sorted from low to high value. Increases speed by 35%.
condition : string
A condition that is applied to the hits in numexpr. Only if the expression evaluates to True the hit is taken.
Returns
-------
numpy.array
hit array with the hits in events.
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L773-L814
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_hits_of_scan_parameter
|
def get_hits_of_scan_parameter(input_file_hits, scan_parameters=None, try_speedup=False, chunk_size=10000000):
'''Takes the hit table of a hdf5 file and returns hits in chunks for each unique combination of scan_parameters.
Yields the hits in chunks, since they usually do not fit into memory.
Parameters
----------
input_file_hits : pytable hdf5 file
Has to include a hits node
scan_parameters : iterable with strings
try_speedup : bool
If true a speed up by searching for the event numbers in the data is done. If the event numbers are not in the data
this slows down the search.
chunk_size : int
How many rows of data are read into ram.
Returns
-------
Yields tuple, numpy.array
Actual scan parameter tuple, hit array with the hits of a chunk of the given scan parameter tuple
'''
with tb.open_file(input_file_hits, mode="r+") as in_file_h5:
hit_table = in_file_h5.root.Hits
meta_data = in_file_h5.root.meta_data[:]
meta_data_table_at_scan_parameter = get_unique_scan_parameter_combinations(meta_data, scan_parameters=scan_parameters)
parameter_values = get_scan_parameters_table_from_meta_data(meta_data_table_at_scan_parameter, scan_parameters)
event_number_ranges = get_ranges_from_array(meta_data_table_at_scan_parameter['event_number']) # get the event number ranges for the different scan parameter settings
index_event_number(hit_table) # create a event_numer index to select the hits by their event number fast, no needed but important for speed up
#
# variables for read speed up
index = 0 # index where to start the read out of the hit table, 0 at the beginning, increased during looping
best_chunk_size = chunk_size # number of hits to copy to RAM during looping, the optimal chunk size is determined during looping
# loop over the selected events
for parameter_index, (start_event_number, stop_event_number) in enumerate(event_number_ranges):
logging.debug('Read hits for ' + str(scan_parameters) + ' = ' + str(parameter_values[parameter_index]))
readout_hit_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
# loop over the hits in the actual selected events with optimizations: determine best chunk size, start word index given
for hits, index in data_aligned_at_events(hit_table, start_event_number=start_event_number, stop_event_number=stop_event_number, start_index=index, try_speedup=try_speedup, chunk_size=best_chunk_size):
yield parameter_values[parameter_index], hits
readout_hit_len += hits.shape[0]
best_chunk_size = int(1.5 * readout_hit_len) if int(1.05 * readout_hit_len) < chunk_size and int(1.05 * readout_hit_len) > 1e3 else chunk_size
|
python
|
def get_hits_of_scan_parameter(input_file_hits, scan_parameters=None, try_speedup=False, chunk_size=10000000):
'''Takes the hit table of a hdf5 file and returns hits in chunks for each unique combination of scan_parameters.
Yields the hits in chunks, since they usually do not fit into memory.
Parameters
----------
input_file_hits : pytable hdf5 file
Has to include a hits node
scan_parameters : iterable with strings
try_speedup : bool
If true a speed up by searching for the event numbers in the data is done. If the event numbers are not in the data
this slows down the search.
chunk_size : int
How many rows of data are read into ram.
Returns
-------
Yields tuple, numpy.array
Actual scan parameter tuple, hit array with the hits of a chunk of the given scan parameter tuple
'''
with tb.open_file(input_file_hits, mode="r+") as in_file_h5:
hit_table = in_file_h5.root.Hits
meta_data = in_file_h5.root.meta_data[:]
meta_data_table_at_scan_parameter = get_unique_scan_parameter_combinations(meta_data, scan_parameters=scan_parameters)
parameter_values = get_scan_parameters_table_from_meta_data(meta_data_table_at_scan_parameter, scan_parameters)
event_number_ranges = get_ranges_from_array(meta_data_table_at_scan_parameter['event_number']) # get the event number ranges for the different scan parameter settings
index_event_number(hit_table) # create a event_numer index to select the hits by their event number fast, no needed but important for speed up
#
# variables for read speed up
index = 0 # index where to start the read out of the hit table, 0 at the beginning, increased during looping
best_chunk_size = chunk_size # number of hits to copy to RAM during looping, the optimal chunk size is determined during looping
# loop over the selected events
for parameter_index, (start_event_number, stop_event_number) in enumerate(event_number_ranges):
logging.debug('Read hits for ' + str(scan_parameters) + ' = ' + str(parameter_values[parameter_index]))
readout_hit_len = 0 # variable to calculate a optimal chunk size value from the number of hits for speed up
# loop over the hits in the actual selected events with optimizations: determine best chunk size, start word index given
for hits, index in data_aligned_at_events(hit_table, start_event_number=start_event_number, stop_event_number=stop_event_number, start_index=index, try_speedup=try_speedup, chunk_size=best_chunk_size):
yield parameter_values[parameter_index], hits
readout_hit_len += hits.shape[0]
best_chunk_size = int(1.5 * readout_hit_len) if int(1.05 * readout_hit_len) < chunk_size and int(1.05 * readout_hit_len) > 1e3 else chunk_size
|
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input_file_hits : pytable hdf5 file
Has to include a hits node
scan_parameters : iterable with strings
try_speedup : bool
If true a speed up by searching for the event numbers in the data is done. If the event numbers are not in the data
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chunk_size : int
How many rows of data are read into ram.
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|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L817-L859
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_data_in_event_range
|
def get_data_in_event_range(array, event_start=None, event_stop=None, assume_sorted=True):
'''Selects the data (rows of a table) that occurred in the given event range [event_start, event_stop[
Parameters
----------
array : numpy.array
event_start : int, None
event_stop : int, None
assume_sorted : bool
Set to true if the hits are sorted by the event_number. Increases speed.
Returns
-------
numpy.array
hit array with the hits in the event range.
'''
logging.debug("Calculate data of the the given event range [" + str(event_start) + ", " + str(event_stop) + "[")
event_number = array['event_number']
if assume_sorted:
data_event_start = event_number[0]
data_event_stop = event_number[-1]
if (event_start is not None and event_stop is not None) and (data_event_stop < event_start or data_event_start > event_stop or event_start == event_stop): # special case, no intersection at all
return array[0:0]
# get min/max indices with values that are also in the other array
if event_start is None:
min_index_data = 0
else:
min_index_data = np.argmin(event_number < event_start)
if event_stop is None:
max_index_data = event_number.shape[0]
else:
max_index_data = np.argmax(event_number >= event_stop)
if min_index_data < 0:
min_index_data = 0
if max_index_data == 0 or max_index_data > event_number.shape[0]:
max_index_data = event_number.shape[0]
return array[min_index_data:max_index_data]
else:
return array[ne.evaluate('event_number >= event_start & event_number < event_stop')]
|
python
|
def get_data_in_event_range(array, event_start=None, event_stop=None, assume_sorted=True):
'''Selects the data (rows of a table) that occurred in the given event range [event_start, event_stop[
Parameters
----------
array : numpy.array
event_start : int, None
event_stop : int, None
assume_sorted : bool
Set to true if the hits are sorted by the event_number. Increases speed.
Returns
-------
numpy.array
hit array with the hits in the event range.
'''
logging.debug("Calculate data of the the given event range [" + str(event_start) + ", " + str(event_stop) + "[")
event_number = array['event_number']
if assume_sorted:
data_event_start = event_number[0]
data_event_stop = event_number[-1]
if (event_start is not None and event_stop is not None) and (data_event_stop < event_start or data_event_start > event_stop or event_start == event_stop): # special case, no intersection at all
return array[0:0]
# get min/max indices with values that are also in the other array
if event_start is None:
min_index_data = 0
else:
min_index_data = np.argmin(event_number < event_start)
if event_stop is None:
max_index_data = event_number.shape[0]
else:
max_index_data = np.argmax(event_number >= event_stop)
if min_index_data < 0:
min_index_data = 0
if max_index_data == 0 or max_index_data > event_number.shape[0]:
max_index_data = event_number.shape[0]
return array[min_index_data:max_index_data]
else:
return array[ne.evaluate('event_number >= event_start & event_number < event_stop')]
|
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array : numpy.array
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assume_sorted : bool
Set to true if the hits are sorted by the event_number. Increases speed.
Returns
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hit array with the hits in the event range.
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L862-L903
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
write_hits_in_events
|
def write_hits_in_events(hit_table_in, hit_table_out, events, start_hit_word=0, chunk_size=5000000, condition=None):
'''Selects the hits that occurred in events and writes them to a pytable. This function reduces the in RAM operations and has to be
used if the get_hits_in_events function raises a memory error. Also a condition can be set to select hits.
Parameters
----------
hit_table_in : pytable.table
hit_table_out : pytable.table
functions need to be able to write to hit_table_out
events : array like
defines the events to be written from hit_table_in to hit_table_out. They do not have to exists at all.
start_hit_word: int
Index of the first hit word to be analyzed. Used for speed up.
chunk_size : int
defines how many hits are analyzed in RAM. Bigger numbers increase the speed, too big numbers let the program crash with a memory error.
condition : string
A condition that is applied to the hits in numexpr style. Only if the expression evaluates to True the hit is taken.
Returns
-------
start_hit_word: int
Index of the last hit word analyzed. Used to speed up the next call of write_hits_in_events.
'''
if len(events) > 0: # needed to avoid crash
min_event = np.amin(events)
max_event = np.amax(events)
logging.debug("Write hits from hit number >= %d that exists in the selected %d events with %d <= event number <= %d into a new hit table." % (start_hit_word, len(events), min_event, max_event))
table_size = hit_table_in.shape[0]
iHit = 0
for iHit in range(start_hit_word, table_size, chunk_size):
hits = hit_table_in.read(iHit, iHit + chunk_size)
last_event_number = hits[-1]['event_number']
hit_table_out.append(get_hits_in_events(hits, events=events, condition=condition))
if last_event_number > max_event: # speed up, use the fact that the hits are sorted by event_number
return iHit
return start_hit_word
|
python
|
def write_hits_in_events(hit_table_in, hit_table_out, events, start_hit_word=0, chunk_size=5000000, condition=None):
'''Selects the hits that occurred in events and writes them to a pytable. This function reduces the in RAM operations and has to be
used if the get_hits_in_events function raises a memory error. Also a condition can be set to select hits.
Parameters
----------
hit_table_in : pytable.table
hit_table_out : pytable.table
functions need to be able to write to hit_table_out
events : array like
defines the events to be written from hit_table_in to hit_table_out. They do not have to exists at all.
start_hit_word: int
Index of the first hit word to be analyzed. Used for speed up.
chunk_size : int
defines how many hits are analyzed in RAM. Bigger numbers increase the speed, too big numbers let the program crash with a memory error.
condition : string
A condition that is applied to the hits in numexpr style. Only if the expression evaluates to True the hit is taken.
Returns
-------
start_hit_word: int
Index of the last hit word analyzed. Used to speed up the next call of write_hits_in_events.
'''
if len(events) > 0: # needed to avoid crash
min_event = np.amin(events)
max_event = np.amax(events)
logging.debug("Write hits from hit number >= %d that exists in the selected %d events with %d <= event number <= %d into a new hit table." % (start_hit_word, len(events), min_event, max_event))
table_size = hit_table_in.shape[0]
iHit = 0
for iHit in range(start_hit_word, table_size, chunk_size):
hits = hit_table_in.read(iHit, iHit + chunk_size)
last_event_number = hits[-1]['event_number']
hit_table_out.append(get_hits_in_events(hits, events=events, condition=condition))
if last_event_number > max_event: # speed up, use the fact that the hits are sorted by event_number
return iHit
return start_hit_word
|
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Parameters
----------
hit_table_in : pytable.table
hit_table_out : pytable.table
functions need to be able to write to hit_table_out
events : array like
defines the events to be written from hit_table_in to hit_table_out. They do not have to exists at all.
start_hit_word: int
Index of the first hit word to be analyzed. Used for speed up.
chunk_size : int
defines how many hits are analyzed in RAM. Bigger numbers increase the speed, too big numbers let the program crash with a memory error.
condition : string
A condition that is applied to the hits in numexpr style. Only if the expression evaluates to True the hit is taken.
Returns
-------
start_hit_word: int
Index of the last hit word analyzed. Used to speed up the next call of write_hits_in_events.
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L906-L941
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
write_hits_in_event_range
|
def write_hits_in_event_range(hit_table_in, hit_table_out, event_start=None, event_stop=None, start_hit_word=0, chunk_size=5000000, condition=None):
'''Selects the hits that occurred in given event range [event_start, event_stop[ and write them to a pytable. This function reduces the in RAM
operations and has to be used if the get_data_in_event_range function raises a memory error. Also a condition can be set to select hits.
Parameters
----------
hit_table_in : pytable.table
hit_table_out : pytable.table
functions need to be able to write to hit_table_out
event_start, event_stop : int, None
start/stop event numbers. Stop event number is excluded. If None start/stop is set automatically.
chunk_size : int
defines how many hits are analyzed in RAM. Bigger numbers increase the speed, too big numbers let the program crash with a memory error.
condition : string
A condition that is applied to the hits in numexpr style. Only if the expression evaluates to True the hit is taken.
Returns
-------
start_hit_word: int
Index of the last hit word analyzed. Used to speed up the next call of write_hits_in_events.
'''
logging.debug('Write hits that exists in the given event range from + ' + str(event_start) + ' to ' + str(event_stop) + ' into a new hit table')
table_size = hit_table_in.shape[0]
for iHit in range(0, table_size, chunk_size):
hits = hit_table_in.read(iHit, iHit + chunk_size)
last_event_number = hits[-1]['event_number']
selected_hits = get_data_in_event_range(hits, event_start=event_start, event_stop=event_stop)
if condition is not None:
# bad hack to be able to use numexpr
for variable in set(re.findall(r'[a-zA-Z_]+', condition)):
exec(variable + ' = hits[\'' + variable + '\']')
selected_hits = selected_hits[ne.evaluate(condition)]
hit_table_out.append(selected_hits)
if last_event_number > event_stop: # speed up, use the fact that the hits are sorted by event_number
return iHit + chunk_size
return start_hit_word
|
python
|
def write_hits_in_event_range(hit_table_in, hit_table_out, event_start=None, event_stop=None, start_hit_word=0, chunk_size=5000000, condition=None):
'''Selects the hits that occurred in given event range [event_start, event_stop[ and write them to a pytable. This function reduces the in RAM
operations and has to be used if the get_data_in_event_range function raises a memory error. Also a condition can be set to select hits.
Parameters
----------
hit_table_in : pytable.table
hit_table_out : pytable.table
functions need to be able to write to hit_table_out
event_start, event_stop : int, None
start/stop event numbers. Stop event number is excluded. If None start/stop is set automatically.
chunk_size : int
defines how many hits are analyzed in RAM. Bigger numbers increase the speed, too big numbers let the program crash with a memory error.
condition : string
A condition that is applied to the hits in numexpr style. Only if the expression evaluates to True the hit is taken.
Returns
-------
start_hit_word: int
Index of the last hit word analyzed. Used to speed up the next call of write_hits_in_events.
'''
logging.debug('Write hits that exists in the given event range from + ' + str(event_start) + ' to ' + str(event_stop) + ' into a new hit table')
table_size = hit_table_in.shape[0]
for iHit in range(0, table_size, chunk_size):
hits = hit_table_in.read(iHit, iHit + chunk_size)
last_event_number = hits[-1]['event_number']
selected_hits = get_data_in_event_range(hits, event_start=event_start, event_stop=event_stop)
if condition is not None:
# bad hack to be able to use numexpr
for variable in set(re.findall(r'[a-zA-Z_]+', condition)):
exec(variable + ' = hits[\'' + variable + '\']')
selected_hits = selected_hits[ne.evaluate(condition)]
hit_table_out.append(selected_hits)
if last_event_number > event_stop: # speed up, use the fact that the hits are sorted by event_number
return iHit + chunk_size
return start_hit_word
|
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functions need to be able to write to hit_table_out
event_start, event_stop : int, None
start/stop event numbers. Stop event number is excluded. If None start/stop is set automatically.
chunk_size : int
defines how many hits are analyzed in RAM. Bigger numbers increase the speed, too big numbers let the program crash with a memory error.
condition : string
A condition that is applied to the hits in numexpr style. Only if the expression evaluates to True the hit is taken.
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-------
start_hit_word: int
Index of the last hit word analyzed. Used to speed up the next call of write_hits_in_events.
|
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train
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L944-L979
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_events_with_n_cluster
|
def get_events_with_n_cluster(event_number, condition='n_cluster==1'):
'''Selects the events with a certain number of cluster.
Parameters
----------
event_number : numpy.array
Returns
-------
numpy.array
'''
logging.debug("Calculate events with clusters where " + condition)
n_cluster_in_events = analysis_utils.get_n_cluster_in_events(event_number)
n_cluster = n_cluster_in_events[:, 1]
# return np.take(n_cluster_in_events, ne.evaluate(condition), axis=0) # does not return 1d, bug?
return n_cluster_in_events[ne.evaluate(condition), 0]
|
python
|
def get_events_with_n_cluster(event_number, condition='n_cluster==1'):
'''Selects the events with a certain number of cluster.
Parameters
----------
event_number : numpy.array
Returns
-------
numpy.array
'''
logging.debug("Calculate events with clusters where " + condition)
n_cluster_in_events = analysis_utils.get_n_cluster_in_events(event_number)
n_cluster = n_cluster_in_events[:, 1]
# return np.take(n_cluster_in_events, ne.evaluate(condition), axis=0) # does not return 1d, bug?
return n_cluster_in_events[ne.evaluate(condition), 0]
|
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L982-L998
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_events_with_cluster_size
|
def get_events_with_cluster_size(event_number, cluster_size, condition='cluster_size==1'):
'''Selects the events with cluster of a given cluster size.
Parameters
----------
event_number : numpy.array
cluster_size : numpy.array
condition : string
Returns
-------
numpy.array
'''
logging.debug("Calculate events with clusters with " + condition)
return np.unique(event_number[ne.evaluate(condition)])
|
python
|
def get_events_with_cluster_size(event_number, cluster_size, condition='cluster_size==1'):
'''Selects the events with cluster of a given cluster size.
Parameters
----------
event_number : numpy.array
cluster_size : numpy.array
condition : string
Returns
-------
numpy.array
'''
logging.debug("Calculate events with clusters with " + condition)
return np.unique(event_number[ne.evaluate(condition)])
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train
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1001-L1016
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_events_with_error_code
|
def get_events_with_error_code(event_number, event_status, select_mask=0b1111111111111111, condition=0b0000000000000000):
'''Selects the events with a certain error code.
Parameters
----------
event_number : numpy.array
event_status : numpy.array
select_mask : int
The mask that selects the event error code to check.
condition : int
The value the selected event error code should have.
Returns
-------
numpy.array
'''
logging.debug("Calculate events with certain error code")
return np.unique(event_number[event_status & select_mask == condition])
|
python
|
def get_events_with_error_code(event_number, event_status, select_mask=0b1111111111111111, condition=0b0000000000000000):
'''Selects the events with a certain error code.
Parameters
----------
event_number : numpy.array
event_status : numpy.array
select_mask : int
The mask that selects the event error code to check.
condition : int
The value the selected event error code should have.
Returns
-------
numpy.array
'''
logging.debug("Calculate events with certain error code")
return np.unique(event_number[event_status & select_mask == condition])
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The value the selected event error code should have.
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1019-L1037
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_scan_parameter
|
def get_scan_parameter(meta_data_array, unique=True):
'''Takes the numpy meta data array and returns the different scan parameter settings and the name aligned in a dictionary
Parameters
----------
meta_data_array : numpy.ndarray
unique: boolean
If true only unique values for each scan parameter are returned
Returns
-------
python.dict{string, numpy.Histogram}:
A dictionary with the scan parameter name/values pairs
'''
try:
last_not_parameter_column = meta_data_array.dtype.names.index('error_code') # for interpreted meta_data
except ValueError:
last_not_parameter_column = meta_data_array.dtype.names.index('error') # for raw data file meta_data
if last_not_parameter_column == len(meta_data_array.dtype.names) - 1: # no meta_data found
return
scan_parameters = collections.OrderedDict()
for scan_par_name in meta_data_array.dtype.names[4:]: # scan parameters are in columns 5 (= index 4) and above
scan_parameters[scan_par_name] = np.unique(meta_data_array[scan_par_name]) if unique else meta_data_array[scan_par_name]
return scan_parameters
|
python
|
def get_scan_parameter(meta_data_array, unique=True):
'''Takes the numpy meta data array and returns the different scan parameter settings and the name aligned in a dictionary
Parameters
----------
meta_data_array : numpy.ndarray
unique: boolean
If true only unique values for each scan parameter are returned
Returns
-------
python.dict{string, numpy.Histogram}:
A dictionary with the scan parameter name/values pairs
'''
try:
last_not_parameter_column = meta_data_array.dtype.names.index('error_code') # for interpreted meta_data
except ValueError:
last_not_parameter_column = meta_data_array.dtype.names.index('error') # for raw data file meta_data
if last_not_parameter_column == len(meta_data_array.dtype.names) - 1: # no meta_data found
return
scan_parameters = collections.OrderedDict()
for scan_par_name in meta_data_array.dtype.names[4:]: # scan parameters are in columns 5 (= index 4) and above
scan_parameters[scan_par_name] = np.unique(meta_data_array[scan_par_name]) if unique else meta_data_array[scan_par_name]
return scan_parameters
|
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python.dict{string, numpy.Histogram}:
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train
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1040-L1064
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_scan_parameters_table_from_meta_data
|
def get_scan_parameters_table_from_meta_data(meta_data_array, scan_parameters=None):
'''Takes the meta data array and returns the scan parameter values as a view of a numpy array only containing the parameter data .
Parameters
----------
meta_data_array : numpy.ndarray
The array with the scan parameters.
scan_parameters : list of strings
The name of the scan parameters to take. If none all are used.
Returns
-------
numpy.Histogram
'''
if scan_parameters is None:
try:
last_not_parameter_column = meta_data_array.dtype.names.index('error_code') # for interpreted meta_data
except ValueError:
return
if last_not_parameter_column == len(meta_data_array.dtype.names) - 1: # no meta_data found
return
# http://stackoverflow.com/questions/15182381/how-to-return-a-view-of-several-columns-in-numpy-structured-array
scan_par_data = {name: meta_data_array.dtype.fields[name] for name in meta_data_array.dtype.names[last_not_parameter_column + 1:]}
else:
scan_par_data = collections.OrderedDict()
for name in scan_parameters:
scan_par_data[name] = meta_data_array.dtype.fields[name]
return np.ndarray(meta_data_array.shape, np.dtype(scan_par_data), meta_data_array, 0, meta_data_array.strides)
|
python
|
def get_scan_parameters_table_from_meta_data(meta_data_array, scan_parameters=None):
'''Takes the meta data array and returns the scan parameter values as a view of a numpy array only containing the parameter data .
Parameters
----------
meta_data_array : numpy.ndarray
The array with the scan parameters.
scan_parameters : list of strings
The name of the scan parameters to take. If none all are used.
Returns
-------
numpy.Histogram
'''
if scan_parameters is None:
try:
last_not_parameter_column = meta_data_array.dtype.names.index('error_code') # for interpreted meta_data
except ValueError:
return
if last_not_parameter_column == len(meta_data_array.dtype.names) - 1: # no meta_data found
return
# http://stackoverflow.com/questions/15182381/how-to-return-a-view-of-several-columns-in-numpy-structured-array
scan_par_data = {name: meta_data_array.dtype.fields[name] for name in meta_data_array.dtype.names[last_not_parameter_column + 1:]}
else:
scan_par_data = collections.OrderedDict()
for name in scan_parameters:
scan_par_data[name] = meta_data_array.dtype.fields[name]
return np.ndarray(meta_data_array.shape, np.dtype(scan_par_data), meta_data_array, 0, meta_data_array.strides)
|
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1067-L1095
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_scan_parameters_index
|
def get_scan_parameters_index(scan_parameter):
'''Takes the scan parameter array and creates a scan parameter index labeling the unique scan parameter combinations.
Parameters
----------
scan_parameter : numpy.ndarray
The table with the scan parameters.
Returns
-------
numpy.Histogram
'''
_, index = np.unique(scan_parameter, return_index=True)
index = np.sort(index)
values = np.array(range(0, len(index)), dtype='i4')
index = np.append(index, len(scan_parameter))
counts = np.diff(index)
return np.repeat(values, counts)
|
python
|
def get_scan_parameters_index(scan_parameter):
'''Takes the scan parameter array and creates a scan parameter index labeling the unique scan parameter combinations.
Parameters
----------
scan_parameter : numpy.ndarray
The table with the scan parameters.
Returns
-------
numpy.Histogram
'''
_, index = np.unique(scan_parameter, return_index=True)
index = np.sort(index)
values = np.array(range(0, len(index)), dtype='i4')
index = np.append(index, len(scan_parameter))
counts = np.diff(index)
return np.repeat(values, counts)
|
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https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1098-L1114
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
get_unique_scan_parameter_combinations
|
def get_unique_scan_parameter_combinations(meta_data_array, scan_parameters=None, scan_parameter_columns_only=False):
'''Takes the numpy meta data array and returns the first rows with unique combinations of different scan parameter values for selected scan parameters.
If selected columns only is true, the returned histogram only contains the selected columns.
Parameters
----------
meta_data_array : numpy.ndarray
scan_parameters : list of string, None
Scan parameter names taken. If None all are used.
selected_columns_only : bool
Returns
-------
numpy.Histogram
'''
try:
last_not_parameter_column = meta_data_array.dtype.names.index('error_code') # for interpreted meta_data
except ValueError:
last_not_parameter_column = meta_data_array.dtype.names.index('error') # for raw data file meta_data
if last_not_parameter_column == len(meta_data_array.dtype.names) - 1: # no meta_data found
return
if scan_parameters is None:
return unique_row(meta_data_array, use_columns=range(4, len(meta_data_array.dtype.names)), selected_columns_only=scan_parameter_columns_only)
else:
use_columns = []
for scan_parameter in scan_parameters:
try:
use_columns.append(meta_data_array.dtype.names.index(scan_parameter))
except ValueError:
logging.error('No scan parameter ' + scan_parameter + ' found')
raise RuntimeError('Scan parameter not found')
return unique_row(meta_data_array, use_columns=use_columns, selected_columns_only=scan_parameter_columns_only)
|
python
|
def get_unique_scan_parameter_combinations(meta_data_array, scan_parameters=None, scan_parameter_columns_only=False):
'''Takes the numpy meta data array and returns the first rows with unique combinations of different scan parameter values for selected scan parameters.
If selected columns only is true, the returned histogram only contains the selected columns.
Parameters
----------
meta_data_array : numpy.ndarray
scan_parameters : list of string, None
Scan parameter names taken. If None all are used.
selected_columns_only : bool
Returns
-------
numpy.Histogram
'''
try:
last_not_parameter_column = meta_data_array.dtype.names.index('error_code') # for interpreted meta_data
except ValueError:
last_not_parameter_column = meta_data_array.dtype.names.index('error') # for raw data file meta_data
if last_not_parameter_column == len(meta_data_array.dtype.names) - 1: # no meta_data found
return
if scan_parameters is None:
return unique_row(meta_data_array, use_columns=range(4, len(meta_data_array.dtype.names)), selected_columns_only=scan_parameter_columns_only)
else:
use_columns = []
for scan_parameter in scan_parameters:
try:
use_columns.append(meta_data_array.dtype.names.index(scan_parameter))
except ValueError:
logging.error('No scan parameter ' + scan_parameter + ' found')
raise RuntimeError('Scan parameter not found')
return unique_row(meta_data_array, use_columns=use_columns, selected_columns_only=scan_parameter_columns_only)
|
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Scan parameter names taken. If None all are used.
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train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1117-L1149
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
data_aligned_at_events
|
def data_aligned_at_events(table, start_event_number=None, stop_event_number=None, start_index=None, stop_index=None, chunk_size=10000000, try_speedup=False, first_event_aligned=True, fail_on_missing_events=True):
'''Takes the table with a event_number column and returns chunks with the size up to chunk_size. The chunks are chosen in a way that the events are not splitted.
Additional parameters can be set to increase the readout speed. Events between a certain range can be selected.
Also the start and the stop indices limiting the table size can be specified to improve performance.
The event_number column must be sorted.
In case of try_speedup is True, it is important to create an index of event_number column with pytables before using this function. Otherwise the queries are slowed down.
Parameters
----------
table : pytables.table
The data.
start_event_number : int
The retruned data contains events with event number >= start_event_number. If None, no limit is set.
stop_event_number : int
The retruned data contains events with event number < stop_event_number. If None, no limit is set.
start_index : int
Start index of data. If None, no limit is set.
stop_index : int
Stop index of data. If None, no limit is set.
chunk_size : int
Maximum chunk size per read.
try_speedup : bool
If True, try to reduce the index range to read by searching for the indices of start and stop event number. If these event numbers are usually
not in the data this speedup can even slow down the function!
The following parameters are not used when try_speedup is True:
first_event_aligned : bool
If True, assuming that the first event is aligned to the data chunk and will be added. If False, the lowest event number of the first chunk will not be read out.
fail_on_missing_events : bool
If True, an error is given when start_event_number or stop_event_number is not part of the data.
Returns
-------
Iterator of tuples
Data of the actual data chunk and start index for the next chunk.
Example
-------
start_index = 0
for scan_parameter in scan_parameter_range:
start_event_number, stop_event_number = event_select_function(scan_parameter)
for data, start_index in data_aligned_at_events(table, start_event_number=start_event_number, stop_event_number=stop_event_number, start_index=start_index):
do_something(data)
for data, index in data_aligned_at_events(table):
do_something(data)
'''
# initialize variables
start_index_known = False
stop_index_known = False
start_index = 0 if start_index is None else start_index
stop_index = table.nrows if stop_index is None else stop_index
if stop_index < start_index:
raise InvalidInputError('Invalid start/stop index')
table_max_rows = table.nrows
if stop_event_number is not None and start_event_number is not None and stop_event_number < start_event_number:
raise InvalidInputError('Invalid start/stop event number')
# set start stop indices from the event numbers for fast read if possible; not possible if the given event number does not exist in the data stream
if try_speedup and table.colindexed["event_number"]:
if start_event_number is not None:
start_condition = 'event_number==' + str(start_event_number)
start_indices = table.get_where_list(start_condition, start=start_index, stop=stop_index)
if start_indices.shape[0] != 0: # set start index if possible
start_index = start_indices[0]
start_index_known = True
if stop_event_number is not None:
stop_condition = 'event_number==' + str(stop_event_number)
stop_indices = table.get_where_list(stop_condition, start=start_index, stop=stop_index)
if stop_indices.shape[0] != 0: # set the stop index if possible, stop index is excluded
stop_index = stop_indices[0]
stop_index_known = True
if start_index_known and stop_index_known and start_index + chunk_size >= stop_index: # special case, one read is enough, data not bigger than one chunk and the indices are known
yield table.read(start=start_index, stop=stop_index), stop_index
else: # read data in chunks, chunks do not divide events, abort if stop_event_number is reached
# search for begin
current_start_index = start_index
if start_event_number is not None:
while current_start_index < stop_index:
current_stop_index = min(current_start_index + chunk_size, stop_index)
array_chunk = table.read(start=current_start_index, stop=current_stop_index) # stop index is exclusive, so add 1
last_event_in_chunk = array_chunk["event_number"][-1]
if last_event_in_chunk < start_event_number:
current_start_index = current_start_index + chunk_size # not there yet, continue to next read (assuming sorted events)
else:
first_event_in_chunk = array_chunk["event_number"][0]
# if stop_event_number is not None and first_event_in_chunk >= stop_event_number and start_index != 0 and start_index == current_start_index:
# raise InvalidInputError('The stop event %d is missing. Change stop_event_number.' % stop_event_number)
if array_chunk.shape[0] == chunk_size and first_event_in_chunk == last_event_in_chunk:
raise InvalidInputError('Chunk size too small. Increase chunk size to fit full event.')
if not first_event_aligned and first_event_in_chunk == start_event_number and start_index != 0 and start_index == current_start_index: # first event in first chunk not aligned at index 0, so take next event
if fail_on_missing_events:
raise InvalidInputError('The start event %d is missing. Change start_event_number.' % start_event_number)
chunk_start_index = np.searchsorted(array_chunk["event_number"], start_event_number + 1, side='left')
elif fail_on_missing_events and first_event_in_chunk > start_event_number and start_index == current_start_index:
raise InvalidInputError('The start event %d is missing. Change start_event_number.' % start_event_number)
elif first_event_aligned and first_event_in_chunk == start_event_number and start_index == current_start_index:
chunk_start_index = 0
else:
chunk_start_index = np.searchsorted(array_chunk["event_number"], start_event_number, side='left')
if fail_on_missing_events and array_chunk["event_number"][chunk_start_index] != start_event_number and start_index == current_start_index:
raise InvalidInputError('The start event %d is missing. Change start_event_number.' % start_event_number)
# if fail_on_missing_events and ((start_index == current_start_index and chunk_start_index == 0 and start_index != 0 and not first_event_aligned) or array_chunk["event_number"][chunk_start_index] != start_event_number):
# raise InvalidInputError('The start event %d is missing. Change start_event_number.' % start_event_number)
current_start_index = current_start_index + chunk_start_index # calculate index for next loop
break
elif not first_event_aligned and start_index != 0:
while current_start_index < stop_index:
current_stop_index = min(current_start_index + chunk_size, stop_index)
array_chunk = table.read(start=current_start_index, stop=current_stop_index) # stop index is exclusive, so add 1
first_event_in_chunk = array_chunk["event_number"][0]
last_event_in_chunk = array_chunk["event_number"][-1]
if array_chunk.shape[0] == chunk_size and first_event_in_chunk == last_event_in_chunk:
raise InvalidInputError('Chunk size too small. Increase chunk size to fit full event.')
chunk_start_index = np.searchsorted(array_chunk["event_number"], first_event_in_chunk + 1, side='left')
current_start_index = current_start_index + chunk_start_index
if not first_event_in_chunk == last_event_in_chunk:
break
# data loop
while current_start_index < stop_index:
current_stop_index = min(current_start_index + chunk_size, stop_index)
array_chunk = table.read(start=current_start_index, stop=current_stop_index) # stop index is exclusive, so add 1
first_event_in_chunk = array_chunk["event_number"][0]
last_event_in_chunk = array_chunk["event_number"][-1]
chunk_start_index = 0
if stop_event_number is None:
if current_stop_index == table_max_rows:
chunk_stop_index = array_chunk.shape[0]
else:
chunk_stop_index = np.searchsorted(array_chunk["event_number"], last_event_in_chunk, side='left')
else:
if last_event_in_chunk >= stop_event_number:
chunk_stop_index = np.searchsorted(array_chunk["event_number"], stop_event_number, side='left')
elif current_stop_index == table_max_rows: # this will also add the last event of the table
chunk_stop_index = array_chunk.shape[0]
else:
chunk_stop_index = np.searchsorted(array_chunk["event_number"], last_event_in_chunk, side='left')
nrows = chunk_stop_index - chunk_start_index
if nrows == 0:
if array_chunk.shape[0] == chunk_size and first_event_in_chunk == last_event_in_chunk:
raise InvalidInputError('Chunk size too small to fit event. Data corruption possible. Increase chunk size to read full event.')
elif chunk_start_index == 0: # not increasing current_start_index
return
elif stop_event_number is not None and last_event_in_chunk >= stop_event_number:
return
else:
yield array_chunk[chunk_start_index:chunk_stop_index], current_start_index + nrows + chunk_start_index
current_start_index = current_start_index + nrows + chunk_start_index
|
python
|
def data_aligned_at_events(table, start_event_number=None, stop_event_number=None, start_index=None, stop_index=None, chunk_size=10000000, try_speedup=False, first_event_aligned=True, fail_on_missing_events=True):
'''Takes the table with a event_number column and returns chunks with the size up to chunk_size. The chunks are chosen in a way that the events are not splitted.
Additional parameters can be set to increase the readout speed. Events between a certain range can be selected.
Also the start and the stop indices limiting the table size can be specified to improve performance.
The event_number column must be sorted.
In case of try_speedup is True, it is important to create an index of event_number column with pytables before using this function. Otherwise the queries are slowed down.
Parameters
----------
table : pytables.table
The data.
start_event_number : int
The retruned data contains events with event number >= start_event_number. If None, no limit is set.
stop_event_number : int
The retruned data contains events with event number < stop_event_number. If None, no limit is set.
start_index : int
Start index of data. If None, no limit is set.
stop_index : int
Stop index of data. If None, no limit is set.
chunk_size : int
Maximum chunk size per read.
try_speedup : bool
If True, try to reduce the index range to read by searching for the indices of start and stop event number. If these event numbers are usually
not in the data this speedup can even slow down the function!
The following parameters are not used when try_speedup is True:
first_event_aligned : bool
If True, assuming that the first event is aligned to the data chunk and will be added. If False, the lowest event number of the first chunk will not be read out.
fail_on_missing_events : bool
If True, an error is given when start_event_number or stop_event_number is not part of the data.
Returns
-------
Iterator of tuples
Data of the actual data chunk and start index for the next chunk.
Example
-------
start_index = 0
for scan_parameter in scan_parameter_range:
start_event_number, stop_event_number = event_select_function(scan_parameter)
for data, start_index in data_aligned_at_events(table, start_event_number=start_event_number, stop_event_number=stop_event_number, start_index=start_index):
do_something(data)
for data, index in data_aligned_at_events(table):
do_something(data)
'''
# initialize variables
start_index_known = False
stop_index_known = False
start_index = 0 if start_index is None else start_index
stop_index = table.nrows if stop_index is None else stop_index
if stop_index < start_index:
raise InvalidInputError('Invalid start/stop index')
table_max_rows = table.nrows
if stop_event_number is not None and start_event_number is not None and stop_event_number < start_event_number:
raise InvalidInputError('Invalid start/stop event number')
# set start stop indices from the event numbers for fast read if possible; not possible if the given event number does not exist in the data stream
if try_speedup and table.colindexed["event_number"]:
if start_event_number is not None:
start_condition = 'event_number==' + str(start_event_number)
start_indices = table.get_where_list(start_condition, start=start_index, stop=stop_index)
if start_indices.shape[0] != 0: # set start index if possible
start_index = start_indices[0]
start_index_known = True
if stop_event_number is not None:
stop_condition = 'event_number==' + str(stop_event_number)
stop_indices = table.get_where_list(stop_condition, start=start_index, stop=stop_index)
if stop_indices.shape[0] != 0: # set the stop index if possible, stop index is excluded
stop_index = stop_indices[0]
stop_index_known = True
if start_index_known and stop_index_known and start_index + chunk_size >= stop_index: # special case, one read is enough, data not bigger than one chunk and the indices are known
yield table.read(start=start_index, stop=stop_index), stop_index
else: # read data in chunks, chunks do not divide events, abort if stop_event_number is reached
# search for begin
current_start_index = start_index
if start_event_number is not None:
while current_start_index < stop_index:
current_stop_index = min(current_start_index + chunk_size, stop_index)
array_chunk = table.read(start=current_start_index, stop=current_stop_index) # stop index is exclusive, so add 1
last_event_in_chunk = array_chunk["event_number"][-1]
if last_event_in_chunk < start_event_number:
current_start_index = current_start_index + chunk_size # not there yet, continue to next read (assuming sorted events)
else:
first_event_in_chunk = array_chunk["event_number"][0]
# if stop_event_number is not None and first_event_in_chunk >= stop_event_number and start_index != 0 and start_index == current_start_index:
# raise InvalidInputError('The stop event %d is missing. Change stop_event_number.' % stop_event_number)
if array_chunk.shape[0] == chunk_size and first_event_in_chunk == last_event_in_chunk:
raise InvalidInputError('Chunk size too small. Increase chunk size to fit full event.')
if not first_event_aligned and first_event_in_chunk == start_event_number and start_index != 0 and start_index == current_start_index: # first event in first chunk not aligned at index 0, so take next event
if fail_on_missing_events:
raise InvalidInputError('The start event %d is missing. Change start_event_number.' % start_event_number)
chunk_start_index = np.searchsorted(array_chunk["event_number"], start_event_number + 1, side='left')
elif fail_on_missing_events and first_event_in_chunk > start_event_number and start_index == current_start_index:
raise InvalidInputError('The start event %d is missing. Change start_event_number.' % start_event_number)
elif first_event_aligned and first_event_in_chunk == start_event_number and start_index == current_start_index:
chunk_start_index = 0
else:
chunk_start_index = np.searchsorted(array_chunk["event_number"], start_event_number, side='left')
if fail_on_missing_events and array_chunk["event_number"][chunk_start_index] != start_event_number and start_index == current_start_index:
raise InvalidInputError('The start event %d is missing. Change start_event_number.' % start_event_number)
# if fail_on_missing_events and ((start_index == current_start_index and chunk_start_index == 0 and start_index != 0 and not first_event_aligned) or array_chunk["event_number"][chunk_start_index] != start_event_number):
# raise InvalidInputError('The start event %d is missing. Change start_event_number.' % start_event_number)
current_start_index = current_start_index + chunk_start_index # calculate index for next loop
break
elif not first_event_aligned and start_index != 0:
while current_start_index < stop_index:
current_stop_index = min(current_start_index + chunk_size, stop_index)
array_chunk = table.read(start=current_start_index, stop=current_stop_index) # stop index is exclusive, so add 1
first_event_in_chunk = array_chunk["event_number"][0]
last_event_in_chunk = array_chunk["event_number"][-1]
if array_chunk.shape[0] == chunk_size and first_event_in_chunk == last_event_in_chunk:
raise InvalidInputError('Chunk size too small. Increase chunk size to fit full event.')
chunk_start_index = np.searchsorted(array_chunk["event_number"], first_event_in_chunk + 1, side='left')
current_start_index = current_start_index + chunk_start_index
if not first_event_in_chunk == last_event_in_chunk:
break
# data loop
while current_start_index < stop_index:
current_stop_index = min(current_start_index + chunk_size, stop_index)
array_chunk = table.read(start=current_start_index, stop=current_stop_index) # stop index is exclusive, so add 1
first_event_in_chunk = array_chunk["event_number"][0]
last_event_in_chunk = array_chunk["event_number"][-1]
chunk_start_index = 0
if stop_event_number is None:
if current_stop_index == table_max_rows:
chunk_stop_index = array_chunk.shape[0]
else:
chunk_stop_index = np.searchsorted(array_chunk["event_number"], last_event_in_chunk, side='left')
else:
if last_event_in_chunk >= stop_event_number:
chunk_stop_index = np.searchsorted(array_chunk["event_number"], stop_event_number, side='left')
elif current_stop_index == table_max_rows: # this will also add the last event of the table
chunk_stop_index = array_chunk.shape[0]
else:
chunk_stop_index = np.searchsorted(array_chunk["event_number"], last_event_in_chunk, side='left')
nrows = chunk_stop_index - chunk_start_index
if nrows == 0:
if array_chunk.shape[0] == chunk_size and first_event_in_chunk == last_event_in_chunk:
raise InvalidInputError('Chunk size too small to fit event. Data corruption possible. Increase chunk size to read full event.')
elif chunk_start_index == 0: # not increasing current_start_index
return
elif stop_event_number is not None and last_event_in_chunk >= stop_event_number:
return
else:
yield array_chunk[chunk_start_index:chunk_stop_index], current_start_index + nrows + chunk_start_index
current_start_index = current_start_index + nrows + chunk_start_index
|
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] |
Takes the table with a event_number column and returns chunks with the size up to chunk_size. The chunks are chosen in a way that the events are not splitted.
Additional parameters can be set to increase the readout speed. Events between a certain range can be selected.
Also the start and the stop indices limiting the table size can be specified to improve performance.
The event_number column must be sorted.
In case of try_speedup is True, it is important to create an index of event_number column with pytables before using this function. Otherwise the queries are slowed down.
Parameters
----------
table : pytables.table
The data.
start_event_number : int
The retruned data contains events with event number >= start_event_number. If None, no limit is set.
stop_event_number : int
The retruned data contains events with event number < stop_event_number. If None, no limit is set.
start_index : int
Start index of data. If None, no limit is set.
stop_index : int
Stop index of data. If None, no limit is set.
chunk_size : int
Maximum chunk size per read.
try_speedup : bool
If True, try to reduce the index range to read by searching for the indices of start and stop event number. If these event numbers are usually
not in the data this speedup can even slow down the function!
The following parameters are not used when try_speedup is True:
first_event_aligned : bool
If True, assuming that the first event is aligned to the data chunk and will be added. If False, the lowest event number of the first chunk will not be read out.
fail_on_missing_events : bool
If True, an error is given when start_event_number or stop_event_number is not part of the data.
Returns
-------
Iterator of tuples
Data of the actual data chunk and start index for the next chunk.
Example
-------
start_index = 0
for scan_parameter in scan_parameter_range:
start_event_number, stop_event_number = event_select_function(scan_parameter)
for data, start_index in data_aligned_at_events(table, start_event_number=start_event_number, stop_event_number=stop_event_number, start_index=start_index):
do_something(data)
for data, index in data_aligned_at_events(table):
do_something(data)
|
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] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1160-L1320
|
SiLab-Bonn/pyBAR
|
pybar/analysis/analysis_utils.py
|
select_good_pixel_region
|
def select_good_pixel_region(hits, col_span, row_span, min_cut_threshold=0.2, max_cut_threshold=2.0):
'''Takes the hit array and masks all pixels with a certain occupancy.
Parameters
----------
hits : array like
If dim > 2 the additional dimensions are summed up.
min_cut_threshold : float
A number to specify the minimum threshold, which pixel to take. Pixels are masked if
occupancy < min_cut_threshold * np.ma.median(occupancy)
0 means that no pixels are masked
max_cut_threshold : float
A number to specify the maximum threshold, which pixel to take. Pixels are masked if
occupancy > max_cut_threshold * np.ma.median(occupancy)
Can be set to None that no pixels are masked by max_cut_threshold
Returns
-------
numpy.ma.array, shape=(80,336)
The hits array with masked pixels.
'''
hits = np.sum(hits, axis=(-1)).astype('u8')
mask = np.ones(shape=(80, 336), dtype=np.uint8)
mask[min(col_span):max(col_span) + 1, min(row_span):max(row_span) + 1] = 0
ma = np.ma.masked_where(mask, hits)
if max_cut_threshold is not None:
return np.ma.masked_where(np.logical_or(ma < min_cut_threshold * np.ma.median(ma), ma > max_cut_threshold * np.ma.median(ma)), ma)
else:
return np.ma.masked_where(ma < min_cut_threshold * np.ma.median(ma), ma)
|
python
|
def select_good_pixel_region(hits, col_span, row_span, min_cut_threshold=0.2, max_cut_threshold=2.0):
'''Takes the hit array and masks all pixels with a certain occupancy.
Parameters
----------
hits : array like
If dim > 2 the additional dimensions are summed up.
min_cut_threshold : float
A number to specify the minimum threshold, which pixel to take. Pixels are masked if
occupancy < min_cut_threshold * np.ma.median(occupancy)
0 means that no pixels are masked
max_cut_threshold : float
A number to specify the maximum threshold, which pixel to take. Pixels are masked if
occupancy > max_cut_threshold * np.ma.median(occupancy)
Can be set to None that no pixels are masked by max_cut_threshold
Returns
-------
numpy.ma.array, shape=(80,336)
The hits array with masked pixels.
'''
hits = np.sum(hits, axis=(-1)).astype('u8')
mask = np.ones(shape=(80, 336), dtype=np.uint8)
mask[min(col_span):max(col_span) + 1, min(row_span):max(row_span) + 1] = 0
ma = np.ma.masked_where(mask, hits)
if max_cut_threshold is not None:
return np.ma.masked_where(np.logical_or(ma < min_cut_threshold * np.ma.median(ma), ma > max_cut_threshold * np.ma.median(ma)), ma)
else:
return np.ma.masked_where(ma < min_cut_threshold * np.ma.median(ma), ma)
|
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] |
Takes the hit array and masks all pixels with a certain occupancy.
Parameters
----------
hits : array like
If dim > 2 the additional dimensions are summed up.
min_cut_threshold : float
A number to specify the minimum threshold, which pixel to take. Pixels are masked if
occupancy < min_cut_threshold * np.ma.median(occupancy)
0 means that no pixels are masked
max_cut_threshold : float
A number to specify the maximum threshold, which pixel to take. Pixels are masked if
occupancy > max_cut_threshold * np.ma.median(occupancy)
Can be set to None that no pixels are masked by max_cut_threshold
Returns
-------
numpy.ma.array, shape=(80,336)
The hits array with masked pixels.
|
[
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"pixels",
"with",
"a",
"certain",
"occupancy",
"."
] |
train
|
https://github.com/SiLab-Bonn/pyBAR/blob/5ad95bbcd41cd358825823fb78f396cfce23593e/pybar/analysis/analysis_utils.py#L1323-L1353
|
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