##############################################################################
##
# This file is part of Sardana
##
# http://www.sardana-controls.org/
##
# Copyright 2011 CELLS / ALBA Synchrotron, Bellaterra, Spain
##
# Sardana is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
##
# Sardana is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Lesser General Public License for more details.
##
# You should have received a copy of the GNU Lesser General Public License
# along with Sardana. If not, see <http://www.gnu.org/licenses/>.
##
##############################################################################
"""
Macro library containning scan macros for the macros server Tango device
server as part of the Sardana project.
"""
__all__ = ["a2scan", "a3scan", "a4scan", "amultiscan", "aNscan", "ascan",
"d2scan", "d3scan", "d4scan", "dmultiscan", "dNscan", "dscan",
"fscan", "mesh", "timescan", "rscan", "r2scan", "r3scan",
"a2scanc", "a3scanc", "a4scanc", "ascanc",
"d2scanc", "d3scanc", "d4scanc", "dscanc",
"meshc",
"a2scanct", "a3scanct", "a4scanct", "ascanct", "meshct",
"rscanct",
"scanhist", "getCallable", "UNCONSTRAINED",
"scanstats"]
__docformat__ = 'restructuredtext'
import os
import copy
import datetime
import numpy
from taurus.core.util import SafeEvaluator
from sardana.macroserver.msexception import UnknownEnv
from sardana.macroserver.macro import Hookable, Macro, Type, Table, List
from sardana.macroserver.scan.gscan import SScan, CTScan, HScan, \
MoveableDesc, CSScan, TScan
from sardana.util.motion import MotionPath
from sardana.util.tree import BranchNode
UNCONSTRAINED = "unconstrained"
StepMode = 's'
# TODO: change it to be more verbose e.g. ContinuousSwMode
ContinuousMode = 'c'
ContinuousHwTimeMode = 'ct'
HybridMode = 'h'
def getCallable(repr):
"""
returns a function .
Ideas: repr could be an URL for a file where the function is contained,
or be evaluable code, or a pickled function object,...
In any case, the return from it should be a callable of the form:
f(x1,x2) where x1, x2 are points in the moveable space and the return value
of f is True if the movement from x1 to x2 is allowed. False otherwise
"""
if repr == UNCONSTRAINED:
return lambda x1, x2: True
else:
return lambda: None
# TODO: remove starts
def _calculate_positions(moveable_node, start, end):
'''Function to calculate starting and ending positions on the physical
motors level.
:param moveable_node: (BaseNode) node representing a moveable.
Can be a BranchNode representing a PseudoMotor,
or a LeafNode representing a PhysicalMotor).
:param start: (float) starting position of the moveable
:param end: (float) ending position of the moveable
:return: (list<(float,float)>) a list of tuples comprising starting
and ending positions. List order is important and preserved.'''
start_positions = []
end_positions = []
if isinstance(moveable_node, BranchNode):
pseudo_node = moveable_node
moveable = pseudo_node.data
moveable_nodes = moveable_node.children
starts = moveable.calcPhysical(start)
ends = moveable.calcPhysical(end)
for moveable_node, start, end in zip(moveable_nodes, starts,
ends):
_start_positions, _end_positions = _calculate_positions(
moveable_node,
start, end)
start_positions += _start_positions
end_positions += _end_positions
else:
start_positions = [start]
end_positions = [end]
return start_positions, end_positions
class GeneralConditionManager:
"""Manage general condition.
To be used by scan generators to know whether to repeat a scan
point or not.
.. note::
The GeneralConditionManager class has been included in Sardana
on a provisional basis. Backwards incompatible changes
(up to and including its removal) may occur if
deemed necessary by the core developers.
"""
def __init__(self, macro):
self.macro = macro
self.repeat_nb = 0
try:
self.condition = macro.getEnv("GeneralCondition")
self.macro.gscan.deterministic_scan = False
except UnknownEnv:
self.condition = None
def need_repeat(self):
if self.condition is None:
return False
try:
repeat = self.macro.runMacro(self.condition)
except Exception as e:
self._macro.debug(
"Exception when running general condition macro",
exc_info=True
)
repeat = False
if repeat:
self.repeat_nb += 1
return repeat
[docs]class aNscan(Hookable):
"""N-dimensional scan. This is **not** meant to be called by the user,
but as a generic base to construct ascan, a2scan, a3scan,..."""
hints = {'scan': 'aNscan', 'allowsHooks': ('pre-scan', 'pre-move',
'post-move', 'pre-acq',
'post-acq', 'post-step',
'post-scan')}
# env = ('ActiveMntGrp',)
def _prepare(self, motorlist, startlist, endlist, scan_length, integ_time,
mode=StepMode, latency_time=0, **opts):
self.motors = motorlist
self.starts = numpy.array(startlist, dtype='d')
self.finals = numpy.array(endlist, dtype='d')
self.mode = mode
self.integ_time = integ_time
self.opts = opts
if len(self.motors) == self.starts.size == self.finals.size:
self.N = self.finals.size
else:
raise ValueError(
'Moveablelist, startlist and endlist must all be same length')
moveables = []
for m, start, final in zip(self.motors, self.starts, self.finals):
moveables.append(MoveableDesc(moveable=m, min_value=min(
start, final), max_value=max(start, final)))
moveables[0].is_reference = True
env = opts.get('env', {})
constrains = [getCallable(cns) for cns in opts.get(
'constrains', [UNCONSTRAINED])]
extrainfodesc = opts.get('extrainfodesc', [])
# Hooks are not always set at this point. We will call getHooks
# later on in the scan_loop
# self.pre_scan_hooks = self.getHooks('pre-scan')
# self.post_scan_hooks = self.getHooks('post-scan'
if mode == StepMode:
self.nr_interv = scan_length
self.nb_points = self.nr_interv + 1
self.interv_sizes = (self.finals - self.starts) / self.nr_interv
self.name = opts.get('name', 'a%iscan' % self.N)
self._gScan = SScan(self, self._stepGenerator,
moveables, env, constrains, extrainfodesc)
elif mode in [ContinuousMode, ContinuousHwTimeMode]:
# TODO: probably not 100% correct,
# the idea is to allow passing a list of waypoints
if isinstance(endlist[0], list):
self.waypoints = self.finals
else:
self.waypoints = [self.finals]
self.nr_waypoints = len(self.waypoints)
if mode == ContinuousMode:
self.slow_down = scan_length
# aNscans will only have two waypoints (the start and the final
# positions)
self.nr_waypoints = 2
self.way_lengths = (
self.finals - self.starts) / (self.nr_waypoints - 1)
self.name = opts.get('name', 'a%iscanc' % self.N)
self._gScan = CSScan(self, self._waypoint_generator,
self._period_generator, moveables, env,
constrains, extrainfodesc)
elif mode == ContinuousHwTimeMode:
if scan_length < 0:
self.do_last_point = False
self.nr_interv = -scan_length
self.nb_points = self.nr_interv
else:
self.do_last_point = True
self.nr_interv = scan_length
self.nb_points = self.nr_interv + 1
mg_name = self.getEnv('ActiveMntGrp')
mg = self.getMeasurementGroup(mg_name)
mg_latency_time = mg.getLatencyTime()
if mg_latency_time > latency_time:
self.info("Choosing measurement group latency time: %f" %
mg_latency_time)
latency_time = mg_latency_time
self.latency_time = latency_time
self.name = opts.get('name', 'a%iscanct' % self.N)
self._gScan = CTScan(self, self._waypoint_generator_hwtime,
moveables,
env,
constrains,
extrainfodesc)
elif mode == HybridMode:
self.nr_interv = scan_length
self.nb_points = self.nr_interv + 1
self.interv_sizes = (self.finals - self.starts) / self.nr_interv
self.name = opts.get('name', 'a%iscanh' % self.N)
self._gScan = HScan(self, self._stepGenerator,
moveables, env, constrains, extrainfodesc)
else:
raise ValueError('invalid value for mode %s' % mode)
# _data is the default member where the Macro class stores the data.
# Assign the date produced by GScan (or its subclasses) to it so all
# the Macro infrastructure related to the data works e.g. getter,
# property, etc. Ideally this should be done by the data setter
# but this is available in the Macro class and we inherit from it
# latter. More details in sardana-org/sardana#683.
self._data = self._gScan.data
def _stepGenerator(self, estimate=False):
step = {}
step["integ_time"] = self.integ_time
step["pre-move-hooks"] = self.getHooks('pre-move')
step["post-move-hooks"] = self.getHooks('post-move')
step["pre-acq-hooks"] = self.getHooks('pre-acq')
step["post-acq-hooks"] = self.getHooks('post-acq') + self.getHooks(
'_NOHINTS_')
step["post-step-hooks"] = self.getHooks('post-step')
step["check_func"] = []
gen_cond_manager = GeneralConditionManager(self)
for point_nb in range(self.nb_points):
step["point_id"] = point_nb + gen_cond_manager.repeat_nb
step["positions"] = self.starts + point_nb * self.interv_sizes
yield step
if estimate:
continue
while gen_cond_manager.need_repeat():
step["point_id"] = point_nb + gen_cond_manager.repeat_nb
# avoid moving motors to the same position
step["positions"] = [None] * self.N
yield step
def _waypoint_generator(self):
step = {}
step["pre-move-hooks"] = self.getHooks('pre-move')
step["post-move-hooks"] = self.getHooks('post-move')
step["check_func"] = []
step["slow_down"] = self.slow_down
for point_no in range(self.nr_waypoints):
step["positions"] = self.starts + point_no * self.way_lengths
step["waypoint_id"] = point_no
yield step
def _waypoint_generator_hwtime(self):
# CScan in its constructor populates a list of data structures - trees.
# Each tree represent one Moveables with its hierarchy of inferior
# moveables.
moveables_trees = self._gScan.get_moveables_trees()
step = {}
step["pre-move-hooks"] = self.getHooks('pre-move')
post_move_hooks = self.getHooks(
'post-move')
step["post-move-hooks"] = post_move_hooks
step["pre-acq-hooks"] = self.getHooks('pre-acq')
step["post-acq-hooks"] = self.getHooks('post-acq') + self.getHooks(
'_NOHINTS_')
step["check_func"] = []
step["active_time"] = self.nb_points * (self.integ_time
+ self.latency_time)
step["positions"] = []
step["start_positions"] = []
starts = self.starts
for point_no, waypoint in enumerate(self.waypoints):
for start, end, moveable_tree in zip(starts, waypoint,
moveables_trees):
moveable_root = moveable_tree.root()
start_positions, end_positions = _calculate_positions(
moveable_root, start, end)
step["start_positions"] += start_positions
step["positions"] += end_positions
step["waypoint_id"] = point_no
starts = waypoint
yield step
def _period_generator(self):
step = {}
step["integ_time"] = self.integ_time
step["pre-acq-hooks"] = self.getHooks('pre-acq')
step["post-acq-hooks"] = (self.getHooks('post-acq') +
self.getHooks('_NOHINTS_'))
step["post-step-hooks"] = self.getHooks('post-step')
step["check_func"] = []
step['extrainfo'] = {}
point_no = 0
while(True):
point_no += 1
step["point_id"] = point_no
yield step
def run(self, *args):
for step in self._gScan.step_scan():
yield step
def getTimeEstimation(self):
gScan = self._gScan
mode = self.mode
try:
it = gScan.generator(estimate=True)
except TypeError:
# this generator does not accept estimate kwarg
it = gScan.generator()
v_motors = gScan.get_virtual_motors()
curr_pos = gScan.motion.readPosition()
total_time = 0.0
if mode == StepMode:
# calculate motion time
max_step0_time, max_step_time = 0.0, 0.0
# first motion takes longer, all others should be "equal"
step0 = next(it)
for v_motor, start, stop, length in zip(v_motors, curr_pos,
step0['positions'],
self.interv_sizes):
path0 = MotionPath(v_motor, start, stop)
path = MotionPath(v_motor, 0, length)
max_step0_time = max(max_step0_time, path0.duration)
max_step_time = max(max_step_time, path.duration)
motion_time = max_step0_time + self.nr_interv * max_step_time
# calculate acquisition time
acq_time = self.nb_points * self.integ_time
total_time = motion_time + acq_time
elif mode == ContinuousMode:
total_time = gScan.waypoint_estimation()
# TODO: add time estimation for ContinuousHwTimeMode
return total_time
def getIntervalEstimation(self):
mode = self.mode
if mode in [StepMode, ContinuousHwTimeMode, HybridMode]:
return self.nr_interv
elif mode == ContinuousMode:
return self.nr_waypoints
def _get_nr_points(self):
msg = ("nr_points is deprecated since version 3.0.3. "
"Use nb_points instead.")
self.warning(msg)
return self.nb_points
nr_points = property(_get_nr_points)
@property
def gscan(self):
return self._gScan
[docs]class dNscan(aNscan):
"""
same as aNscan but it interprets the positions as being relative to the
current positions and upon completion, it returns the motors to their
original positions
"""
hints = copy.deepcopy(aNscan.hints)
hints['scan'] = 'dNscan'
def _prepare(self, motorlist, startlist, endlist, scan_length,
integ_time, mode=StepMode, **opts):
self._motion = self.getMotion([m.getName() for m in motorlist])
self.originalPositions = numpy.array(
self._motion.readPosition(force=True))
starts = numpy.array(startlist, dtype='d') + self.originalPositions
finals = numpy.array(endlist, dtype='d') + self.originalPositions
aNscan._prepare(self, motorlist, starts, finals,
scan_length, integ_time, mode=mode, **opts)
def do_restore(self):
self.info("Returning to start positions...")
self._motion.move(self.originalPositions)
[docs]class ascan(aNscan, Macro):
"""
Do an absolute scan of the specified motor.
ascan scans one motor, as specified by motor. The motor starts at the
position given by start_pos and ends at the position given by final_pos.
The step size is (start_pos-final_pos)/nr_interv. The number of data
points collected will be nr_interv+1. Count time is given by time which
if positive, specifies seconds and if negative, specifies monitor counts.
"""
param_def = [
['motor', Type.Moveable, None, 'Moveable to move'],
['start_pos', Type.Float, None, 'Scan start position'],
['final_pos', Type.Float, None, 'Scan final position'],
['nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time']
]
[docs] def prepare(self, motor, start_pos, final_pos, nr_interv, integ_time,
**opts):
self._prepare([motor], [start_pos], [final_pos],
nr_interv, integ_time, **opts)
[docs]class a2scan(aNscan, Macro):
"""
two-motor scan.
a2scan scans two motors, as specified by motor1 and motor2.
Each motor moves the same number of intervals with starting and ending
positions given by start_pos1 and final_pos1, start_pos2 and final_pos2,
respectively. The step size for each motor is:
(start_pos-final_pos)/nr_interv
The number of data points collected will be nr_interv+1.
Count time is given by time which if positive, specifies seconds and
if negative, specifies monitor counts.
"""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time']
]
[docs] def prepare(self, motor1, start_pos1, final_pos1, motor2, start_pos2,
final_pos2, nr_interv, integ_time, **opts):
self._prepare([motor1, motor2], [start_pos1, start_pos2], [
final_pos1, final_pos2], nr_interv, integ_time, **opts)
[docs]class a3scan(aNscan, Macro):
"""three-motor scan .
a3scan scans three motors, as specified by motor1, motor2 and motor3.
Each motor moves the same number of intervals with starting and ending
positions given by start_pos1 and final_pos1, start_pos2 and final_pos2,
start_pos3 and final_pos3, respectively.
The step size for each motor is (start_pos-final_pos)/nr_interv.
The number of data points collected will be nr_interv+1.
Count time is given by time which if positive, specifies seconds and
if negative, specifies monitor counts."""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['motor3', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos3', Type.Float, None, 'Scan start position 3'],
['final_pos3', Type.Float, None, 'Scan final position 3'],
['nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time']
]
[docs] def prepare(self, m1, s1, f1, m2, s2, f2, m3, s3, f3, nr_interv,
integ_time, **opts):
self._prepare([m1, m2, m3], [s1, s2, s3], [f1, f2, f3],
nr_interv, integ_time, **opts)
[docs]class a4scan(aNscan, Macro):
"""four-motor scan .
a4scan scans four motors, as specified by motor1, motor2, motor3 and
motor4.
Each motor moves the same number of intervals with starting and ending
positions given by start_posN and final_posN (for N=1,2,3,4).
The step size for each motor is (start_pos-final_pos)/nr_interv.
The number of data points collected will be nr_interv+1.
Count time is given by time which if positive, specifies seconds and
if negative, specifies monitor counts."""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['motor3', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos3', Type.Float, None, 'Scan start position 3'],
['final_pos3', Type.Float, None, 'Scan final position 3'],
['motor4', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos4', Type.Float, None, 'Scan start position 3'],
['final_pos4', Type.Float, None, 'Scan final position 3'],
['nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time']
]
[docs] def prepare(self, m1, s1, f1, m2, s2, f2, m3, s3, f3, m4, s4, f4,
nr_interv, integ_time, **opts):
self._prepare([m1, m2, m3, m4], [s1, s2, s3, s4], [
f1, f2, f3, f4], nr_interv, integ_time, **opts)
[docs]class amultiscan(aNscan, Macro):
"""
Multiple motor scan.
amultiscan scans N motors, as specified by motor1, motor2,...,motorN.
Each motor moves the same number of intervals with starting and ending
positions given by start_posN and final_posN (for N=1,2,...).
The step size for each motor is (start_pos-final_pos)/nr_interv.
The number of data points collected will be nr_interv+1.
Count time is given by time which if positive, specifies seconds and
if negative, specifies monitor counts.
Syntax:
amultiscan [[mot01 1 10][mot02 2 2]] 10 1
Without brackets it does not work.
"""
param_def = [
['motor_start_end_list',
[['motor', Type.Moveable, None, 'Moveable to move'],
['start', Type.Float, None, 'Starting position'],
['end', Type.Float, None, 'Final position']],
None, 'List of motor, start and end positions'],
['nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time']
]
[docs] def prepare(self, *args, **opts):
motors, starts, ends = zip(*args[0])
nr_interv = args[1]
integ_time = args[2]
self._prepare(motors, starts, ends, nr_interv, integ_time, **opts)
[docs]class dmultiscan(dNscan, Macro):
"""
Multiple motor scan relative to the starting positions.
dmultiscan scans N motors, as specified by motor1, motor2,...,motorN.
Each motor moves the same number of intervals If each motor is at a
position X before the scan begins, it will be scanned from X+start_posN
to X+final_posN (where N is one of 1,2,...)
The step size for each motor is (start_pos-final_pos)/nr_interv.
The number of data points collected will be nr_interv+1.
Count time is given by time which if positive, specifies seconds and
if negative, specifies monitor counts.
"""
param_def = [
['motor_start_end_list',
[['motor', Type.Moveable, None, 'Moveable to move'],
['start', Type.Float, None, 'Starting position'],
['end', Type.Float, None, 'Final position']],
None, 'List of motor, start and end positions'],
['nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time']
]
[docs] def prepare(self, *args, **opts):
motors = args[0:-2:3]
starts = args[1:-2:3]
ends = args[2:-2:3]
nr_interv = args[-2]
integ_time = args[-1]
self._prepare(motors, starts, ends, nr_interv, integ_time, **opts)
[docs]class dscan(dNscan, Macro):
"""motor scan relative to the starting position.
dscan scans one motor, as specified by motor. If motor motor is at a
position X before the scan begins, it will be scanned from X+start_pos
to X+final_pos. The step size is (start_pos-final_pos)/nr_interv.
The number of data points collected will be nr_interv+1. Count time is
given by time which if positive, specifies seconds and if negative,
specifies monitor counts. """
param_def = [
['motor', Type.Moveable, None, 'Moveable to move'],
['start_pos', Type.Float, None, 'Scan start position'],
['final_pos', Type.Float, None, 'Scan final position'],
['nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time']
]
[docs] def prepare(self, motor, start_pos, final_pos, nr_interv, integ_time,
**opts):
self._prepare([motor], [start_pos], [final_pos],
nr_interv, integ_time, **opts)
[docs]class d2scan(dNscan, Macro):
"""two-motor scan relative to the starting position.
d2scan scans two motors, as specified by motor1 and motor2.
Each motor moves the same number of intervals. If each motor is at a
position X before the scan begins, it will be scanned from X+start_posN
to X+final_posN (where N is one of 1,2).
The step size for each motor is (start_pos-final_pos)/nr_interv.
The number of data points collected will be nr_interv+1.
Count time is given by time which if positive, specifies seconds and
if negative, specifies monitor counts."""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time']
]
[docs] def prepare(self, motor1, start_pos1, final_pos1, motor2, start_pos2,
final_pos2, nr_interv, integ_time, **opts):
self._prepare([motor1, motor2], [start_pos1, start_pos2], [
final_pos1, final_pos2], nr_interv, integ_time, **opts)
[docs]class d3scan(dNscan, Macro):
"""three-motor scan .
d3scan scans three motors, as specified by motor1, motor2 and motor3.
Each motor moves the same number of intervals. If each motor is at a
position X before the scan begins, it will be scanned from X+start_posN
to X+final_posN (where N is one of 1,2,3)
The step size for each motor is (start_pos-final_pos)/nr_interv.
The number of data points collected will be nr_interv+1.
Count time is given by time which if positive, specifies seconds and
if negative, specifies monitor counts."""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['motor3', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos3', Type.Float, None, 'Scan start position 3'],
['final_pos3', Type.Float, None, 'Scan final position 3'],
['nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time']
]
[docs] def prepare(self, m1, s1, f1, m2, s2, f2, m3, s3, f3, nr_interv,
integ_time, **opts):
self._prepare([m1, m2, m3], [s1, s2, s3], [f1, f2, f3],
nr_interv, integ_time, **opts)
[docs]class d4scan(dNscan, Macro):
"""four-motor scan relative to the starting positions
a4scan scans four motors, as specified by motor1, motor2, motor3 and
motor4.
Each motor moves the same number of intervals. If each motor is at a
position X before the scan begins, it will be scanned from X+start_posN
to X+final_posN (where N is one of 1,2,3,4).
The step size for each motor is (start_pos-final_pos)/nr_interv.
The number of data points collected will be nr_interv+1.
Count time is given by time which if positive, specifies seconds and
if negative, specifies monitor counts.
Upon termination, the motors are returned to their starting positions.
"""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['motor3', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos3', Type.Float, None, 'Scan start position 3'],
['final_pos3', Type.Float, None, 'Scan final position 3'],
['motor4', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos4', Type.Float, None, 'Scan start position 3'],
['final_pos4', Type.Float, None, 'Scan final position 3'],
['nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time']
]
[docs] def prepare(self, m1, s1, f1, m2, s2, f2, m3, s3, f3, m4, s4, f4,
nr_interv, integ_time, **opts):
self._prepare([m1, m2, m3, m4], [s1, s2, s3, s4], [
f1, f2, f3, f4], nr_interv, integ_time, **opts)
[docs]class mesh(Macro, Hookable):
"""2d grid scan.
The mesh scan traces out a grid using motor1 and motor2.
The first motor scans from m1_start_pos to m1_final_pos using the specified
number of intervals. The second motor similarly scans from m2_start_pos
to m2_final_pos. Each point is counted for for integ_time seconds
(or monitor counts, if integ_time is negative).
The scan of motor1 is done at each point scanned by motor2. That is, the
first motor scan is nested within the second motor scan.
"""
hints = {'scan': 'mesh', 'allowsHooks': ('pre-scan', 'pre-move',
'post-move', 'pre-acq',
'post-acq', 'post-step',
'post-scan')}
env = ('ActiveMntGrp',)
param_def = [
['motor1', Type.Moveable, None, 'First motor to move'],
['m1_start_pos', Type.Float, None, 'Scan start position for first '
'motor'],
['m1_final_pos', Type.Float, None, 'Scan final position for first '
'motor'],
['m1_nr_interv', Type.Integer, None, 'Number of scan intervals'],
['motor2', Type.Moveable, None, 'Second motor to move'],
['m2_start_pos', Type.Float, None, 'Scan start position for second '
'motor'],
['m2_final_pos', Type.Float, None, 'Scan final position for second '
'motor'],
['m2_nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time'],
['bidirectional', Type.Boolean, False, 'Save time by scanning '
's-shaped']
]
[docs] def prepare(self, m1, m1_start_pos, m1_final_pos, m1_nr_interv,
m2, m2_start_pos, m2_final_pos, m2_nr_interv, integ_time,
bidirectional, **opts):
self.motors = [m1, m2]
self.starts = numpy.array([m1_start_pos, m2_start_pos], dtype='d')
self.finals = numpy.array([m1_final_pos, m2_final_pos], dtype='d')
self.nr_intervs = numpy.array([m1_nr_interv, m2_nr_interv], dtype='i')
self.nb_points = (m1_nr_interv + 1) * (m2_nr_interv + 1)
self.integ_time = integ_time
self.bidirectional_mode = bidirectional
self.name = opts.get('name', 'mesh')
generator = self._generator
moveables = []
for m, start, final in zip(self.motors, self.starts, self.finals):
moveables.append(MoveableDesc(moveable=m,
min_value=min(start, final),
max_value=max(start, final)))
moveables[0].is_reference = True
env = opts.get('env', {})
constrains = [getCallable(cns) for cns in opts.get(
'constrains', [UNCONSTRAINED])]
# Hooks are not always set at this point. We will call getHooks
# later on in the scan_loop
# self.pre_scan_hooks = self.getHooks('pre-scan')
# self.post_scan_hooks = self.getHooks('post-scan')
self._gScan = SScan(self, generator, moveables, env, constrains)
# _data is the default member where the Macro class stores the data.
# Assign the date produced by GScan (or its subclasses) to it so all
# the Macro infrastructure related to the data works e.g. getter,
# property, etc.
self.setData(self._gScan.data)
def _generator(self, estimate=False):
step = {}
step["integ_time"] = self.integ_time
step["pre-move-hooks"] = self.getHooks('pre-move')
step["post-move-hooks"] = self.getHooks('post-move')
step["pre-acq-hooks"] = self.getHooks('pre-acq')
step["post-acq-hooks"] = (self.getHooks('post-acq') +
self.getHooks('_NOHINTS_'))
step["post-step-hooks"] = self.getHooks('post-step')
step["check_func"] = []
m1start, m2start = self.starts
m1end, m2end = self.finals
points1, points2 = self.nr_intervs + 1
point_no = 1
m1_space = numpy.linspace(m1start, m1end, points1)
m1_space_inv = numpy.linspace(m1end, m1start, points1)
gen_cond_manager = GeneralConditionManager(self)
for i, m2pos in enumerate(numpy.linspace(m2start, m2end, points2)):
space = m1_space
if i % 2 != 0 and self.bidirectional_mode:
space = m1_space_inv
for m1pos in space:
step["positions"] = numpy.array([m1pos, m2pos])
# TODO: maybe another ID would be better? (e.g. "(A,B)")
step["point_id"] = point_no
point_no += 1
yield step
if estimate:
continue
while gen_cond_manager.need_repeat():
step["point_id"] = i + gen_cond_manager.repeat_nb
step["positions"] = [None, None]
yield step
[docs] def run(self, *args):
for step in self._gScan.step_scan():
yield step
@property
def gscan(self):
return self._gScan
class dmesh(mesh):
"""
2d relative grid scan.
The relative mesh scan traces out a grid using motor1 and motor2.
If first motor is at the position X before the scan begins, it will
be scanned from X+m1_start_pos to X+m1_final_pos using the specified
m1_nr_interv number of intervals. If the second motor is
at the position Y before the scan begins, it will be scanned
from Y+m2_start_pos to Y+m2_final_pos using the specified m2_nr_interv
number of intervals.
Each point is counted for the integ_time seconds (or monitor counts,
if integ_time is negative).
The scan of motor1 is done at each point scanned by motor2. That is, the
first motor scan is nested within the second motor scan.
Upon scan completion, it returns the motors to their original positions.
"""
hints = copy.deepcopy(mesh.hints)
hints['scan'] = 'dmesh'
env = copy.deepcopy(mesh.env)
param_def = [
['motor1', Type.Moveable, None, 'First motor to move'],
['m1_start_pos', Type.Float, None, 'Scan start position for first '
'motor'],
['m1_final_pos', Type.Float, None, 'Scan final position for first '
'motor'],
['m1_nr_interv', Type.Integer, None, 'Number of scan intervals'],
['motor2', Type.Moveable, None, 'Second motor to move'],
['m2_start_pos', Type.Float, None, 'Scan start position for second '
'motor'],
['m2_final_pos', Type.Float, None, 'Scan final position for second '
'motor'],
['m2_nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time'],
['bidirectional', Type.Boolean, False, 'Save time by scanning '
's-shaped']
]
def prepare(self, m1, m1_start_pos, m1_final_pos, m1_nr_interv,
m2, m2_start_pos, m2_final_pos, m2_nr_interv, integ_time,
bidirectional, **opts):
self._motion = self.getMotion([m1, m2])
self.originalPositions = numpy.array(
self._motion.readPosition(force=True))
start1 = self.originalPositions[0] + m1_start_pos
start2 = self.originalPositions[1] + m2_start_pos
final1 = self.originalPositions[0] + m1_final_pos
final2 = self.originalPositions[1] + m2_final_pos
mesh.prepare(self, m1, start1, final1, m1_nr_interv,
m2, start2, final2, m2_nr_interv, integ_time,
bidirectional, **opts)
def do_restore(self):
self.info("Returning to start positions...")
self._motion.move(self.originalPositions)
[docs]class fscan(Macro, Hookable):
"""
N-dimensional scan along user defined paths.
The motion path for each motor is defined through the evaluation of a
user-supplied function that is evaluated as a function of the independent
variables.
-independent variables are supplied through the indepvar string.
The syntax for indepvar is "x=expresion1,y=expresion2,..."
-If no indep vars need to be defined, write "!" or "*" or "None"
-motion path for motor is generated by evaluating the corresponding
function 'func'
-Count time is given by integ_time. If integ_time is a scalar, then
the same integ_time is used for all points. If it evaluates as an array
(with same length as the paths), fscan will assign a different integration
time to each acquisition point.
-If integ_time is positive, it specifies seconds and if negative, specifies
monitor counts.
IMPORTANT Notes:
-no spaces are allowed in the indepvar string.
-all funcs must evaluate to the same number of points
>>> fscan "x=[1,3,5,7,9],y=arange(5)" 0.1 motor1 x**2 motor2 sqrt(y*x+3)
>>> fscan "x=[1,3,5,7,9],y=arange(5)" "[0.1,0.2,0.3,0.4,0.5]" motor1 x**2 \
motor2 sqrt(y*x+3)
"""
# ['integ_time', Type.String, None, 'Integration time']
hints = {'scan': 'fscan',
'allowsHooks': ('pre-scan', 'pre-move', 'post-move', 'pre-acq',
'post-acq', 'post-step', 'post-scan')}
env = ('ActiveMntGrp',)
param_def = [
['indepvars', Type.String, None, 'Independent Variables'],
['integ_time', Type.String, None, 'Integration time'],
['motor_funcs',
[['motor', Type.Moveable, None, 'motor'],
['func', Type.String, None, 'curve defining path']],
None, 'List of motor and path curves']
]
[docs] def prepare(self, *args, **opts):
if args[0].lower() in ["!", "*", "none", None]:
indepvars = {}
else:
indepvars = SafeEvaluator({'dict': dict}).eval(
'dict(%s)' % args[0]) # create a dict containing the indepvars
self.motors = [item[0] for item in args[2]]
self.funcstrings = [item[1] for item in args[2]]
globals_lst = [dict(list(zip(indepvars, values)))
for values in zip(*list(indepvars.values()))]
self.paths = [[SafeEvaluator(globals).eval(
func) for globals in globals_lst] for func in self.funcstrings]
self._integ_time = numpy.array(eval(args[1]), dtype='d')
self.opts = opts
if len(self.motors) == len(self.paths) > 0:
self.N = len(self.motors)
else:
raise ValueError(
'Moveable and func lists must be non-empty and same length')
npoints = len(self.paths[0])
try:
# if everything is OK, the following lines should return a 2D array
# n which each motor path is a row.
# Typical failure is due to shape mismatch due to inconsistent
# input
self.paths = numpy.array(self.paths, dtype='d')
self.paths.reshape((self.N, npoints))
except Exception: # shape mismatch?
# try to give a meaningful description of the error
for p, fs in zip(self.paths, self.funcstrings):
if len(p) != npoints:
raise ValueError('"%s" and "%s" yield different number '
'of points (%i vs %i)' %
(self.funcstrings[0], fs, npoints,
len(p)))
raise # the problem wasn't a shape mismatch
self._nb_points = npoints
if self._integ_time.size == 1:
self._integ_time = self._integ_time * \
numpy.ones(self._nb_points) # extend integ_time
elif self._integ_time.size != self._nb_points:
raise ValueError('time_integ must either be a scalar or '
'length=npoints (%i)' % self._nb_points)
self.name = opts.get('name', 'fscan')
generator = self._generator
moveables = self.motors
env = opts.get('env', {})
constrains = [getCallable(cns) for cns in opts.get(
'constrains', [UNCONSTRAINED])]
# Hooks are not always set at this point. We will call getHooks
# later on in the scan_loop
# self.pre_scan_hooks = self.getHooks('pre-scan')
# self.post_scan_hooks = self.getHooks('post-scan'
self._gScan = SScan(self, generator, moveables, env, constrains)
# _data is the default member where the Macro class stores the data.
# Assign the date produced by GScan (or its subclasses) to it so all
# the Macro infrastructure related to the data works e.g. getter,
# property, etc.
self.setData(self._gScan.data)
def _generator(self, estimate=False):
step = {}
step["pre-move-hooks"] = self.getHooks('pre-move')
step["post-move-hooks"] = self.getHooks('post-move')
step["pre-acq-hooks"] = self.getHooks('pre-acq')
step["post-acq-hooks"] = (self.getHooks('post-acq') +
self.getHooks('_NOHINTS_'))
step["post-step-hooks"] = self.getHooks('post-step')
step["check_func"] = []
gen_cond_manager = GeneralConditionManager(self)
for i in range(self._nb_points):
step["positions"] = self.paths[:, i]
step["integ_time"] = self._integ_time[i]
step["point_id"] = i
yield step
if estimate:
continue
while gen_cond_manager.need_repeat():
step["point_id"] = i + gen_cond_manager.repeat_nb
# avoid moving motors to the same position
step["positions"] = [None] * self.N
yield step
[docs] def run(self, *args):
for step in self._gScan.step_scan():
yield step
def _get_nr_points(self):
msg = ("nr_points is deprecated since version 3.0.3. "
"Use nb_points instead.")
self.warning(msg)
return self.nb_points
nr_points = property(_get_nr_points)
@property
def gscan(self):
return self._gScan
class ascanh(aNscan, Macro):
"""Do an absolute scan of the specified motor.
ascan scans one motor, as specified by motor. The motor starts at the
position given by start_pos and ends at the position given by final_pos.
The step size is (start_pos-final_pos)/nr_interv. The number of data
points collected will be nr_interv+1. Count time is given by time which
if positive, specifies seconds and if negative, specifies monitor
counts. """
param_def = [
['motor', Type.Moveable, None, 'Moveable to move'],
['start_pos', Type.Float, None, 'Scan start position'],
['final_pos', Type.Float, None, 'Scan final position'],
['nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time']
]
def prepare(self, motor, start_pos, final_pos, nr_interv, integ_time,
**opts):
self._prepare([motor], [start_pos], [final_pos], nr_interv, integ_time,
mode=HybridMode, **opts)
[docs]class rscan(Macro, Hookable):
"""rscan.
Do an absolute scan of the specified motor with different number of intervals for each region.
It uses the gscan framework.
"""
hints = {'scan': 'rscan', 'allowsHooks': ('pre-scan', 'pre-move',
'post-move', 'pre-acq',
'post-acq', 'post-step',
'post-scan')}
# env = ('ActiveMntGrp',)
param_def = [
['motor', Type.Moveable, None, 'Motor to move'],
['start_pos', Type.Float, None, 'Start position'],
['regions',
[['next_pos', Type.Float, None, 'next position'],
['region_nr_intervals', Type.Integer, None,
'Region number of intervals']],
None, 'List of tuples: (next_pos, region_nr_intervals'],
['integ_time', Type.Float, None, 'Integration time']
]
[docs] def prepare(self, motor, start_pos, regions, integ_time, **opts):
self.name = 'rscan'
self.integ_time = integ_time
self.start_pos = start_pos
self.regions = regions
self.regions_count = len(self.regions) // 2
generator = self._generator
self.motors = [motor]
env = opts.get('env', {})
constrains = []
self._gScan = SScan(self, generator, self.motors, env, constrains)
self._data = self._gScan.data
def _generator(self, estimate=False):
step = {}
step["integ_time"] = self.integ_time
step["pre-move-hooks"] = self.getHooks('pre-move')
step["post-move-hooks"] = self.getHooks('post-move')
step["pre-acq-hooks"] = self.getHooks('pre-acq')
step["post-acq-hooks"] = self.getHooks('post-acq') + self.getHooks(
'_NOHINTS_')
step["post-step-hooks"] = self.getHooks('post-step')
point_id = 0
region_start = self.start_pos
gen_cond_manager = GeneralConditionManager(self)
for r in range(len(self.regions)):
region_stop, region_nr_intervals = self.regions[
r][0], self.regions[r][1]
positions = numpy.linspace(
region_start, region_stop, region_nr_intervals + 1)
if point_id != 0:
# positions must be calculated from the start to the end of the region
# but after the first region, the 'start' point must not be
# repeated
positions = positions[1:]
for p in positions:
step['positions'] = [p]
step['point_id'] = point_id
point_id += 1
yield step
if estimate:
continue
while gen_cond_manager.need_repeat():
step["point_id"] = point_id + gen_cond_manager.repeat_nb
point_id = step["point_id"]
# avoid moving motors to the same position
step["positions"] = [None]
yield step
region_start = region_stop
[docs] def run(self, *args):
for step in self._gScan.step_scan():
yield step
@property
def gscan(self):
return self._gScan
[docs]class r2scan(Macro, Hookable):
"""r2scan.
Do an absolute scan of the specified motors with different number of intervals for each region.
It uses the gscan framework. All the motors will be drived to the same position in each step
"""
hints = {'scan': 'r2scan', 'allowsHooks': ('pre-scan', 'pre-move',
'post-move', 'pre-acq',
'post-acq', 'post-step',
'post-scan')}
# env = ('ActiveMntGrp',)
param_def = [
['motor1', Type.Moveable, None, 'Motor to move'],
['motor2', Type.Moveable, None, 'Motor to move'],
['start_pos', Type.Float, None, 'Start position'],
['regions',
[['next_pos', Type.Float, None, 'next position'],
['region_nr_intervals', Type.Integer, None,
'Region number of intervals']],
None, 'List of tuples: (next_pos, region_nr_intervals'],
['integ_time', Type.Float, None, 'Integration time'],
]
[docs] def prepare(self, motor1, motor2, start_pos, regions, integ_time, **opts):
self.name = 'r2scan'
self.integ_time = integ_time
self.start_pos = start_pos
self.regions = regions
self.regions_count = len(self.regions) // 2
generator = self._generator
self.motors = [motor1, motor2]
env = opts.get('env', {})
constrains = []
self._gScan = SScan(self, generator, self.motors, env, constrains)
self._data = self._gScan.data
def _generator(self, estimate=False):
step = {}
step["integ_time"] = self.integ_time
step["pre-move-hooks"] = self.getHooks('pre-move')
step["post-move-hooks"] = self.getHooks('post-move')
step["pre-acq-hooks"] = self.getHooks('pre-acq')
step["post-acq-hooks"] = self.getHooks('post-acq') + self.getHooks(
'_NOHINTS_')
step["post-step-hooks"] = self.getHooks('post-step')
point_id = 0
region_start = self.start_pos
gen_cond_manager = GeneralConditionManager(self)
for r in range(len(self.regions)):
region_stop, region_nr_intervals = self.regions[
r][0], self.regions[r][1]
positions = numpy.linspace(
region_start, region_stop, region_nr_intervals + 1)
if point_id != 0:
# positions must be calculated from the start to the end of the region
# but after the first region, the 'start' point must not be
# repeated
positions = positions[1:]
for p in positions:
step['positions'] = [p, p]
step['point_id'] = point_id
point_id += 1
yield step
if estimate:
continue
while gen_cond_manager.need_repeat():
step["point_id"] = point_id + gen_cond_manager.repeat_nb
point_id = step["point_id"]
# avoid moving motors to the same position
step["positions"] = [None, None]
yield step
region_start = region_stop
[docs] def run(self, *args):
for step in self._gScan.step_scan():
yield step
@property
def gscan(self):
return self._gScan
[docs]class r3scan(Macro, Hookable):
"""r3scan.
Do an absolute scan of the specified motors with different number of
intervals for each region. It uses the gscan framework.
All the motors will be drived to the same position in each step
"""
hints = {'scan': 'r3scan', 'allowsHooks': ('pre-scan', 'pre-move',
'post-move', 'pre-acq',
'post-acq', 'post-step',
'post-scan')}
# env = ('ActiveMntGrp',)
param_def = [
['motor1', Type.Moveable, None, 'Motor to move'],
['motor2', Type.Moveable, None, 'Motor to move'],
['motor3', Type.Moveable, None, 'Motor to move'],
['start_pos', Type.Float, None, 'Start position'],
['regions',
[['next_pos', Type.Float, None, 'next position'],
['region_nr_intervals', Type.Integer, None,
'Region number of intervals']],
None, 'List of tuples: (next_pos, region_nr_intervals'],
['integ_time', Type.Float, None, 'Integration time'],
]
[docs] def prepare(self, motor1, motor2, motor3, start_pos, regions, integ_time, **opts):
self.name = 'r3scan'
self.integ_time = integ_time
self.start_pos = start_pos
self.regions = regions
self.regions_count = len(self.regions) // 2
generator = self._generator
self.motors = [motor1, motor2, motor3]
env = opts.get('env', {})
constrains = []
self._gScan = SScan(self, generator, self.motors, env, constrains)
self._data = self._gScan.data
def _generator(self, estimate=False):
step = {}
step["integ_time"] = self.integ_time
step["pre-move-hooks"] = self.getHooks('pre-move')
step["post-move-hooks"] = self.getHooks('post-move')
step["pre-acq-hooks"] = self.getHooks('pre-acq')
step["post-acq-hooks"] = self.getHooks('post-acq') + self.getHooks(
'_NOHINTS_')
step["post-step-hooks"] = self.getHooks('post-step')
point_id = 0
region_start = self.start_pos
gen_cond_manager = GeneralConditionManager(self)
for r in range(len(self.regions)):
region_stop, region_nr_intervals = self.regions[
r][0], self.regions[r][1]
positions = numpy.linspace(
region_start, region_stop, region_nr_intervals + 1)
if point_id != 0:
# positions must be calculated from the start to the end of the region
# but after the first region, the 'start' point must not be
# repeated
positions = positions[1:]
for p in positions:
step['positions'] = [p, p, p]
step['point_id'] = point_id
point_id += 1
yield step
if estimate:
continue
while gen_cond_manager.need_repeat():
step["point_id"] = point_id + gen_cond_manager.repeat_nb
point_id = step["point_id"]
# avoid moving motors to the same position
step["positions"] = [None, None, None]
yield step
region_start = region_stop
[docs] def run(self, *args):
for step in self._gScan.step_scan():
yield step
@property
def gscan(self):
return self._gScan
[docs]class scanhist(Macro):
"""Shows scan history information. Give optional parameter scan number to
display details about a specific scan"""
param_def = [
['scan number', Type.Integer, -1,
'scan number. [default=-1 meaning show all scans]'],
]
[docs] def run(self, scan_number):
try:
hist = self.getEnv("ScanHistory")
except UnknownEnv:
print("No scan recorded in history")
return
if scan_number < 0:
self.show_all(hist)
else:
self.show_one(hist, scan_number)
def show_one(self, hist, scan_number):
item = None
for h in hist:
if h['serialno'] == scan_number:
item = h
break
if item is None:
self.warning("Could not find scan number %s", scan_number)
return
serialno, title = h['serialno'], h['title']
start = datetime.datetime.fromtimestamp(h['startts'])
end = datetime.datetime.fromtimestamp(h['endts'])
total_time = end - start
start, end, total_time = start.ctime(), end.ctime(), str(total_time)
scan_dir, scan_file = h['ScanDir'], h['ScanFile']
deadtime = '%.1f%%' % h['deadtime']
setuptime = '%.1f%%' % h['setuptime']
user = h['user']
store = "Not stored!"
if scan_dir is not None and scan_file is not None:
if isinstance(scan_file, str):
store = os.path.join(scan_dir, scan_file)
else:
store = scan_dir + os.path.sep + str(scan_file)
channels = ", ".join(h['channels'])
cols = ["#", "Title", "Start time", "End time", "Took", "Dead time",
"Setup time", "User", "Stored", "Channels"]
data = [serialno, title, start, end, total_time, deadtime, setuptime, user, store,
channels]
table = Table([data], row_head_str=cols, row_head_fmt='%*s',
elem_fmt=['%-*s'],
col_sep=' : ')
for line in table.genOutput():
self.output(line)
def show_all(self, hist):
cols = "#", "Title", "Start time", "End time", "Stored"
width = -1, -1, -1, -1, -1
out = List(cols, max_col_width=width)
today = datetime.datetime.today().date()
for h in hist:
start = datetime.datetime.fromtimestamp(h['startts'])
if start.date() == today:
start = start.time().strftime("%H:%M:%S")
else:
start = start.strftime("%Y-%m-%d %H:%M:%S")
end = datetime.datetime.fromtimestamp(h['endts'])
if end.date() == today:
end = end.time().strftime("%H:%M:%S")
else:
end = end.strftime("%Y-%m-%d %H:%M:%S")
scan_file = h['ScanFile']
store = "Not stored!"
if scan_file is not None:
store = ", ".join(scan_file)
row = h['serialno'], h['title'], start, end, store
out.appendRow(row)
for line in out.genOutput():
self.output(line)
[docs]class ascanc(aNscan, Macro):
"""Do an absolute continuous scan of the specified motor.
ascanc scans one motor, as specified by motor."""
param_def = [
['motor', Type.Moveable, None, 'Moveable to move'],
['start_pos', Type.Float, None, 'Scan start position'],
['final_pos', Type.Float, None, 'Scan final position'],
['integ_time', Type.Float, None, 'Integration time'],
['slow_down', Type.Float, 1, 'global scan slow down factor (0, 1]'],
]
[docs] def prepare(self, motor, start_pos, final_pos, integ_time, slow_down,
**opts):
self._prepare([motor], [start_pos], [final_pos], slow_down,
integ_time, mode=ContinuousMode, **opts)
[docs]class a2scanc(aNscan, Macro):
"""two-motor continuous scan"""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['integ_time', Type.Float, None, 'Integration time'],
['slow_down', Type.Float, 1, 'global scan slow down factor (0, 1]'],
]
[docs] def prepare(self, motor1, start_pos1, final_pos1, motor2, start_pos2,
final_pos2, integ_time, slow_down, **opts):
self._prepare([motor1, motor2], [start_pos1, start_pos2],
[final_pos1, final_pos2], slow_down, integ_time,
mode=ContinuousMode, **opts)
[docs]class a3scanc(aNscan, Macro):
"""three-motor continuous scan"""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['motor3', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos3', Type.Float, None, 'Scan start position 3'],
['final_pos3', Type.Float, None, 'Scan final position 3'],
['integ_time', Type.Float, None, 'Integration time'],
['slow_down', Type.Float, 1, 'global scan slow down factor (0, 1]'],
]
[docs] def prepare(self, m1, s1, f1, m2, s2, f2, m3, s3, f3, integ_time,
slow_down, **opts):
self._prepare([m1, m2, m3], [s1, s2, s3], [f1, f2, f3], slow_down,
integ_time, mode=ContinuousMode, **opts)
[docs]class a4scanc(aNscan, Macro):
"""four-motor continuous scan"""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['motor3', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos3', Type.Float, None, 'Scan start position 3'],
['final_pos3', Type.Float, None, 'Scan final position 3'],
['motor4', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos4', Type.Float, None, 'Scan start position 3'],
['final_pos4', Type.Float, None, 'Scan final position 3'],
['integ_time', Type.Float, None, 'Integration time'],
['slow_down', Type.Float, 1, 'global scan slow down factor (0, 1]'],
]
[docs] def prepare(self, m1, s1, f1, m2, s2, f2, m3, s3, f3, m4, s4, f4,
integ_time, slow_down, **opts):
self._prepare([m1, m2, m3, m4], [s1, s2, s3, s4], [f1, f2, f3, f4],
slow_down, integ_time, mode=ContinuousMode, **opts)
class dNscanc(dNscan):
def do_restore(self):
# set velocities to maximum and then move to initial positions
for moveable in self.motors:
self._gScan.set_max_top_velocity(moveable)
dNscan.do_restore(self)
[docs]class dscanc(dNscanc, Macro):
"""continuous motor scan relative to the starting position."""
param_def = [
['motor', Type.Moveable, None, 'Moveable to move'],
['start_pos', Type.Float, None, 'Scan start position'],
['final_pos', Type.Float, None, 'Scan final position'],
['integ_time', Type.Float, None, 'Integration time'],
['slow_down', Type.Float, 1, 'global scan slow down factor (0, 1]'],
]
[docs] def prepare(self, motor, start_pos, final_pos, integ_time, slow_down,
**opts):
self._prepare([motor], [start_pos], [final_pos], slow_down, integ_time,
mode=ContinuousMode, **opts)
[docs]class d2scanc(dNscanc, Macro):
"""continuous two-motor scan relative to the starting positions"""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['integ_time', Type.Float, None, 'Integration time'],
['slow_down', Type.Float, 1, 'global scan slow down factor (0, 1]'],
]
[docs] def prepare(self, motor1, start_pos1, final_pos1, motor2, start_pos2,
final_pos2, integ_time, slow_down, **opts):
self._prepare([motor1, motor2], [start_pos1, start_pos2],
[final_pos1, final_pos2], slow_down, integ_time,
mode=ContinuousMode, **opts)
[docs]class d3scanc(dNscanc, Macro):
"""continuous three-motor scan"""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['motor3', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos3', Type.Float, None, 'Scan start position 3'],
['final_pos3', Type.Float, None, 'Scan final position 3'],
['integ_time', Type.Float, None, 'Integration time'],
['slow_down', Type.Float, 1, 'global scan slow down factor (0, 1]'],
]
[docs] def prepare(self, m1, s1, f1, m2, s2, f2, m3, s3, f3, integ_time,
slow_down, **opts):
self._prepare([m1, m2, m3], [s1, s2, s3], [f1, f2, f3], slow_down,
integ_time, mode=ContinuousMode, **opts)
[docs]class d4scanc(dNscanc, Macro):
"""continuous four-motor scan relative to the starting positions"""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['motor3', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos3', Type.Float, None, 'Scan start position 3'],
['final_pos3', Type.Float, None, 'Scan final position 3'],
['motor4', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos4', Type.Float, None, 'Scan start position 3'],
['final_pos4', Type.Float, None, 'Scan final position 3'],
['integ_time', Type.Float, None, 'Integration time'],
['slow_down', Type.Float, 1, 'global scan slow down factor (0, 1]'],
]
[docs] def prepare(self, m1, s1, f1, m2, s2, f2, m3, s3, f3, m4, s4, f4,
integ_time, slow_down, **opts):
self._prepare([m1, m2, m3, m4], [s1, s2, s3, s4], [f1, f2, f3, f4],
slow_down, integ_time, mode=ContinuousMode, **opts)
[docs]class meshc(Macro, Hookable):
"""2d grid scan. scans continuous"""
hints = {'scan': 'mesh', 'allowsHooks': ('pre-scan', 'pre-move',
'post-move', 'pre-acq',
'post-acq', 'post-step',
'post-scan')}
env = ('ActiveMntGrp',)
param_def = [
['motor1', Type.Moveable, None, 'First motor to move'],
['m1_start_pos', Type.Float, None, 'Scan start position for first '
'motor'],
['m1_final_pos', Type.Float, None, 'Scan final position for first '
'motor'],
['slow_down', Type.Float, None, 'global scan slow down factor (0, 1]'],
['motor2', Type.Moveable, None, 'Second motor to move'],
['m2_start_pos', Type.Float, None, 'Scan start position for second '
'motor'],
['m2_final_pos', Type.Float, None, 'Scan final position for second '
'motor'],
['m2_nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time'],
['bidirectional', Type.Boolean, False, 'Save time by scanning '
's-shaped']
]
[docs] def prepare(self, m1, m1_start_pos, m1_final_pos, slow_down,
m2, m2_start_pos, m2_final_pos, m2_nr_interv, integ_time,
bidirectional, **opts):
self.motors = [m1, m2]
self.slow_down = slow_down
self.starts = numpy.array([m1_start_pos, m2_start_pos], dtype='d')
self.finals = numpy.array([m1_final_pos, m2_final_pos], dtype='d')
self.m2_nr_interv = m2_nr_interv
self.integ_time = integ_time
self.bidirectional_mode = bidirectional
self.nr_waypoints = m2_nr_interv + 1
self.name = opts.get('name', 'meshc')
moveables = []
for m, start, final in zip(self.motors, self.starts, self.finals):
moveables.append(MoveableDesc(moveable=m, min_value=min(
start, final), max_value=max(start, final)))
moveables[0].is_reference = True
env = opts.get('env', {})
constrains = [getCallable(cns) for cns in opts.get(
'constrains', [UNCONSTRAINED])]
extrainfodesc = opts.get('extrainfodesc', [])
# Hooks are not always set at this point. We will call getHooks
# later on in the scan_loop
# self.pre_scan_hooks = self.getHooks('pre-scan')
# self.post_scan_hooks = self.getHooks('post-scan'
self._gScan = CSScan(self, self._waypoint_generator,
self._period_generator, moveables, env,
constrains, extrainfodesc)
self._gScan.frozen_motors = [m2]
# _data is the default member where the Macro class stores the data.
# Assign the date produced by GScan (or its subclasses) to it so all
# the Macro infrastructure related to the data works e.g. getter,
# property, etc.
self.setData(self._gScan.data)
def _waypoint_generator(self):
step = {}
step["pre-move-hooks"] = self.getHooks('pre-move')
step["post-move-hooks"] = self.getHooks('post-move')
step["check_func"] = []
step["slow_down"] = self.slow_down
points2 = self.m2_nr_interv + 1
m1start, m2start = self.starts
m1end, m2end = self.finals
point_no = 1
for i, m2pos in enumerate(numpy.linspace(m2start, m2end, points2)):
start, end = m1start, m1end
if i % 2 != 0 and self.bidirectional_mode:
start, end = m1end, m1start
step["start_positions"] = numpy.array([start, m2pos])
step["positions"] = numpy.array([end, m2pos])
step["point_id"] = point_no
point_no += 1
yield step
def _period_generator(self):
step = {}
step["integ_time"] = self.integ_time
step["pre-acq-hooks"] = self.getHooks('pre-acq')
step["post-acq-hooks"] = (self.getHooks('post-acq') +
self.getHooks('_NOHINTS_'))
step["post-step-hooks"] = self.getHooks('post-step')
step["check_func"] = []
step['extrainfo'] = {}
point_no = 0
while(True):
point_no += 1
step["point_id"] = point_no
yield step
[docs] def run(self, *args):
for step in self._gScan.step_scan():
yield step
def getTimeEstimation(self):
return self._gScan.waypoint_estimation()
def getIntervalEstimation(self):
return self.nr_waypoints
@property
def gscan(self):
return self._gScan
class dmeshc(meshc):
"""2d relative continuous grid scan.
The relative mesh scan traces out a grid using motor1 and motor2.
If first motor is at the position X before the scan begins, it will
be continuously scanned from X+m1_start_pos to X+m1_final_pos.
If the second motor is at the position Y before the scan begins,
it will be discrete scanned from Y+m2_start_pos to Y+m2_final_pos
using the specified m2_nr_interv number of intervals.
The scan considers the accel. and decel. times of the motor1, so the
counts (for the integ_time seconds or monitor counts,
if integ_time is negative) are executed while motor1 is moving
with the constant velocity.
Upon scan completion, it returns the motors to their original positions.
"""
hints = copy.deepcopy(meshc.hints)
hints['scan'] = 'dmeshc'
env = copy.deepcopy(meshc.env)
param_def = [
['motor1', Type.Moveable, None, 'First motor to move'],
['m1_start_pos', Type.Float, None, 'Scan start position for first '
'motor'],
['m1_final_pos', Type.Float, None, 'Scan final position for first '
'motor'],
['slow_down', Type.Float, None, 'global scan slow down factor (0, 1]'],
['motor2', Type.Moveable, None, 'Second motor to move'],
['m2_start_pos', Type.Float, None, 'Scan start position for second '
'motor'],
['m2_final_pos', Type.Float, None, 'Scan final position for second '
'motor'],
['m2_nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time'],
['bidirectional', Type.Boolean, False, 'Save time by scanning '
's-shaped']
]
def prepare(self, m1, m1_start_pos, m1_final_pos, slow_down,
m2, m2_start_pos, m2_final_pos, m2_nr_interv, integ_time,
bidirectional, **opts):
self._motion = self.getMotion([m1, m2])
self.originalPositions = numpy.array(
self._motion.readPosition(force=True))
start1 = self.originalPositions[0] + m1_start_pos
start2 = self.originalPositions[1] + m2_start_pos
final1 = self.originalPositions[0] + m1_final_pos
final2 = self.originalPositions[1] + m2_final_pos
meshc.prepare(self, m1, start1, final1, slow_down,
m2, start2, final2, m2_nr_interv, integ_time,
bidirectional, **opts)
def do_restore(self):
self.info("Returning to start positions...")
self._motion.move(self.originalPositions)
class aNscanct(aNscan):
"""N-dimensional continuous scan. This is **not** meant to be called by
the user, but as a generic base to construct ascanct, a2scanct, a3scanct,
..."""
hints = {"scan": "aNscanct",
"allowsHooks": ("pre-scan", "pre-configuration",
"post-configuration", "pre-move",
"post-move", "pre-acq", "pre-start",
"post-acq", "pre-cleanup", "post-cleanup",
"post-scan")}
[docs]class ascanct(aNscanct, Macro):
"""Do an absolute continuous scan of the specified motor.
ascanct scans one motor, as specified by motor. The motor starts before the
position given by start_pos in order to reach the constant velocity at the
start_pos and finishes at the position after the final_pos in order to
maintain the constant velocity until acquiring the last point."""
param_def = [['motor', Type.Moveable, None, 'Moveable name'],
['start_pos', Type.Float, None, 'Scan start position'],
['final_pos', Type.Float, None, 'Scan final position'],
['nr_interv', Type.Integer, None, ('Number of scan intervals.'
'Negative value indicate skipping the last point.')],
['integ_time', Type.Float, None, 'Integration time'],
['latency_time', Type.Float, 0, 'Latency time']]
[docs] def prepare(self, motor, start_pos, final_pos, nr_interv,
integ_time, latency_time, **opts):
self._prepare([motor], [start_pos], [final_pos], nr_interv,
integ_time, mode=ContinuousHwTimeMode,
latency_time=latency_time, **opts)
[docs]class a2scanct(aNscanct, Macro):
"""Two-motor continuous scan.
a2scanct scans two motors, as specified by motor1 and motor2. Each motor
starts before the position given by its start_pos in order to reach the
constant velocity at its start_pos and finishes at the position after
its final_pos in order to maintain the constant velocity until acquiring
the last point."""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['nr_interv', Type.Integer, None, ('Number of scan intervals'
'Negative value indicate skipping the last point.')],
['integ_time', Type.Float, None, 'Integration time'],
['latency_time', Type.Float, 0, 'Latency time']]
[docs] def prepare(self, m1, s1, f1, m2, s2, f2, nr_interv,
integ_time, latency_time, **opts):
self._prepare([m1, m2], [s1, s2], [f1, f2], nr_interv,
integ_time, mode=ContinuousHwTimeMode,
latency_time=latency_time, **opts)
[docs]class a3scanct(aNscanct, Macro):
"""Three-motor continuous scan.
a2scanct scans three motors, as specified by motor1, motor2 and motor3.
Each motor starts before the position given by its start_pos in order to
reach the constant velocity at its start_pos and finishes at the position
after its final_pos in order to maintain the constant velocity until
acquiring the last point."""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['motor3', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos3', Type.Float, None, 'Scan start position 3'],
['final_pos3', Type.Float, None, 'Scan final position 3'],
['nr_interv', Type.Integer, None, ('Number of scan intervals. '
'Negative value indicate skipping the last point.')],
['integ_time', Type.Float, None, 'Integration time'],
['latency_time', Type.Float, 0, 'Latency time']]
[docs] def prepare(self, m1, s1, f1, m2, s2, f2, m3, s3, f3, nr_interv,
integ_time, latency_time, **opts):
self._prepare([m1, m2, m3], [s1, s2, s3], [f1, f2, f3], nr_interv,
integ_time, mode=ContinuousHwTimeMode,
latency_time=latency_time, **opts)
[docs]class a4scanct(aNscan, Macro):
"""Four-motor continuous scan.
a2scanct scans four motors, as specified by motor1, motor2, motor3 and
motor4. Each motor starts before the position given by its start_pos in
order to reach the constant velocity at its start_pos and finishes at the
position after its final_pos in order to maintain the constant velocity
until acquiring the last point."""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['motor3', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos3', Type.Float, None, 'Scan start position 3'],
['final_pos3', Type.Float, None, 'Scan final position 3'],
['motor4', Type.Moveable, None, 'Moveable 4 to move'],
['start_pos4', Type.Float, None, 'Scan start position 4'],
['final_pos4', Type.Float, None, 'Scan final position 4'],
['nr_interv', Type.Integer, None, ('Number of scan intervals. '
'Negative value indicate skipping the last point.')],
['integ_time', Type.Float, None, 'Integration time'],
['latency_time', Type.Float, 0, 'Latency time']]
[docs] def prepare(self, m1, s1, f1, m2, s2, f2, m3, s3, f3, m4, s4, f4,
nr_interv, integ_time, latency_time, **opts):
self._prepare([m1, m2, m3, m4], [s1, s2, s3, s4], [f1, f2, f3, f4],
nr_interv, integ_time, mode=ContinuousHwTimeMode,
latency_time=latency_time, **opts)
class dNscanct(dNscan):
"""N-dimensional continuous scan. This is **not** meant to be called by
the user, but as a generic base to construct ascanct, a2scanct, a3scanct,
..."""
hints = {"scan": "dNscanct",
"allowsHooks": ("pre-scan", "pre-configuration",
"post-configuration", "pre-move",
"post-move", "pre-acq", "pre-start",
"post-acq", "pre-cleanup", "post-cleanup",
"post-scan")}
class dscanct(dNscanct, Macro):
"""Do an a relative continuous motor scan,
dscanct scans a motor, as specified by motor1.
The Motor starts before the position given by its start_pos in order to
reach the constant velocity at its start_pos and finishes at the position
after its final_pos in order to maintain the constant velocity until
acquiring the last point."""
param_def = [['motor', Type.Moveable, None, 'Moveable name'],
['start_pos', Type.Float, None, 'Scan start position'],
['final_pos', Type.Float, None, 'Scan final position'],
['nr_interv', Type.Integer, None, ('Number of scan intervals. '
'Negative value indicate skipping the last point.')],
['integ_time', Type.Float, None, 'Integration time'],
['latency_time', Type.Float, 0, 'Latency time']]
def prepare(self, motor, start_pos, final_pos, nr_interv,
integ_time, latency_time, **opts):
self._prepare([motor], [start_pos], [final_pos], nr_interv,
integ_time, mode=ContinuousHwTimeMode,
latency_time=latency_time, **opts)
class d2scanct(dNscanct, Macro):
"""continuous two-motor scan relative to the starting positions,
d2scanct scans three motors, as specified by motor1 and motor2.
Each motor starts before the position given by its start_pos in order to
reach the constant velocity at its start_pos and finishes at the position
after its final_pos in order to maintain the constant velocity until
acquiring the last point.
"""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['nr_interv', Type.Integer, None, ('Number of scan intervals. '
'Negative value indicate skipping the last point.')],
['integ_time', Type.Float, None, 'Integration time'],
]
def prepare(self, m1, s1, f1, m2, s2, f2, integ_time, nr_interv, **opts):
self._prepare([m1, m2], [s1, s2], [f1, f2], nr_interv, integ_time,
mode=ContinuousHwTimeMode, **opts)
class d3scanct(dNscanct, Macro):
"""continuous three-motor scan relative to the starting positions,
d3scanct scans three motors, as specified by motor1, motor2 and motor3.
Each motor starts before the position given by its start_pos in order to
reach the constant velocity at its start_pos and finishes at the position
after its final_pos in order to maintain the constant velocity until
acquiring the last point.
"""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['motor3', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos3', Type.Float, None, 'Scan start position 3'],
['final_pos3', Type.Float, None, 'Scan final position 3'],
['nr_interv', Type.Integer, None, ('Number of scan intervals. '
'Negative value indicate skipping the last point.')],
['integ_time', Type.Float, None, 'Integration time']
]
def prepare(self, m1, s1, f1, m2, s2, f2, m3, s3, f3, nr_interv,
integ_time, **opts):
self._prepare([m1, m2, m3], [s1, s2, s3], [f1, f2, f3], nr_interv,
integ_time, mode=ContinuousHwTimeMode, **opts)
class d4scanct(dNscanct, Macro):
"""continuous four-motor scan relative to the starting positions,
d4scanct scans three motors, as specified by motor1, motor2, motor3 and
motor4.
Each motor starts before the position given by its start_pos in order to
reach the constant velocity at its start_pos and finishes at the position
after its final_pos in order to maintain the constant velocity until
acquiring the last point."""
param_def = [
['motor1', Type.Moveable, None, 'Moveable 1 to move'],
['start_pos1', Type.Float, None, 'Scan start position 1'],
['final_pos1', Type.Float, None, 'Scan final position 1'],
['motor2', Type.Moveable, None, 'Moveable 2 to move'],
['start_pos2', Type.Float, None, 'Scan start position 2'],
['final_pos2', Type.Float, None, 'Scan final position 2'],
['motor3', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos3', Type.Float, None, 'Scan start position 3'],
['final_pos3', Type.Float, None, 'Scan final position 3'],
['motor4', Type.Moveable, None, 'Moveable 3 to move'],
['start_pos4', Type.Float, None, 'Scan start position 3'],
['final_pos4', Type.Float, None, 'Scan final position 3'],
['nr_interv', Type.Integer, None, ('Number of scan intervals. '
'Negative value indicate skipping the last point.')],
['integ_time', Type.Float, None, 'Integration time']
]
def prepare(self, m1, s1, f1, m2, s2, f2, m3, s3, f3, m4, s4, f4,
nr_interv, integ_time, **opts):
self._prepare([m1, m2, m3, m4], [s1, s2, s3, s4], [f1, f2, f3, f4],
integ_time, nr_interv, mode=ContinuousHwTimeMode, **opts)
[docs]class meshct(Macro, Hookable):
"""2d grid scan .
The mesh scan traces out a grid using motor1 and motor2.
The first motor scans in contiuous mode from m1_start_pos to m1_final_pos
using the specified number of intervals. The second motor similarly
scans from m2_start_pos to m2_final_pos but it does not move during the
continuous scan. Each point is counted for integ_time seconds
(or monitor counts, if integ_time is negative).
The scan of motor1 is done at each point scanned by motor2. That is, the
first motor scan is nested within the second motor scan.
"""
hints = {"scan": "meshct",
"allowsHooks": ("pre-scan", "pre-configuration",
"post-configuration", "pre-move",
"post-move", "pre-acq", "pre-start",
"post-acq", "pre-cleanup", "post-cleanup",
"post-scan")}
env = ('ActiveMntGrp',)
param_def = [
['motor1', Type.Moveable, None, 'First motor to move'],
['m1_start_pos', Type.Float, None, 'Scan start position for first '
'motor'],
['m1_final_pos', Type.Float, None, 'Scan final position for first '
'motor'],
['m1_nr_interv', Type.Integer, None, ('Number of scan intervals. '
'Negative value indicate skipping the last point.')],
['motor2', Type.Moveable, None, 'Second motor to move'],
['m2_start_pos', Type.Float, None, 'Scan start position for second '
'motor'],
['m2_final_pos', Type.Float, None, 'Scan final position for second '
'motor'],
['m2_nr_interv', Type.Integer, None, 'Number of scan intervals'],
['integ_time', Type.Float, None, 'Integration time'],
['bidirectional', Type.Boolean, False, 'Save time by scanning '
's-shaped'],
['latency_time', Type.Float, 0, 'Latency time']
]
[docs] def prepare(self, m1, m1_start_pos, m1_final_pos, m1_nr_interv,
m2, m2_start_pos, m2_final_pos, m2_nr_interv, integ_time,
bidirectional, latency_time, **opts):
self.motors = [m1, m2]
self.starts = numpy.array([m1_start_pos, m2_start_pos], dtype='d')
self.finals = numpy.array([m1_final_pos, m2_final_pos], dtype='d')
self.nr_intervs = numpy.array([m1_nr_interv, m2_nr_interv], dtype='i')
self.do_last_point = True
# Number of intervals of the first motor which is doing the
# continuous scan.
if m1_nr_interv < 0:
self.do_last_point = False
self.nr_interv = -m1_nr_interv
self.nb_points = self.nr_interv
else:
self.do_last_point = True
self.nr_interv = m1_nr_interv
self.nb_points = self.nr_interv + 1
self.integ_time = integ_time
self.bidirectional_mode = bidirectional
# Prepare the waypoints
m1start, m2start = self.starts
m1end, m2end = self.finals
points1, points2 = self.nr_intervs + 1
m2_space = numpy.linspace(m2start, m2end, points2)
self.waypoints = []
self.starts_points = []
for i, m2pos in enumerate(m2_space):
self.starts_points.append(numpy.array([m1start, m2pos], dtype='d'))
self.waypoints.append(numpy.array([m1end, m2pos], dtype='d'))
if self.bidirectional_mode:
m1start, m1end = m1end, m1start
self.name = opts.get('name', 'meshct')
moveables = []
for m, start, final in zip(self.motors, self.starts, self.finals):
moveables.append(MoveableDesc(moveable=m, min_value=min(
start, final), max_value=max(start, final)))
moveables[0].is_reference = True
env = opts.get('env', {})
mg_name = self.getEnv('ActiveMntGrp')
mg = self.getMeasurementGroup(mg_name)
mg_latency_time = mg.getLatencyTime()
if mg_latency_time > latency_time:
self.info("Choosing measurement group latency time: %f" %
mg_latency_time)
latency_time = mg_latency_time
self.latency_time = latency_time
constrains = [getCallable(cns) for cns in opts.get('constrains',
[UNCONSTRAINED])]
extrainfodesc = opts.get('extrainfodesc', [])
# Hooks are not always set at this point. We will call getHooks
# later on in the scan_loop
# self.pre_scan_hooks = self.getHooks('pre-scan')
# self.post_scan_hooks = self.getHooks('post-scan')
self._gScan = CTScan(self, self._generator, moveables, env, constrains,
extrainfodesc)
# _data is the default member where the Macro class stores the data.
# Assign the date produced by GScan (or its subclasses) to it so all
# the Macro infrastructure related to the data works e.g. getter,
# property, etc.
self.setData(self._gScan.data)
def _generator(self):
moveables_trees = self._gScan.get_moveables_trees()
step = {}
step["pre-move-hooks"] = self.getHooks('pre-move')
post_move_hooks = self.getHooks(
'post-move')
step["post-move-hooks"] = post_move_hooks
step["check_func"] = []
step["active_time"] = self.nb_points * (self.integ_time
+ self.latency_time)
points1, _ = self.nr_intervs + 1
for i, waypoint in enumerate(self.waypoints):
self.point_id = points1 * i
step["waypoint_id"] = i
self.starts = self.starts_points[i]
self.finals = waypoint
step["positions"] = []
step["start_positions"] = []
for start, end, moveable_tree in zip(self.starts, self.finals,
moveables_trees):
moveable_root = moveable_tree.root()
start_positions, end_positions = _calculate_positions(
moveable_root, start, end)
step["start_positions"] += start_positions
step["positions"] += end_positions
yield step
[docs] def run(self, *args):
for step in self._gScan.step_scan():
yield step
def getTimeEstimation(self):
return 0.0
def getIntervalEstimation(self):
return len(self.waypoints)
def _get_nr_points(self):
msg = ("nr_points is deprecated since version 3.0.3. "
"Use nb_points instead.")
self.warning(msg)
return self.nb_points
nr_points = property(_get_nr_points)
@property
def gscan(self):
return self._gScan
[docs]class rscanct(Macro, Hookable):
"""Do an absolute continuous scan of the specified motor.
rscanct scans one motor, as specified as motor. For the first region
the motor starts before the position given by start_pos in order
to reach the constant velocity at the start_pos and finishes
at the position after the next_pos in order to
maintain the constant velocity until the next_pos. Then it will do
the same for other regions but the start position is the next_pos
from the previous region.
Each region may have a different number of intervals, as specified
as region_nb_intervals. The integration time is common to all regions.
"""
hints = {"scan": "rscanct",
"allowsHooks": ("pre-scan", "pre-configuration",
"post-configuration", "pre-move",
"post-move", "pre-acq", "pre-start",
"post-acq", "pre-cleanup", "post-cleanup",
"post-scan")}
param_def = [
["motor", Type.Moveable, None, "Motor to move"],
["start_pos", Type.Float, None, "Start position"],
["regions", [
["next_pos", Type.Float, None, "Next position"],
["region_nb_intervals", Type.Integer, None,
"Region number of intervals"]],
None, "List of tuples: (next_pos, region_nb_intervals"],
["integ_time", Type.Float, None, "Integration time"],
["latency_time", Type.Float, 0, "Latency time"]
]
[docs] def prepare(self, motor, start_pos, ends_intervals_list, int_time,
latency_time, **opts):
self.name = "rscanct"
self.start_pos = start_pos
self.ends_intervals_list = ends_intervals_list
self.integ_time = int_time
self.nr_interv = ends_intervals_list[0][1]
self.nb_points = self.nr_interv + 1
self.waypoints = []
self.starts_points = []
self.intervals_list = []
for end_pos, intervals in self.ends_intervals_list:
self.starts_points.append(self.start_pos)
self.waypoints.append(end_pos)
self.intervals_list.append(intervals)
self.start_pos = end_pos
self.motors = [motor]
moveables = [MoveableDesc(moveable=motor)]
moveables[0].is_reference = True
self.name = opts.get("name", "qExafsc")
env = opts.get("env", {})
mg_name = self.getEnv("ActiveMntGrp")
mg = self.getMeasurementGroup(mg_name)
mg_latency_time = mg.getLatencyTime()
if mg_latency_time > latency_time:
self.info("Choosing measurement group latency time: %f" %
mg_latency_time)
latency_time = mg_latency_time
self.latency_time = latency_time
constrains = [getCallable(cns) for cns in opts.get("constrains",
[UNCONSTRAINED])]
extrainfodesc = opts.get("extrainfodesc", [])
self._gScan = CTScan(self, self._generator, moveables, env, constrains,
extrainfodesc)
self.setData(self._gScan.data)
def _generator(self):
moveables_trees = self._gScan.get_moveables_trees()
step = {}
step["pre-move-hooks"] = self.getHooks("pre-move")
step["post-move-hooks"] = self.getHooks("post-move")
step["pre-acq-hooks"] = self.getHooks("pre-acq")
step["check_func"] = []
nb_waypoints = len(self.waypoints)
for i, waypoint in enumerate(self.waypoints):
self.nr_interv = self.intervals_list[i]
if i < (nb_waypoints - 1):
self.do_last_point = False
self.nb_points = self.nr_interv + 1
else:
self.do_last_point = True
self.nb_points = self.nr_interv + 1
step["active_time"] = self.nb_points * (self.integ_time +
self.latency_time)
step["waypoint_id"] = i
self.starts = numpy.array([self.starts_points[i]])
self.finals = numpy.array([waypoint])
moveable_root = moveables_trees[0].root()
start_positions, end_positions = \
_calculate_positions(
moveable_root,
self.starts[0],
self.finals[0]
)
step["start_positions"] = start_positions
step["positions"] = end_positions
yield step
[docs] def run(self, *args):
try:
for step in self._gScan.step_scan():
yield step
except Exception:
pass
def getTimeEstimation(self):
return 0.0
def getIntervalEstimation(self):
return self.nr_interv
@property
def gscan(self):
return self._gScan
[docs]class timescan(Macro, Hookable):
"""Do a time scan over the specified time intervals. The scan starts
immediately. The number of data points collected will be nr_points.
Count time is given by integ_time. Latency time will be the longer one
of latency_time and measurement group latency time.
"""
hints = {'scan': 'timescan', 'allowsHooks': ('pre-scan', 'pre-acq',
'post-acq', 'post-scan')}
param_def = [
['nb_points', Type.Integer, None, 'Number of scan points'],
['integ_time', Type.Float, None, 'Integration time'],
['latency_time', Type.Float, 0, 'Latency time']]
[docs] def prepare(self, nb_points, integ_time, latency_time):
self.nb_points = nb_points
self.integ_time = integ_time
self.latency_time = latency_time
self._gScan = TScan(self)
# _data is the default member where the Macro class stores the data.
# Assign the date produced by GScan (or its subclasses) to it so all
# the Macro infrastructure related to the data works e.g. getter,
# property, etc.
self.setData(self._gScan.data)
[docs] def run(self, *args):
for step in self._gScan.step_scan():
yield step
def getTimeEstimation(self):
mg_latency_time = self._gScan.measurement_group.getLatencyTime()
latency_time = max(self.latency_time, mg_latency_time)
return self.nb_points * (self.integ_time + latency_time)
def getIntervalEstimation(self):
return 0
def _get_nr_points(self):
msg = ("nr_points is deprecated since version 3.0.3. "
"Use nb_points instead.")
self.warning(msg)
return self.nb_points
nr_points = property(_get_nr_points)
@property
def gscan(self):
return self._gScan
[docs]class scanstats(Macro):
"""Calculate basic statistics of the enabled and plotted channels in
the active measurement group for the last scan. If no channel is selected
for plotting it fallbacks to the first enabled channel. Print stats and
publish them in the env. Also compatible for multi-dimensional scans e.g.
`a2scan`, when statistics are calculated for each motor participating
in the scan if not selected differently with the `motor` parameter.
"""
env = ("ActiveMntGrp", )
param_def = [
["channel",
[["channel", Type.ExpChannel, None, ""], {"min": 0}],
None,
"List of channels for statistics calculations"
],
["motor",
[["motor", Type.Motor, None, ""], {"min": 0}],
None,
"List of motor for statistics calculations"
]
]
[docs] def run(self, channel, motor):
parent = self.getParentMacro()
if not parent:
parent = self.executor.getLastMacro()
if not hasattr(parent, "motors"):
self.warning("last/parent macro must be a scan "
"and must involve at least one moveable "
"to calculate statistics")
return
if not hasattr(parent, "data"):
self.warning("last/parent macro must be a scan "
"and must export some data "
"to calculate statistics")
return
active_meas_grp = self.getEnv("ActiveMntGrp")
meas_grp = self.getMeasurementGroup(active_meas_grp)
calc_channels = []
calc_motors = []
enabled_channels = meas_grp.getEnabled()
scan_motors = [mot.name for mot in parent.motors]
if channel:
stat_channels = [chan.name for chan in channel]
else:
stat_channels = [key for key in enabled_channels.keys()]
if motor:
stat_motors = [mot.name for mot in motor]
else:
stat_motors = scan_motors
for chan in stat_channels:
enabled = enabled_channels.get(chan)
if enabled is None:
self.warning("{} not in {}".format(chan, meas_grp.name))
else:
if not enabled and channel:
self.warning("{} not enabled".format(chan))
elif enabled and channel:
# channel was given as parameters
calc_channels.append(chan)
elif enabled and meas_grp.getPlotType(chan)[chan] == 1:
calc_channels.append(chan)
for mot in stat_motors:
if mot not in scan_motors:
self.warning("{} was not involved in last scan".format(mot))
else:
calc_motors.append(mot)
if len(calc_channels) == 0:
# fallback is first enabled channel in meas_grp
calc_channels.append(next(iter(enabled_channels)))
scalar_channels = []
for _, chan in self.getExpChannels().items():
if chan.type in ("OneDExpChannel", "TwoDExpChannel"):
continue
scalar_channels.append(chan.name)
calc_channels = [ch for ch in calc_channels if ch in scalar_channels]
if len(calc_channels) == 0:
self.warning("measurement group must contain at least one "
"enabled scalar channel to calculate statistics")
return
stats = {}
for motor_name in calc_motors:
stats[motor_name] = {}
motor_data = []
channels_data = {}
for channel_name in calc_channels:
channels_data[channel_name] = []
for idx, rc in parent.data.items():
motor_data.append(rc[motor_name])
for channel_name in calc_channels:
channels_data[channel_name].append(rc[channel_name])
motor_data = numpy.array(motor_data)
for channel_name, data in channels_data.items():
channel_data = numpy.array(data)
(_min, _max, min_at, max_at, half_max, com, mean, _int,
fwhm, cen) = self._calcStats(motor_data, channel_data)
stats[motor_name][channel_name] = {
"min": _min,
"max": _max,
"minpos": min_at,
"maxpos": max_at,
"mean": mean,
"int": _int,
"com": com,
"fwhm": fwhm,
"cen": cen}
col_header = []
cols = []
for channel_name in calc_channels:
col_header.append([channel_name])
temp = [
stats[calc_motors[0]][channel_name]["min"],
stats[calc_motors[0]][channel_name]["max"],
stats[calc_motors[0]][channel_name]["mean"],
stats[calc_motors[0]][channel_name]["int"],
]
for motor_name in calc_motors:
temp.extend([
"",
stats[motor_name][channel_name]["minpos"],
stats[motor_name][channel_name]["maxpos"],
stats[motor_name][channel_name]["com"],
stats[motor_name][channel_name]["fwhm"],
stats[motor_name][channel_name]["cen"],
])
cols.append(temp)
if len(calc_motors) == 1:
self.info("Statistics for movable: {:s}".format(calc_motors[0]))
else:
self.info("Statistics for movables: {:s}".format(
", ".join(calc_motors)))
row_head_str = ["MIN", "MAX", "MEAN", "INT"]
elem_fmt = ["%*g", "%*g", "%*g", "%*g"]
for motor_name in calc_motors:
row_head_str.extend([motor_name,
" MIN@", " MAX@", " COM", " FWHM", " CEN"])
elem_fmt.extend(["%*s", "%*g", "%*g", "%*g", "%*g", "%*g"])
table = Table(elem_list=cols, elem_fmt=elem_fmt,
row_head_str=row_head_str,
col_head_str=col_header, col_head_sep="-")
out = table.genOutput()
for line in out:
self.info(line)
self.setEnv("{:s}.ScanStats".format(self.getDoorName()),
{"Stats": stats,
"Motors": calc_motors,
"ScanID": self.getEnv("ScanID")})
@staticmethod
def _calcStats(x, y):
# max and min
_min = numpy.min(y)
_max = numpy.max(y)
min_idx = numpy.argmin(y)
min_at = x[min_idx]
max_idx = numpy.argmax(y)
max_at = x[max_idx]
# center of mass (com)
try:
com = numpy.sum(y*x)/numpy.sum(y)
except ZeroDivisionError:
com = 0
mean = numpy.mean(y)
_int = numpy.sum(y)
# determine if it is a peak- or erf-like function
half_max = (_max-_min)/2+_min
lower_left = False
lower_right = False
if numpy.any(y[0:max_idx] < half_max):
lower_left = True
if numpy.any(y[max_idx:] < half_max):
lower_right = True
if (lower_left and lower_right) or len(y) == 1:
# it is a peak-like function or data of length 1
y_data = y
elif lower_left:
# it is an erf-like function
# use the gradient for further calculation
y_data = numpy.gradient(y)
# use also the half maximum of the gradient
half_max = (numpy.max(y_data)-numpy.min(y_data)) \
/ 2+numpy.min(y_data)
else:
# it is an erf-like function
# use the gradient for further calculation
y_data = -1*numpy.gradient(y)
# use also the half maximum of the gradient
half_max = (numpy.max(y_data)-numpy.min(y_data)) \
/ 2+numpy.min(y_data)
# cen and fwhm
# this part is adapted from:
#
# The PyMca X-Ray Fluorescence Toolkit
#
# Copyright (c) 2004-2014 European Synchrotron Radiation Facility
#
# This file is part of the PyMca X-ray Fluorescence Toolkit developed
# at the ESRF by the Software group.
max_idx_data = numpy.argmax(y_data)
idx = max_idx_data
try:
while y_data[idx] >= half_max:
idx = idx-1
x0 = x[idx]
x1 = x[idx+1]
y0 = y_data[idx]
y1 = y_data[idx+1]
lhmx = (half_max*(x1-x0) - (y0*x1)+(y1*x0)) / (y1-y0)
except ZeroDivisionError:
lhmx = 0
except IndexError:
lhmx = x[0]
idx = max_idx_data
try:
while y_data[idx] >= half_max:
idx = idx+1
x0 = x[idx-1]
x1 = x[idx]
y0 = y_data[idx-1]
y1 = y_data[idx]
uhmx = (half_max*(x1-x0) - (y0*x1)+(y1*x0)) / (y1-y0)
except ZeroDivisionError:
uhmx = 0
except IndexError:
uhmx = x[-1]
fwhm = uhmx - lhmx
cen = (uhmx + lhmx)/2
return (_min, _max, min_at, max_at, half_max, com, mean, _int,
fwhm, cen)