"""
Contains all the Autoprotocol Instruction objects
:copyright: 2021 by The Autoprotocol Development Team, see AUTHORS
for more details.
:license: BSD, see LICENSE for more details
"""
# pragma pylint: disable=too-few-public-methods, redefined-builtin
from .builders import * # pylint: disable=unused-wildcard-import
from .constants import PROVISION_MEASUREMENT_MODES
from .container import Container
from .informatics import AttachCompounds, Informatics
[docs]class Instruction(object):
"""Base class for an instruction that is to later be encoded as JSON."""
builders = InstructionBuilders()
def __init__(self, op, data, informatics=None):
super(Instruction, self).__init__()
# prevent mutable default value by assigning default value inside the method
if informatics is None:
informatics = []
self.op = op
self.data = self._remove_empty_fields(self._remove_empty_fields(data))
self.__dict__.update(self.data)
self.informatics = self._remove_empty_fields(
self._remove_empty_fields(informatics)
)
if len(self.informatics) > 0:
self._check_informatics()
def __repr__(self):
return f"Instruction({self.op}, {self.data}, {self.informatics})"
def _as_AST(self):
"""generates a Python object representation of Autoprotocol JSON
Returns
-------
dict
a dict of python objects that have the same structure as the
Autoprotocol JSON for the Instruction
Notes
-----
Used downstream for JSON serialization of the Instruction
See Also
--------
:meth:`Protocol._refify` : Protocol serialization method
"""
# informatics is not serialized if empty.
if self.informatics:
return_dict = dict(op=self.op, **self.data, informatics=self.informatics)
else:
return_dict = dict(op=self.op, **self.data)
return return_dict
@staticmethod
def _remove_empty_fields(data):
"""
Helper function to recursively search through and pop items containing
empty dictionaries/lists or dictionaries containing fields with None
values
Parameters
----------
data : dict or list
Data dictionary or list to remove empty fields from
Returns
-------
dict or list
Dictionary or list without fields with None values
"""
# We're not checking against the generic not since there are values
# such as `0` or False which are valid.
def filter_criteria(item):
# Workaround for Unit equality comparison issues
if isinstance(item, Unit):
return False
return item is None or item == [] or item == {}
if isinstance(data, dict):
return {
k: Instruction._remove_empty_fields(v)
for k, v in data.items()
if not filter_criteria(v)
}
if isinstance(data, list):
return [
Instruction._remove_empty_fields(_)
for _ in data
if not filter_criteria(_)
]
return data
def _check_informatics(self):
"""
Validates each Informatics element in informatics.
Raises
------
TypeError
informatics is in a list
TypeError
informatics element is Informatics
"""
if not isinstance(self.informatics, list):
raise TypeError(
f"informatics: {self.informatics} must be provided in a list."
)
for info in self.informatics:
if not isinstance(info, Informatics):
raise TypeError("informatics must be Informatics type.")
if isinstance(info, AttachCompounds):
available_wells = self.get_wells(self.data)
self._check_info_wells(info, available_wells)
@staticmethod
def _check_info_wells(info, available_wells):
"""
validates Informatics wells are included in the wells associated with
the instruction.
Raises
-------
ValueError
'wells' has a value in the informatics data
TypeError
wells are Well, list of Well or WellGroup
ValueError
Informatics wells are part of wells Instruction is operating on
"""
if not info.wells:
raise ValueError(
f"Informatics: {info} must have wells to run this validation."
)
info_wells = info.wells
if not is_valid_well(info_wells):
raise TypeError(
f"wells: {info_wells} must be Well, list of Well or WellGroup."
)
wells = WellGroup(info_wells)
for well in wells.wells:
if well not in available_wells:
raise ValueError(
f"informatics well: {wells} must be one of the wells "
f"used in this instruction."
)
info.wells = wells.wells
# pragma pylint: disable=expression-not-assigned
# pragma pylint: disable=unused-variable
[docs] def get_wells(self, op_data):
"""
Parameters
----------
op_data: dict
Instruction data containing all the operational parameters
Returns
-------
list(Well)
List of all wells associated with the instruction. Note this contains
all source and destination wells for instructions such as `liquid_handle`.
"""
all_wells = []
if isinstance(op_data, dict):
for k, v in op_data.items():
all_wells.append(self.get_wells(v))
elif isinstance(op_data, list):
for i in op_data:
all_wells.extend([self.get_wells(i)])
# if container is provided, all wells in the container are included
elif isinstance(op_data, Container):
all_wells.append(op_data.all_wells())
elif is_valid_well(op_data):
all_wells.append(WellGroup(op_data))
flattened_wells = [item for sublist in all_wells for item in sublist]
# remove any duplicate values in the list
unique_wells = []
[unique_wells.append(x) for x in flattened_wells if x not in unique_wells]
return unique_wells
[docs]class MagneticTransfer(Instruction):
"""
A magnetic_transfer instruction is constructed as a list of lists of
groups, executed in order, where each group is a collect, release, dry,
incubate, or mix sub-operation. These sub-operations control the behavior
of tips which can be magnetized, and a heating platform. Groups in the same
list of groups use the same tips.
Parameters
----------
groups: list(list(dict))
dict in the groups should belong to one of the following categories:
collect:
Collects beads from the specified "object" by raising and lowering
magnetized tips repeatedly with an optional pause at well bottom.
release:
Release beads from unmagnetized tips by oscillating the tips
vertically into and out of the "object".
dry:
Dry beads on magnetized tips above and outside the "object".
incubate:
Incubate the "object".
mix:
Oscillate the tips into and out of the "object"
magnetic_head: str
Head-type used for this instruction
"""
builders = MagneticTransferBuilders()
max_objects = 8
heads = {
"96-deep": ["96-v-kf", "96-deep-kf", "96-deep"],
"96-pcr": ["96-pcr", "96-v-kf", "96-flat", "96-flat-uv"],
}
working_vols: dict = {
"96-v-kf": Unit(200, "microliter"),
"96-deep-kf": Unit(1000, "microliter"),
}
def __init__(self, groups, magnetic_head):
sub_ops = [subgroup for group in groups for subgroup in group]
if not all(len(_) == 1 for _ in sub_ops):
raise ValueError(
f"Not all sub-operations in groups {groups} contain a single "
f"sub-operation."
)
containers = {list(_.values()).pop()["object"] for _ in sub_ops}
valid_container_types = all(
_.container_type.shortname in self.heads[magnetic_head] for _ in containers
)
if not valid_container_types:
raise ValueError(
f"Not all containers: {containers} are in the allowed "
f"container_types: {self.heads[magnetic_head]} for head_type: "
f"{magnetic_head}"
)
wells_with_invalid_volumes = [
w
for container in containers
for w in container.all_wells()
if w.volume
and w.volume > self.working_vols[container.container_type.shortname]
]
if wells_with_invalid_volumes:
non_valid_container_working_vols = [
self.working_vols[w.container.container_type]
for w in wells_with_invalid_volumes
]
raise ValueError(
f"Not all wells: {wells_with_invalid_volumes} have volume "
f"less than MagTransfer working volume for its "
f"container_type: {non_valid_container_working_vols}"
)
# a new tip is used for each group
if len(groups) + len(containers) > self.max_objects:
raise RuntimeError(
f"Only {self.max_objects} total objects can be used within the "
f"same instruction and {len(containers)} "
f"containers: {containers} were specified in addition to "
f"{len(groups)} groups where each group requires a new "
f"tip object."
)
magnetic_transfer = {"groups": groups, "magnetic_head": magnetic_head}
super(MagneticTransfer, self).__init__(
op="magnetic_transfer", data=magnetic_transfer
)
[docs]class Dispense(Instruction):
"""
Dispense specified reagent to specified columns. Only one of reagent,
resource_id, and reagent_source can be specified for a given instruction.
Parameters
----------
object : Container or str
Container for reagent to be dispensed to.
columns : list
Columns to be dispensed to, in the form of a list of dicts specifying
the column number and the volume to be dispensed to that column.
Columns are indexed from 0.
[{"column": <column num>, "volume": <volume>}, ...]
reagent : str, optional
Reagent to be dispensed.
resource_id : str, optional
Resource to be dispensed.
reagent_source : Well, optional
Aliquot to be dispensed from.
step_size : str or Unit, optional
Specifies that the dispense operation must be executed
using a pump that has a dispensing resolution of step_size.
flowrate : str or Unit, optional
The rate at which the peristaltic pump should dispense in Units of
flow rate, e.g. microliter/second.
nozzle_position : dict, optional
A dict represent nozzle offsets from the center of the bottom of the
plate's well. see Dispense.builders.nozzle_position; specified as
{"position_x": Unit, "position_y": Unit, "position_z": Unit}.
pre_dispense : str or Unit, optional
The volume of reagent to be dispensed per-nozzle into waste
immediately prior to dispensing into the ref.
shape: dict, optional
The shape of the dispensing head to be used for the dispense.
See liquid_handle_builders.shape_builder; specified as
{"rows": int, "columns": int, "format": str} with format being a
valid SBS format.
shake_after: dict, optional
Parameters that specify how a plate should be shaken at the very end
of the instruction execution.
{"duration": Unit, "frequency": Unit, "path": str, "amplitude": Unit}
"""
builders = DispenseBuilders()
def __init__(
self,
object,
columns,
reagent=None,
resource_id=None,
reagent_source=None,
step_size=None,
flowrate=None,
nozzle_position=None,
pre_dispense=None,
shape=None,
shake_after=None,
):
disp = {
"object": object,
"columns": columns,
"reagent": reagent,
"resource_id": resource_id,
"reagent_source": reagent_source,
"step_size": step_size,
"pre_dispense": pre_dispense,
"flowrate": flowrate,
"nozzle_position": nozzle_position,
"shape": shape,
"shake_after": shake_after,
}
source_fields = ["reagent", "resource_id", "reagent_source"]
sources = {_: disp[_] for _ in source_fields}
if sum([_ is not None for _ in sources.values()]) != 1:
raise ValueError(
f"Exactly one of `reagent`, `resource_id`, and "
f"`reagent_source` must be specified for Dispense, but got "
f"{sources}."
)
disp = {k: v for k, v in disp.items() if v is not None}
super(Dispense, self).__init__(op="dispense", data=disp)
[docs]class AcousticTransfer(Instruction):
"""
Specify source and destination wells for transferring liquid via an acoustic
liquid handler. Droplet size is usually device-specific.
Parameters
----------
groups : list(dict)
List of `transfer` groups in the form of:
.. code-block:: json
{
"transfer": [
{
"to": "foo/A1",
"from": "bar/A1",
"volume": "1:nl"
}
]
}
droplet_size : str or Unit
Volume representing a droplet_size. The volume of each transfer should
be a multiple of this volume.
"""
def __init__(self, groups, droplet_size):
super(AcousticTransfer, self).__init__(
op="acoustic_transfer",
data={"groups": groups, "droplet_size": droplet_size},
)
[docs]class Spin(Instruction):
"""
Apply the specified amount of acceleration to a plate using a centrifuge.
Parameters
----------
object : Ref or str
Container to be centrifuged.
acceleration : str
Amount of acceleration to be applied to the container, expressed in
units of "g" or "meter/second^2"
duration : str or Unit
Amount of time to apply acceleration.
flow_direction : str
Specifies the direction contents will tend toward with respect to
the container. Valid directions are "inward" and "outward", default
value is "inward".
spin_direction : list(str)
A list of "cw" (clockwise), "cww" (counterclockwise). For each
element in the list, the container will be spun in the stated
direction for the set "acceleration" and "duration". Default values
are derived from the "flow_direction". If "flow_direction" is
"outward", then "spin_direction" defaults to ["cw", "ccw"]. If
"flow_direction" is "inward", then "spin_direction" defaults to
["cw"].
"""
def __init__(
self, object, acceleration, duration, flow_direction=None, spin_direction=None
):
spin_json = {
"object": object,
"acceleration": acceleration,
"duration": duration,
"flow_direction": flow_direction,
"spin_direction": spin_direction,
}
super(Spin, self).__init__(op="spin", data=spin_json)
[docs]class Thermocycle(Instruction):
"""
Append a Thermocycle instruction to the list of instructions, with
groups being a list of dicts in the form of:
.. code-block:: python
"groups": [{
"cycles": integer,
"steps": [{
"duration": duration,
"temperature": temperature,
"read": boolean // optional (default true)
},{
"duration": duration,
"gradient": {
"top": temperature,
"bottom": temperature
},
"read": boolean // optional (default true)
}]
}],
To specify a melting curve, all four melting-relevant parameters must have
a value.
Parameters
----------
object : str or Ref
Container to be thermocycled
groups : list(dict)
List of thermocycling instructions formatted as above
volume : str or Unit, optional
Volume contained in wells being thermocycled
dataref : str, optional
Name of dataref representing read data if performing qPCR
dyes : dict, optional
Dictionary mapping dye types to the wells they're used in
melting : dict
Melting parameters
See Also :meth:`Thermocycle.builders.melting`
lid_temperature: str or Unit
Specifies the lid temperature throughout the duration of the instruction
Raises
------
ValueError
If one of dataref and dyes is specified but the other isn't
ValueError
If melting curve parameters are specified but dyes isn't
"""
builders = ThermocycleBuilders()
def __init__(
self,
object,
groups,
volume="25:microliter",
dataref=None,
dyes=None,
melting=None,
lid_temperature=None,
):
qpcr_params = [dyes, dataref]
if any(qpcr_params) and not all(qpcr_params):
raise ValueError(
f"either dyes {dyes} or dataref {dataref} was specified, but "
f"both are required for qPCR"
)
if melting and any(melting.values()) and not dyes:
raise ValueError(f"melting: {melting} was specified, but dyes was not")
thermocycle = {
"object": object,
"groups": groups,
"volume": volume,
"dataref": dataref,
"dyes": dyes,
"melting": melting,
"lid_temperature": lid_temperature,
}
super(Thermocycle, self).__init__(op="thermocycle", data=thermocycle)
[docs]class Incubate(Instruction):
"""
Store a sample in a specific environment for a given duration. Once the
duration has elapsed, the sample will be returned to the ambient environment
until it is next used in an instruction.
Parameters
----------
object : Ref or str
The container to be incubated
where : Enum({"ambient", "warm_37", "cold_4", "cold_20", "cold_80"})
Temperature at which to incubate specified container
duration : Unit or str
Length of time to incubate container
shaking : bool, optional
Specify whether or not to shake container if available at the specified
temperature
target_temperature : Unit or str, optional
Specify a target temperature for a device (eg. an incubating block)
to reach during the specified duration.
shaking_params: dict, optional
Specify "path" and "frequency" of shaking parameters to be used
with compatible devices (eg. thermoshakes)
co2 : Number, optional
Carbon dioxide percentage
"""
WHERE = [
"ambient",
"warm_30",
"warm_35",
"warm_37",
"cold_4",
"cold_20",
"cold_80",
"cold_196",
]
def __init__(
self,
object,
where,
duration,
shaking=False,
co2=0,
target_temperature=None,
shaking_params=None,
):
if where not in self.WHERE:
raise ValueError(
"Specified `where` not contained in: " f"{', '.join(self.WHERE)}"
)
if where == "ambient" and shaking and not shaking_params:
raise ValueError(
"Shaking is only possible for ambient incubation "
"if 'shaking_params' are specified."
)
incubate_json = {
"object": object,
"where": where,
"duration": duration,
"shaking": shaking,
"co2_percent": co2,
}
if target_temperature:
incubate_json["target_temperature"] = target_temperature
if shaking_params:
incubate_json["shaking_params"] = shaking_params
super(Incubate, self).__init__(op="incubate", data=incubate_json)
[docs]class Agitate(Instruction):
"""
Agitate sample(s) in a container in a specific condition for a given
duration. Once the duration has elapsed, sample(s) will be returned
to specified storage condition until it is used in the next
instruction.
Parameters
----------
object : ref or str
The container to be agitated
mode : Enum(["vortex", "invert", "roll", "stir_bar"])
Specifies the mode of agitation
speed : Unit or str
Speed to agitate container at
duration : Unit or str
Length of time to agitate container
temperature : Unit or str
Temperature to agitate container at
mode_params : dict, optional
Dictionary containing mode params for agitation modes
"""
def __init__(
self, object, mode, speed, duration, temperature=None, mode_params=None
):
agitate_json = {
"object": object,
"mode": mode,
"duration": duration,
"speed": speed,
}
if mode_params:
agitate_json["mode_params"] = mode_params
if temperature:
agitate_json["temperature"] = temperature
super(Agitate, self).__init__(op="agitate", data=agitate_json)
[docs]class IlluminaSeq(Instruction):
"""
Load aliquots into specified lanes for Illumina sequencing.
The specified aliquots should already contain the appropriate mix for
sequencing and require a library concentration reported in
ng/uL.
Parameters
----------
flowcell : str
Flowcell designation: "SR" or " "PE"
lanes : list(dict)
.. code-block:: none
"lanes": [{
"object": aliquot, Well,
"library_concentration": decimal, // ng/uL
},
{...}]
sequencer : str
Sequencer designation: "miseq", "hiseq" or "nextseq"
mode : str
Mode designation: "rapid", "mid" or "high"
index : str
Index designation: "single", "dual" or "none"
library_size: integer
Library size expressed as an integer of basepairs
dataref : str
Name of sequencing dataset that will be returned.
cycles : Enum({"read_1", "read_2", "index_1", "index_2"})
Parameter specific to Illuminaseq read-length or number of
sequenced bases. Refer to the ASC for more details
"""
def __init__(
self, flowcell, lanes, sequencer, mode, index, library_size, dataref, cycles
):
seq = {
"flowcell": flowcell,
"lanes": lanes,
"sequencer": sequencer,
"mode": mode,
"index": index,
"library_size": library_size,
"dataref": dataref,
"cycles": cycles,
}
super(IlluminaSeq, self).__init__(op="illumina_sequence", data=seq)
[docs]class SangerSeq(Instruction):
"""
Send the indicated wells of the container specified for Sanger sequencing.
The specified wells should already contain the appropriate mix for
sequencing, including primers and DNA according to the instructions
provided by the vendor.
Parameters
----------
object : Container or str
Container with well(s) that contain material to be sequenced.
wells : list(str)
Well indices of the container that contain appropriate materials to be
sent for sequencing.
dataref : str
Name of sequencing dataset that will be returned.
type: Enum({"standard", "rca"})
Sanger sequencing type
primer : Container, optional
Tube containing sufficient primer for all RCA reactions. This field
will be ignored if you specify the sequencing type as "standard".
Tube containing sufficient primer for all RCA reactions
"""
def __init__(self, object, wells, dataref, type, primer=None):
seq = {"type": type, "object": object, "wells": wells, "dataref": dataref}
if primer and type == "rca":
seq["primer"] = primer
super(SangerSeq, self).__init__(op="sanger_sequence", data=seq)
[docs]class GelSeparate(Instruction):
"""
Separate nucleic acids on an agarose gel.
Parameters
----------
objects: list or WellGroup or Well
List of wells or WellGroup containing wells to be
separated on gel.
volume : str or Unit
Volume of liquid to be transferred from each well specified to a
lane of the gel.
matrix : str
Matrix (gel) in which to gel separate samples
ladder : str
Ladder by which to measure separated fragment size
duration : str or Unit
Length of time to run current through gel.
dataref : str
Name of this set of gel separation results.
"""
def __init__(self, objects, volume, matrix, ladder, duration, dataref):
super(GelSeparate, self).__init__(
op="gel_separate",
data={
"objects": objects,
"volume": volume,
"matrix": matrix,
"ladder": ladder,
"duration": duration,
"dataref": dataref,
},
)
[docs]class GelPurify(Instruction):
"""
Separate nucleic acids on an agarose gel and purify.
Parameters
----------
objects: list or WellGroup
WellGroup of wells to be purified
volume : str or Unit
Volume of sample required for analysis
dataref : str
Name of this specific dataset of measurements
matrix : str
Agarose concentration and number of wells on gel used for separation
ladder : str
Size range of ladder to be used to compare band size to
dataref : str
Name of dataset containing fragment sizes returned
extract: list(dict)
.. code-block:: none
"extract": [{
"elution_volume": volume,
"elution_buffer": string, "water" | "TE",
"lane": int,
"band_size_range": {
"min_bp": int,
"max_bp": int,
},
"destination": well
},
{...}]
"""
builders = GelPurifyBuilders()
def __init__(self, objects, volume, matrix, ladder, dataref, extract):
super(GelPurify, self).__init__(
op="gel_purify",
data={
"objects": objects,
"volume": volume,
"matrix": matrix,
"ladder": ladder,
"dataref": dataref,
"extract": extract,
},
)
[docs]class Absorbance(Instruction):
"""
Read the absorbance for the indicated wavelength for the indicated
wells. Append an Absorbance instruction to the list of instructions for
this Protocol object.
Parameters
----------
object : str or Ref
Object to execute the absorbance read on
wells : list(Well) or WellGroup
WellGroup of wells to be measured or a list of well references in
the form of ["A1", "B1", "C5", ...]
wavelength : str or Unit
wavelength of light absorbance to be read for the indicated wells
dataref : str
name of this specific dataset of measured absorbances
flashes : int, optional
number of flashes for the read
incubate_before: dict, optional
incubation prior to reading if desired
shaking: dict, optional
shake parameters if desired
amplitude: str or Unit
amplitude of shaking between 1 and 6:millimeter
orbital: bool
True for oribital and False for linear shaking
duration: str, Unit, optional
time prior to plate reading
temperature: str or Unit, optional
set temperature to heat plate reading chamber
settle_time: str or Unit, optional
time to pause before each well read
"""
builders = PlateReaderBuilders()
def __init__(
self,
object,
wells,
wavelength,
dataref,
flashes=25,
incubate_before=None,
temperature=None,
settle_time=None,
):
json_dict = {
"object": object,
"wells": wells,
"wavelength": wavelength,
"num_flashes": flashes,
"dataref": dataref,
"incubate_before": incubate_before,
"temperature": temperature,
"settle_time": settle_time,
}
super(Absorbance, self).__init__(op="absorbance", data=json_dict)
[docs]class Fluorescence(Instruction):
"""
Read the fluorescence for the indicated wavelength for the indicated
wells. Append a Fluorescence instruction to the list of instructions
for this Protocol object.
Parameters
----------
object : str or Container
object to execute the fluorescence read on
wells : list(Well) or WellGroup
WellGroup of wells to be measured or a list of well references in
the form of ["A1", "B1", "C5", ...]
excitation : str or Unit
wavelength of light used to excite the wells indicated
emission : str or Unit
wavelength of light to be measured for the indicated wells
dataref : str
name of this specific dataset of measured absorbances
flashes : int, optional
number of flashes for this read
incubate_before: dict, optional
incubation prior to reading if desired
shaking: dict, optional
shake parameters if desired
amplitude: str or Unit
amplitude of shaking between 1 and 6:millimeter
orbital: bool
True for oribital and False for linear shaking
duration: str, Unit, optional
time prior to plate reading
temperature: str or Unit, optional
set temperature to heat plate reading chamber
gain: float, optional
float between 0 and 1, multiplier of maximum signal amplification
detection_mode: str, optional
set the detection mode of the optics, ["top", "bottom"],
defaults to vendor specified defaults.
position_z: dict, optional
distance from the optics to the surface of the plate transport,
only valid for "top" detection_mode and vendor capabilities.
Specified as either a set distance - "manual", OR calculated from
a WellGroup - "calculated_from_wells". Only one position_z
determination may be specified
.. code-block:: none
position_z = {
"manual": Unit
- OR -
"calculated_from_wells": []
}
manual: str, Unit, optional
parameter available within "position_z" to set the distance from
the optics to the plate transport.
calculated_from_wells: list, WellGroup, Well, optional
parameter available within "position_z" to set the distance from
the optics to the plate transport. If specified, the average
optimal (maximal signal) distance will be chosen from the list
of wells and applied to all measurements.
settle_time: Unit, optional
the time before the start of the measurement, defaults
to vendor specifications
lag_time: Unit, optional
time between flashes and the start of the signal integration,
defaults to vendor specifications
integration_time: Unit, optional
duration of the signal recording, per Well, defaults to vendor
specifications
"""
builders = PlateReaderBuilders()
def __init__(
self,
object,
wells,
excitation,
emission,
dataref,
flashes=25,
incubate_before=None,
temperature=None,
gain=None,
detection_mode=None,
position_z=None,
settle_time=None,
lag_time=None,
integration_time=None,
):
json_dict = {
"object": object,
"wells": wells,
"excitation": excitation,
"emission": emission,
"num_flashes": flashes,
"dataref": dataref,
"incubate_before": incubate_before,
"temperature": temperature,
"gain": gain,
"settle_time": settle_time,
"lag_time": lag_time,
"integration_time": integration_time,
"detection_mode": detection_mode,
"position_z": position_z,
}
super(Fluorescence, self).__init__(op="fluorescence", data=json_dict)
[docs]class Luminescence(Instruction):
"""
Read luminesence of indicated wells
Parameters
----------
object: str or Container
object to execute the luminescence read on
wells : list or WellGroup
WellGroup or list of wells to be measured
dataref : str
name which dataset will be saved under
incubate_before: dict, optional
incubation prior to reading if desired
shaking: dict, optional
shake parameters if desired
amplitude: str or Unit
amplitude of shaking between 1 and 6:millimeter
orbital: bool
True for oribital and False for linear shaking
duration: str, Unit, optional
time prior to plate reading
temperature: str or Unit, optional
set temperature to heat plate reading chamber
settle_time: str or Unit, optional
time to pause before each well read
integration_time: Unit, optional
duration of the signal recording, per Well, defaults to vendor
specifications
"""
builders = PlateReaderBuilders()
def __init__(
self,
object,
wells,
dataref,
incubate_before=None,
temperature=None,
settle_time=None,
integration_time=None,
):
json_dict = {
"object": object,
"wells": wells,
"dataref": dataref,
"incubate_before": incubate_before,
"temperature": temperature,
"settle_time": settle_time,
"integration_time": integration_time,
}
super(Luminescence, self).__init__(op="luminescence", data=json_dict)
[docs]class Seal(Instruction):
"""
Seal indicated container using the automated plate sealer.
Parameters
----------
object : Ref or str
Container to be sealed
type : str, optional
Seal type to be used (optional)
mode : str, optional
Method used to seal plate (optional). "thermal" or "adhesive"
mode_params : dict, optional
Thermal sealing parameters
temperature : str, optional
Temperature to seal plate at
duration : str, optional
Duration for which to apply heated sealing plate onto ref
"""
def __init__(self, object, type="ultra-clear", mode=None, mode_params=None):
seal_dict = {
"object": object,
"type": type,
"mode": mode,
"mode_params": mode_params,
}
super(Seal, self).__init__(op="seal", data=seal_dict)
[docs]class Unseal(Instruction):
"""
Remove seal from indicated container using the automated plate unsealer.
Parameters
----------
object : Ref or str
Container to be unsealed
"""
def __init__(self, object):
super(Unseal, self).__init__(op="unseal", data={"object": object})
[docs]class Cover(Instruction):
"""
Place specified lid type on specified container
Parameters
----------
object : str
Container to be covered
lid : Enum({'standard', 'universal', 'low_evaporation'}), optional
Type of lid to cover container with
retrieve_lid : bool
Flag to retrieve lid from stored location
"""
LIDS = ["standard", "universal", "low_evaporation"]
def __init__(self, object, lid="standard", retrieve_lid=None):
if lid and lid not in self.LIDS:
raise ValueError(f"{lid} is not a valid lid type")
cover = {"object": object, "lid": lid, "retrieve_lid": retrieve_lid}
super(Cover, self).__init__(op="cover", data=cover)
[docs]class Uncover(Instruction):
"""
Remove lid from specified container
Parameters
----------
object : str
Container to remove lid from
store_lid : bool
Flag to store the uncovered lid
"""
def __init__(self, object, store_lid=None):
super(Uncover, self).__init__(
op="uncover", data={"object": object, "store_lid": store_lid}
)
[docs]class FlowCytometry(Instruction):
"""
This instruction provides a non-ambiguous set of parameters for the
performance of flow cytometry.
Parameters
----------
dataref : str
Name of dataset that will be returned.
samples : list(Well) or Well or WellGroup
Wells to be analyzed
lasers : list(dict)
See FlowCytometryBuilders.laser.
collection_conditions : dict
See FlowCytometryBuilders.collection_conditions.
width_threshold : int or float, optional
Threshold to determine width measurement.
window_extension : int or float, optional
Front and rear window extension.
remove_coincident_events : bool, optional
Remove coincident events. Defaults to false.
"""
builders = FlowCytometryBuilders()
def __init__(
self,
dataref,
samples,
lasers,
collection_conditions,
width_threshold=None,
window_extension=None,
remove_coincident_events=None,
):
instruction = {
"dataref": dataref,
"samples": samples,
"lasers": lasers,
"collection_conditions": collection_conditions,
"width_threshold": width_threshold,
"window_extension": window_extension,
"remove_coincident_events": remove_coincident_events,
}
super(FlowCytometry, self).__init__(op="flow_cytometry", data=instruction)
[docs]class FlowAnalyze(Instruction):
"""
Perform flow cytometry.The instruction will be executed within the voltage
range specified for each channel, optimized for the best sample
separation/distribution that can be achieved within these limits. The
vendor will specify the device that this instruction is executed on and
which excitation and emission spectra are available. At least one negative
control is required, which will be used to define data acquisition
parameters as well as to determine any autofluorescent properties for the
sample set. Additional negative positive control samples are optional.
Positive control samples will be used to optimize single color signals and,
if desired, to minimize bleed into other channels.
For each sample this instruction asks you to specify the `volume` and/or
`captured_events`. Vendors might also require `captured_events` in case
their device does not support volumetric sample intake. If both conditions
are supported, the vendor will specify if data will be collected only until
the first one is met or until both conditions are fulfilled.
Example Usage:
Autoprotocol Output:
Parameters
----------
dataref : str
Name of flow analysis dataset generated.
FSC : dict
Dictionary containing FSC channel parameters in the form of:
.. code-block:: none
{
"voltage_range": {
"low": "230:volt",
"high": "280:volt"
},
"area": true, //default: true
"height": true, //default: true
"weight": false //default: false
}
SSC : dict
Dictionary of SSC channel parameters in the form of:
.. code-block:: none
{
"voltage_range": {
"low": <voltage>,
"high": <voltage>"
},
"area": true, //default: true
"height": true, //default: false
"weight": false //default: false
}
negative_controls : list(dict)
List of negative control wells in the form of:
.. code-block:: none
{
"well": well,
"volume": volume,
"captured_events": integer, // optional, default infinity
"channel": [channel_name]
}
at least one negative control is required.
samples : list(dict)
List of samples in the form of:
.. code-block:: none
{
"well": well,
"volume": volume,
"captured_events": integer, // optional, default infinity
}
at least one sample is required
colors : list(dict), optional
Optional list of colors in the form of:
.. code-block:: none
[{
"name": "FitC",
"emission_wavelength": "495:nanometer",
"excitation_wavelength": "519:nanometer",
"voltage_range": {
"low": <voltage>,
"high": <voltage>
},
"area": true, //default: true
"height": false, //default: false
"weight": false //default: false
}]
positive_controls : list(dict), optional
Optional list of positive control wells in the form of:
.. code-block:: none
[{
"well": well,
"volume": volume,
"captured_events": integer, // optional, default infinity
"channel": [channel_name],
"minimize_bleed": [{ // optional
"from": color,
"to": [color]
}]
}]
"""
def __init__(
self,
dataref,
FSC,
SSC,
negative_controls,
samples,
colors=None,
positive_controls=None,
):
flow_instr = {"dataref": dataref, "channels": {}}
flow_instr["channels"]["FSC"] = FSC
flow_instr["channels"]["SSC"] = SSC
flow_instr["negative_controls"] = negative_controls
flow_instr["samples"] = samples
if colors:
flow_instr["channels"]["colors"] = colors
if positive_controls:
flow_instr["positive_controls"] = positive_controls
super(FlowAnalyze, self).__init__(op="flow_analyze", data=flow_instr)
[docs]class Oligosynthesize(Instruction):
"""
Parameters
----------
oligos : list of dicts
List of oligonucleotides to synthesize. Each dictionary should
contain the oligo's sequence, destination, scale and purification
.. code-block:: none
[
{
"destination": "my_plate/A1",
"sequence": "GATCRYMKSWHBVDN",
// - standard IUPAC base codes
// - IDT also allows rX (RNA), mX (2' O-methyl RNA), and
// X*/rX*/mX* (phosphorothioated)
// - they also allow inline annotations for modifications,
// eg "GCGACTC/3Phos/" for a 3' phosphorylation
// eg "aggg/iAzideN/cgcgc" for an internal modification
"scale": "25nm" | "100nm" | "250nm" | "1um",
"purification": "standard" | "page" | "hplc",
// default: standard
},
...
]
"""
def __init__(self, oligos):
super(Oligosynthesize, self).__init__(
op="oligosynthesize", data={"oligos": oligos}
)
[docs]class Autopick(Instruction):
"""
Pick colonies from the agar-containing location(s) specified in `sources`
to the location(s) specified in `dests` in highest to lowest rank order
until there are no more colonies available. If fewer than min_abort
pickable colonies have been identified from the location(s) specified in
`sources`, the run will stop and no further instructions will be executed.
Parameters
----------
groups : list(dict)
Groups of colonies to pick and where to transport them to
criteria : dict
Dictionary of autopicking criteria.
dataref: str
Name of dataset to save the picked colonies to
"""
def __init__(self, groups, criteria, dataref):
pick = {"groups": groups, "dataref": dataref, "criteria": criteria}
super(Autopick, self).__init__(op="autopick", data=pick)
[docs]class ImagePlate(Instruction):
"""
Capture an image of the specified container.
Parameters
----------
object : str
Container to take image of
mode : str
Imaging mode (currently supported: "top")
dataref : str
Name of data reference of resulting image
"""
def __init__(self, object, mode, dataref):
super(ImagePlate, self).__init__(
op="image_plate", data={"object": object, "mode": mode, "dataref": dataref}
)
[docs]class Image(Instruction):
"""
Capture an image of the specified container.
Parameters
----------
ref : Container
Container of which to take image.
mode : str
Angle of image, one of "top", "bottom", "side"
num_images : int
Number of images taken of the container. Defaults to 1.
dataref : str
Name of data reference of resulting image
backlighting : Bool, optional
Whether back-lighting is desired.
magnification : float
Ratio of sizes of the image projected on the camera
sensor compared to the actual size of the object
captured. Defaults to 1.0.
exposure : dict, optional
Parameters to control exposure: "aperture", "iso",
and "shutter_speed".
shutter_speed: Unit, optional
Duration that the imaging sensor is exposed.
iso : Float, optional
Light sensitivity of the imaging sensor.
aperture: Float, optional
Diameter of the lens opening.
"""
def __init__(
self, ref, mode, dataref, num_images, backlighting, exposure, magnification
):
json_dict = {
"object": ref,
"mode": mode,
"dataref": dataref,
"num_images": num_images,
"magnification": magnification,
"back_lighting": backlighting,
"exposure": exposure,
}
super(Image, self).__init__(op="image", data=json_dict)
[docs]class Provision(Instruction):
"""
Provision a commercial resource from a catalog into the specified
destination well(s). A new tip is used for each destination well
specified to avoid contamination.
Parameters
----------
resource_id : str
Resource ID from catalog.
dests : list(dict)
Destination(s) for specified resource, together with volume information
measurement_mode : str
Measurement mode. Possible values are
:py:class:`autoprotocol.constants.MEASUREMENT_MODES`
informatics : list(Informatics)
List of expected aliquot effects at the completion of this instruction
Raises
------
TypeError
If resource_id is not a string.
RuntimeError
If length of the list of volumes specified does not match the number of
destination wells specified.
RuntimeError
If the measurement mode is not supported.
TypeError
If volume is not specified as a string or Unit (or a list of either)
"""
def __init__(self, resource_id, dests, measurement_mode="volume", informatics=None):
if measurement_mode not in PROVISION_MEASUREMENT_MODES:
raise RuntimeError(
f"{measurement_mode} is not a valid measurement mode for provisioning"
)
super(Provision, self).__init__(
op="provision",
data={
"resource_id": resource_id,
"measurement_mode": measurement_mode,
"to": dests,
},
informatics=informatics,
)
[docs]class FlashFreeze(Instruction):
"""
Flash freeze the contents of the specified container by submerging it in
liquid nitrogen for the specified amount of time.
Parameters
----------
object : Container or str
Container to be flash frozen.
duration : str or Unit
Duration to submerge specified container in liquid nitrogen.
"""
def __init__(self, object, duration):
super(FlashFreeze, self).__init__(
op="flash_freeze", data={"object": object, "duration": duration}
)
[docs]class Evaporate(Instruction):
"""
Removes liquid or moisture from sample.
Parameters
----------
ref : Container
Sample container
mode : str
Mode of evaporation
duration : Unit or str
Duration object is processed
evaporator_temperature : Unit or str
Temperature object is exposed to
mode_params : dict
Dictionary of parameters for evaporation mode
"""
builders = EvaporateBuilders()
def __init__(self, ref, mode, duration, evaporator_temperature, mode_params):
json_dict = {
"object": ref,
"mode": mode,
"duration": duration,
"evaporator_temperature": evaporator_temperature,
"mode_params": mode_params,
}
super(Evaporate, self).__init__(op="evaporate", data=json_dict)
[docs]class SPE(Instruction):
"""
Apply a solid phase extraction (spe) technique to a sample.
Parameters
----------
well : Well
Well to solid phase extract.
cartridge : str
Cartridge to use for solid phase extraction.
pressure_mode : str
The direction of pressure applied to the cartridge to force
liquid flow. One of "positive", "negative".
load_sample: dict
Parameters for applying the sample to the cartridge.
Single 'mobile_phase_param'.
elute: list(dict)
Parameters for applying a mobile phase to the cartridge
with one or more solvents. List of 'mobile_phase_params'.
Requires `destination_well`.
condition: list(dict), optional
Parameters for applying a mobile phase to the cartridge
with one or more solvents. List of 'mobile_phase_params'.
equilibrate: list(dict), optional
Parameters for applying a mobile phase to the cartridge
with one or more solvents. List of 'mobile_phase_params'.
rinse: list(dict), optional
Parameters for applying a mobile phase to the cartridge
with one or more solvents. List of 'mobile_phase_params'.
mobile_phase_params:
resource_id: str
Resource ID of desired solvent.
volume: volume
Volume added to the cartridge.
loading_flowrate: Unit
Speed at which volume is added to cartridge.
settle_time: Unit
Duration for which the solvent remains on the cartridge
before a pressure mode is applied.
processing_time: Unit
Duration for which pressure is applied to the cartridge
after `settle_time` has elapsed.
flow_pressure: Unit
Pressure applied to the column.
destination_well: Well
Destination well for eluate. Required parameter for
each `elute` mobile phase parameter
"""
builders = SPEBuilders()
def __init__(
self,
well,
cartridge,
pressure_mode,
load_sample,
elute,
condition,
equilibrate,
rinse,
):
json_dict = {
"object": well,
"cartridge": cartridge,
"pressure_mode": pressure_mode,
"condition": condition,
"load_sample": load_sample,
"rinse": rinse,
"elute": elute,
"equilibrate": equilibrate,
}
super(SPE, self).__init__(op="spe", data=json_dict)
[docs]class MeasureConcentration(Instruction):
"""
Measure the concentration of DNA, ssDNA, RNA or Protein in the specified
volume of the source aliquots.
Parameters
----------
object : list or WellGroup
WellGroup of wells to be measured
volume : str or Unit
Volume of sample required for analysis
dataref : str
Name of this specific dataset of measurements
measurement : str
Class of material to be measured. One of ["DNA", "ssDNA", "RNA",
"protein"].
"""
def __init__(self, object, volume, dataref, measurement):
json_dict = {
"object": object,
"volume": volume,
"dataref": dataref,
"measurement": measurement,
}
super(MeasureConcentration, self).__init__(
op="measure_concentration", data=json_dict
)
[docs]class Sonicate(Instruction):
"""
Sonicate wells using high intensity ultrasonic vibrations.
Parameters
----------
wells : Well or WellGroup or list(Well)
Wells to be sonicated
duration : Unit or str
Duration for which to sonicate wells
mode: Enum({"bath", "horn"})
Sonicating method to be used, must be "horn" or "bath". Sonicate
mode "horn" uses metal probe to create a localized shear force
directly in the sample media; "bath" mode applies ultrasound to
wells held inside a bath.
temperature: Unit or str, optional
Temperature at which the sample is kept during sonication. Optional,
defaults to ambient
frequency: Unit or str, optional
Frequency of the ultrasonic wave, usually indicated in kHz.
Optional; defaults to the most commonly used frequency for each
mode: 20 kHz for `horn`, and 40 kHz for `bath` mode
mode_params: Dict
Dictionary containing mode parameters for the specified mode.
"""
def __init__(self, wells, duration, mode, mode_params, frequency, temperature):
json_dict = {
"wells": wells,
"duration": duration,
"frequency": frequency,
"mode": mode,
"mode_params": mode_params,
}
if temperature:
json_dict["temperature"] = temperature
super(Sonicate, self).__init__(op="sonicate", data=json_dict)
[docs]class MeasureMass(Instruction):
"""
Measure the mass of containers
Parameters
----------
object : Container
Container ref
dataref: str
Name of the data for the measurement
"""
def __init__(self, object, dataref):
json_dict = {"object": object, "dataref": dataref}
super(MeasureMass, self).__init__(op="measure_mass", data=json_dict)
[docs]class MeasureVolume(Instruction):
"""
Measure the mass of containers
Parameters
----------
object: list(Container)
list of containers
dataref: str
Name of the data for the measurement
"""
def __init__(self, object, dataref):
json_dict = {"object": object, "dataref": dataref}
super(MeasureVolume, self).__init__(op="measure_volume", data=json_dict)
[docs]class CountCells(Instruction):
"""
Count the number of cells in a sample that are positive/negative
for a given set of labels.
Parameters
----------
wells: WellGroup
List of wells that will be used for cell counting.
volume: Unit
Volume that should be consumed from each well for the purpose
of cell counting.
dataref: str
Name of dataset that will be returned.
labels: list(string), optional
Cells will be scored for presence or absence of each label
in this list. If staining is required to visualize these labels,
they must be added before execution of this instruction.
"""
def __init__(self, wells, volume, dataref, labels=None):
json_dict = {
"wells": wells,
"volume": volume,
"dataref": dataref,
"labels": labels,
}
super(CountCells, self).__init__(op="count_cells", data=json_dict)
[docs]class Spectrophotometry(Instruction):
"""
Execute a Spectrophotometry plate read on the obj.
Parameters
----------
dataref : str
Name of the resultant dataset to be returned.
object : Container or str
Container to be read.
groups : list
A list of groups generated by SpectrophotometryBuilders groups builders,
any of absorbance_mode_params, fluorescence_mode_params,
luminescence_mode_params, or shake_mode_params.
interval : Unit or str, optional
The time between each of the read intervals.
num_intervals : int, optional
The number of times that the groups should be executed.
temperature : Unit or str, optional
The temperature that the entire instruction should be executed at.
shake_before : dict, optional
A dict of params generated by SpectrophotometryBuilders.shake_before
that dictates how the obj should be incubated with shaking before any of
the groups are executed.
"""
builders = SpectrophotometryBuilders()
def __init__(
self,
dataref,
object,
groups,
interval=None,
num_intervals=None,
temperature=None,
shake_before=None,
):
spec = {
"dataref": dataref,
"object": object,
"groups": groups,
"interval": interval,
"num_intervals": num_intervals,
"temperature": temperature,
"shake_before": shake_before,
}
super(Spectrophotometry, self).__init__(op="spectrophotometry", data=spec)
[docs]class LiquidHandle(Instruction):
"""Manipulates liquids within locations
A liquid handle instruction is constructed as a list of locations, where
each location consists of the well location and the tip transports carried
out within the well.
Each liquid handle instruction corresponds to a single tip or set of tips.
Parameters
----------
locations : list(dict)
See Also :meth:`LiquidHandle.builders.location`
shape : dict, optional
See Also :meth:`LiquidHandle.builders.shape`
mode : str, optional
the liquid handling mode
mode_params : dict, optional
See Also :meth:`LiquidHandle.builders.instruction_mode_params`
informatics : list(Informatics), optional
List of Informatics describing the intended aliquot effects upon
completion of this instruction.
"""
builders = LiquidHandleBuilders()
def __init__(
self, locations, shape=None, mode=None, mode_params=None, informatics=None
):
data = {
"locations": locations,
"shape": shape,
"mode": mode,
"mode_params": mode_params,
}
super(LiquidHandle, self).__init__(
op="liquid_handle", data=data, informatics=informatics
)