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"""Dranspose is a WFM used at MAX-IV: https://github.com/felix-engelmann/dranspose
It runs map-reduce style workflows. This script is an example of the Ewoks equivalent.
"""
import os
import time
import random
import numpy
from silx.io import h5py_utils
from ewoksppf import execute_graph
from ewokscore.task import Task
class GenerateData(
Task,
input_names=["nblocks", "block_size"],
optional_input_names=["block_index"],
output_names=["image_stack", "block_index", "finished"],
):
def run(self):
t0 = time.perf_counter()
block_index = self.get_input_value("block_index", 0)
block_size = self.inputs.block_size
start_value = block_index * block_size
values = numpy.arange(start_value, start_value + block_size, dtype=numpy.int32)
image_stack = numpy.broadcast_to(
values.reshape(block_size, 1, 1), (block_size, 4096, 4096)
)
self.outputs.image_stack = image_stack.copy()
self.outputs.block_index = block_index + 1
self.outputs.finished = self.outputs.block_index >= self.inputs.nblocks
t1 = time.perf_counter()
print(f"{block_size/(t1-t0)} images/sec")
class IntegrateData(
Task,
input_names=["image_stack", "block_index"],
optional_input_names=["axis", "delay"],
output_names=["pattern_stack", "block_index"],
):
def run(self):
image_axis = self.get_input_value("axis", 0)
self.outputs.pattern_stack = self.inputs.image_stack.sum(axis=image_axis + 1)
self.outputs.block_index = self.inputs.block_index
delay = self.get_input_value("delay", 0)
if delay:
time.sleep(random.uniform(delay, delay * 1.5))
else:
time.sleep(random.uniform(0, 0.1))
class SaveData(
Task,
input_names=["data_stack", "block_index", "filename"],
output_names=["hdf5_url"],
):
def run(self):
filename = os.path.abspath(self.inputs.filename)
block_index = self.inputs.block_index
data_stack = self.inputs.data_stack
block_size = len(data_stack)
start_index = (block_index - 1) * block_size
stop_index = start_index + block_size
with h5py_utils.open_item(filename, "/", mode="a") as f:
ndim0_required = stop_index
# Make sure dataset exists and is large enough
if "data" in f:
dset = f["data"]
if len(dset) < ndim0_required:
dset.resize(ndim0_required, axis=0)
else:
data_shape = data_stack.shape[1:]
dset = f.create_dataset(
"data",
shape=(ndim0_required, *data_shape),
maxshape=(None, *data_shape),
dtype=data_stack.dtype,
)
dset[start_index:stop_index, ...] = data_stack
self.outputs.hdf5_url = (
f"silx://{filename}?path=/data&slice={start_index},{stop_index}"
)
print(f"Saved {self.outputs.hdf5_url}")
if __name__ == "__main__":
import logging
logging.basicConfig(level=logging.INFO)
logging.getLogger("pypushflow").setLevel(logging.WARNING)
nodes = [
{
"id": "generate",
"task_type": "class",
"task_identifier": "__main__.GenerateData",
},
{
"id": "integrate",
"task_type": "class",
"task_identifier": "__main__.IntegrateData",
},
{
"id": "save",
"task_type": "class",
"task_identifier": "__main__.SaveData",
},
]
links = [
{
"source": "generate",
"target": "generate",
"data_mapping": [
{"source_output": "block_index", "target_input": "block_index"}
],
"conditions": [{"source_output": "finished", "value": False}],
},
{
"source": "generate",
"target": "integrate",
"data_mapping": [
{"source_output": "image_stack", "target_input": "image_stack"},
{"source_output": "block_index", "target_input": "block_index"},
],
},
{
"source": "integrate",
"target": "save",
"data_mapping": [
{"source_output": "pattern_stack", "target_input": "data_stack"},
{"source_output": "block_index", "target_input": "block_index"},
],
},
]
workflow = {"graph": {"id": "test"}, "nodes": nodes, "links": links}
inputs = [
{"id": "generate", "name": "nblocks", "value": 10},
{"id": "generate", "name": "block_size", "value": 3},
{"id": "integrate", "name": "axis", "value": 1},
{
"id": "integrate",
"name": "delay",
"value": 0,
}, # add fake time to integration
{"id": "save", "name": "filename", "value": "result.h5"},
]
if os.path.exists("result.h5"):
os.unlink("result.h5")
result = execute_graph(
workflow,
inputs=inputs,
pool_type="process", # thread, process, gevent
scaling_workers=False,
max_workers=16,
raise_error=True,
)
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