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moved calculation of force_send_visualisation out of work function

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augustin_s
2024-08-08 15:40:32 +02:00
parent f77f109ed2
commit 779d1e1d77
3 changed files with 45 additions and 32 deletions
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1
dap/algos/__init__.py
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@ -1,6 +1,7 @@
from .addmask import calc_apply_additional_mask from .addmask import calc_apply_additional_mask
from .jfdata import JFData from .jfdata import JFData
from .forcesend import calc_force_send
from .mask import calc_mask_pixels from .mask import calc_mask_pixels
from .peakfind import calc_peakfinder_analysis from .peakfind import calc_peakfinder_analysis
from .radprof import calc_radial_integration from .radprof import calc_radial_integration

40
dap/algos/forcesend.py Normal file
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@ -0,0 +1,40 @@
import numpy as np
def calc_force_send(results, data, pixel_mask_pf, image, n_aggregated_images, data_summed):
force_send_visualisation = False
if data.dtype != np.uint16:
apply_threshold = results.get("apply_threshold", False)
apply_aggregation = results.get("apply_aggregation", False)
if not apply_aggregation:
data_summed = None
n_aggregated_images = 1
if apply_threshold or apply_aggregation:
if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
threshold_min = float(results["threshold_min"])
threshold_max = float(results["threshold_max"])
data[data < threshold_min] = 0.0
if threshold_max > threshold_min:
data[data > threshold_max] = 0.0
if apply_aggregation and "aggregation_max" in results:
if data_summed is not None:
data += data_summed
n_aggregated_images += 1
data_summed = data.copy()
data_summed[data == -np.nan] = -np.nan #TODO: this does nothing
results["aggregated_images"] = n_aggregated_images
results["worker"] = 1 #TODO: keep this for backwards compatibility?
if n_aggregated_images >= results["aggregation_max"]:
force_send_visualisation = True
data_summed = None
n_aggregated_images = 1
if pixel_mask_pf is not None:
data[~pixel_mask_pf] = np.nan
else:
data = image
return data, force_send_visualisation, n_aggregated_images, data_summed

36
dap/worker.py
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@ -3,7 +3,7 @@ from random import randint
import numpy as np import numpy as np
from algos import calc_apply_threshold, calc_mask_pixels, calc_peakfinder_analysis, calc_radial_integration, calc_roi, calc_spi_analysis, JFData from algos import calc_apply_threshold, calc_force_send, calc_mask_pixels, calc_peakfinder_analysis, calc_radial_integration, calc_roi, calc_spi_analysis, JFData
from utils import BufferedJSON, read_bit from utils import BufferedJSON, read_bit
from zmqsocks import ZMQSockets from zmqsocks import ZMQSockets
@ -118,37 +118,7 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
calc_peakfinder_analysis(results, pfdata, pixel_mask_pf) calc_peakfinder_analysis(results, pfdata, pixel_mask_pf)
# ??? # ???
force_send_visualisation = False data, force_send_visualisation, n_aggregated_images, data_summed = calc_force_send(results, data, pixel_mask_pf, image, n_aggregated_images, data_summed)
if data.dtype != np.uint16:
apply_threshold = results.get("apply_threshold", False)
apply_aggregation = results.get("apply_aggregation", False)
if not apply_aggregation:
data_summed = None
n_aggregated_images = 1
if apply_threshold or apply_aggregation:
if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
threshold_min = float(results["threshold_min"])
threshold_max = float(results["threshold_max"])
data[data < threshold_min] = 0.0
if threshold_max > threshold_min:
data[data > threshold_max] = 0.0
if apply_aggregation and "aggregation_max" in results:
if data_summed is not None:
data += data_summed
n_aggregated_images += 1
data_summed = data.copy()
data_summed[data == -np.nan] = -np.nan #TODO: this does nothing
results["aggregated_images"] = n_aggregated_images
results["worker"] = 1 #TODO: keep this for backwards compatibility?
if n_aggregated_images >= results["aggregation_max"]:
force_send_visualisation = True
data_summed = None
n_aggregated_images = 1
if pixel_mask_pf is not None:
data[~pixel_mask_pf] = np.nan
else:
data = image
results["type"] = str(data.dtype) results["type"] = str(data.dtype)
results["shape"] = data.shape results["shape"] = data.shape
@ -157,6 +127,8 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
zmq_socks.send_accumulator(results) zmq_socks.send_accumulator(results)
apply_aggregation = results.get("apply_aggregation", False)
send_empty_cond1 = (apply_aggregation and "aggregation_max" in results and not force_send_visualisation) send_empty_cond1 = (apply_aggregation and "aggregation_max" in results and not force_send_visualisation)
send_empty_cond2 = (not results["is_good_frame"] or not (results["is_hit_frame"] or randint(1, skip_frames_rate) == 1)) send_empty_cond2 = (not results["is_good_frame"] or not (results["is_hit_frame"] or randint(1, skip_frames_rate) == 1))