update imports

pcore/baby_client.py

@@ -20,7 +20,7 @@ from baby.crawler import BabyCrawler
 from requests.exceptions import Timeout, HTTPError
 from requests_toolbelt.multipart.encoder import MultipartEncoder
 
-from core.utils import Cache, accumulate, get_store_path
+from pcore.utils import Cache, accumulate, get_store_path
 
 ################### Dask Methods ################################
 def format_segmentation(segmentation, tp):

pcore/haystack.py

@@ -4,7 +4,7 @@ from pathlib import Path
 
 import tensorflow as tf
 
-from core.io.writer import DynamicWriter
+from pcore.io.writer import DynamicWriter
 
 
 def initialise_tf(version):
@@ -16,29 +16,29 @@ def initialise_tf(version):
         return session
     # TODO this only works for TF2
     if version == 2:
-        gpus = tf.config.experimental.list_physical_devices('GPU')
+        gpus = tf.config.experimental.list_physical_devices("GPU")
         if gpus:
             for gpu in gpus:
                 tf.config.experimental.set_memory_growth(gpu, True)
-            logical_gpus = tf.config.experimental.list_logical_devices('GPU')
-            print(len(gpus), "Physical GPUs,", len(logical_gpus),
-                  "Logical GPUs")
+            logical_gpus = tf.config.experimental.list_logical_devices("GPU")
+            print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
         return None
 
 
 def timer(func, *args, **kwargs):
     start = perf_counter()
     result = func(*args, **kwargs)
-    print(f'Function {func.__name__}: {perf_counter() - start}s')
+    print(f"Function {func.__name__}: {perf_counter() - start}s")
     return result
 
 
 ################## CUSTOM OBJECTS ##################################
 
+
 class ModelPredictor:
     """Generic object that takes a NN and returns the prediction.
-    
-    Use for predicting fluorescence/other from bright field. 
+
+    Use for predicting fluorescence/other from bright field.
     This does not do instance segmentations of anything.
     """
 
@@ -49,15 +49,13 @@ class ModelPredictor:
 
     def get_data(self, tp):
         # Change axes to X,Y,Z rather than Z,Y,X
-        return self.tiler.get_tp_data(tp, self.bf_channel).swapaxes(1,
-                                                                    3).swapaxes(
-            1, 2)
+        return self.tiler.get_tp_data(tp, self.bf_channel).swapaxes(1, 3).swapaxes(1, 2)
 
     def format_result(self, result, tp):
-        return {self.name: result, 'timepoints': [tp] * len(result)}
+        return {self.name: result, "timepoints": [tp] * len(result)}
 
     def run_tp(self, tp, **kwargs):
-        """ Simulating processing time with sleep"""
+        """Simulating processing time with sleep"""
         # Access the image
         segmentation = self.model.predict(self.get_data(tp))
         return self._format_result(segmentation, tp)
@@ -66,13 +64,12 @@ class ModelPredictor:
 class ModelPredictorWriter(DynamicWriter):
     def __init__(self, file, name, shape, dtype):
         super.__init__(file)
-        self.datatypes = {name: (shape, dtype),
-                          'timepoint': ((None,), np.uint16)}
-        self.group = f'{self.name}_info'
+        self.datatypes = {name: (shape, dtype), "timepoint": ((None,), np.uint16)}
+        self.group = f"{self.name}_info"
 
 
 class Saver:
-    channel_names = {0: 'BrightField', 1: 'GFP'}
+    channel_names = {0: "BrightField", 1: "GFP"}
 
     def __init__(self, tiler, save_directory, pos_name):
         """This class straight up saves the trap data for use with neural networks in the future."""
@@ -96,5 +93,5 @@ class Saver:
             ch_dir = self.channel_dir(ch)
             data = self.get_data(tp, ch)
             for tid, trap in enumerate(data):
-                np.save(ch_dir / f'{self.name}_{tid}_{tp}.npy', trap)
+                np.save(ch_dir / f"{self.name}_{tid}_{tp}.npy", trap)
         return

pcore/io/signal.py

@@ -8,7 +8,7 @@ import h5py
 import pandas as pd
 from utils_find_1st import find_1st, cmp_larger
 
-from core.io.base import BridgeH5
+from pcore.io.base import BridgeH5
 
 
 class Signal(BridgeH5):

scripts/distributed_alan.py

@@ -3,13 +3,10 @@ import json
 from time import perf_counter
 import logging
 
-from core.experiment import MetaData
 from pathos.multiprocessing import Pool
 from multiprocessing import set_start_method
 import numpy as np
 
-from extraction.core.functions.defaults import exparams_from_meta
-from core.io.signal import Signal
 
 # set_start_method("spawn")
 
@@ -21,13 +18,16 @@ import operator
 
 from baby.brain import BabyBrain
 
-from core.io.omero import Dataset, Image
-from core.haystack import initialise_tf
-from core.baby_client import DummyRunner
-from core.segment import Tiler
-from core.io.writer import TilerWriter, BabyWriter
-from core.utils import timed
+from pcore.experiment import MetaData
+from pcore.io.omero import Dataset, Image
+from pcore.haystack import initialise_tf
+from pcore.baby_client import DummyRunner
+from pcore.segment import Tiler
+from pcore.io.writer import TilerWriter, BabyWriter
+from pcore.utils import timed
 
+from pcore.io.signal import Signal
+from extraction.core.functions.defaults import exparams_from_meta
 from extraction.core.extractor import Extractor
 from extraction.core.parameters import Parameters
 from extraction.core.functions.defaults import get_params
@@ -278,9 +278,9 @@ def visualise_timing(timings: dict, save_file: str):
 strain = ""
 # exp = 18616
 # exp = 19232
-# exp = 19995
+exp = 19995
 # exp = 19993
-exp = 20191
+# exp = 20191
 # exp = 19831
 
 with Dataset(exp) as conn:
@@ -294,17 +294,17 @@ tps = int(meta["size_t"])
 config = dict(
     general=dict(
         id=exp,
-        distributed=4,
+        distributed=5,
         tps=tps,
         directory="../data/",
         strain=strain,
-        tile_size=117,
+        tile_size=96,
     ),
     # general=dict(id=19303, distributed=0, tps=tps, strain=strain, directory="../data/"),
     tiler=dict(),
     baby=dict(tf_version=2),
     earlystop=dict(
-        min_tp=200,
+        min_tp=300,
         thresh_pos_clogged=0.3,
         thresh_trap_clogged=7,
         ntps_to_eval=5,