From 26a1e4522dd18372690a7913f0c2df4a688a3245 Mon Sep 17 00:00:00 2001
From: Timothe Jost <timothe.jost@wanadoo.fr>
Date: Tue, 10 Oct 2023 01:39:02 +0200
Subject: [PATCH] almost working base feature set
---
.coverage | Bin 53248 -> 53248 bytes
pypelines.egg-info/PKG-INFO | 18 +
pypelines.egg-info/SOURCES.txt | 19 +
pypelines.egg-info/dependency_links.txt | 1 +
pypelines.egg-info/top_level.txt | 2 +
pypelines/__init__.py | 1 +
.../__pycache__/__init__.cpython-311.pyc | Bin 332 -> 358 bytes
pypelines/__pycache__/__init__.cpython-39.pyc | Bin 0 -> 273 bytes
pypelines/__pycache__/disk.cpython-311.pyc | Bin 0 -> 14332 bytes
.../__pycache__/examples.cpython-311.pyc | Bin 1512 -> 1575 bytes
pypelines/__pycache__/loggs.cpython-39.pyc | Bin 0 -> 2328 bytes
.../__pycache__/multisession.cpython-39.pyc | Bin 0 -> 482 bytes
pypelines/__pycache__/pipe.cpython-311.pyc | Bin 6924 -> 5753 bytes
pypelines/__pycache__/pipe.cpython-39.pyc | Bin 0 -> 4904 bytes
.../__pycache__/pipeline.cpython-311.pyc | Bin 3274 -> 4843 bytes
pypelines/__pycache__/sessions.cpython-39.pyc | Bin 0 -> 3653 bytes
pypelines/__pycache__/step.cpython-311.pyc | Bin 9923 -> 10915 bytes
pypelines/__pycache__/step.cpython-39.pyc | Bin 0 -> 6030 bytes
pypelines/disk.py | 156 ++-
pypelines/examples.py | 2 +-
pypelines/feature_test.ipynb | 1000 +++++++++++++++++
pypelines/pipe.py | 54 +-
pypelines/pipeline.py | 62 +-
pypelines/step.py | 107 +-
scripts/self_install_local_editmode.cmd | 4 +
setup.py | 8 +-
tests/__pycache__/tests.cpython-311.pyc | Bin 2021 -> 2021 bytes
27 files changed, 1331 insertions(+), 103 deletions(-)
create mode 100644 pypelines.egg-info/PKG-INFO
create mode 100644 pypelines.egg-info/SOURCES.txt
create mode 100644 pypelines.egg-info/dependency_links.txt
create mode 100644 pypelines.egg-info/top_level.txt
create mode 100644 pypelines/__pycache__/__init__.cpython-39.pyc
create mode 100644 pypelines/__pycache__/disk.cpython-311.pyc
create mode 100644 pypelines/__pycache__/loggs.cpython-39.pyc
create mode 100644 pypelines/__pycache__/multisession.cpython-39.pyc
create mode 100644 pypelines/__pycache__/pipe.cpython-39.pyc
create mode 100644 pypelines/__pycache__/sessions.cpython-39.pyc
create mode 100644 pypelines/__pycache__/step.cpython-39.pyc
create mode 100644 pypelines/feature_test.ipynb
create mode 100644 scripts/self_install_local_editmode.cmd
diff --git a/.coverage b/.coverage
index 7f4cc21f0600cbe15bb35deaaf856e3f4f26e781..437d2d4b5c9f6b39ff6542e53809bbf383d04dae 100644
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diff --git a/pypelines.egg-info/PKG-INFO b/pypelines.egg-info/PKG-INFO
new file mode 100644
index 0000000..b27b9d9
--- /dev/null
+++ b/pypelines.egg-info/PKG-INFO
@@ -0,0 +1,18 @@
+Metadata-Version: 2.1
+Name: pypelines
+Version: 0.0.1
+Summary: Framework to organize processing code outputs to/from disk, processing chaining and versionning with a common easy to use api
+Home-page: https://gitlab.pasteur.fr/haisslab/data-management/pypelines
+Author: Timothé Jost-MOUSSEAU
+Author-email: timothe.jost-mousseau@pasteur.com
+License: MIT
+Classifier: Development Status :: 3 - Alpha
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Programming Language :: Python :: 3.8
+Classifier: Programming Language :: Python :: 3.9
+Classifier: Programming Language :: Python :: 3.10
+Classifier: Programming Language :: Python :: 3.11
diff --git a/pypelines.egg-info/SOURCES.txt b/pypelines.egg-info/SOURCES.txt
new file mode 100644
index 0000000..d9bcaf8
--- /dev/null
+++ b/pypelines.egg-info/SOURCES.txt
@@ -0,0 +1,19 @@
+README.md
+setup.py
+pypelines/__init__.py
+pypelines/disk.py
+pypelines/examples.py
+pypelines/loggs.py
+pypelines/multisession.py
+pypelines/pickle_backend.py
+pypelines/pipe.py
+pypelines/pipeline.py
+pypelines/sessions.py
+pypelines/step.py
+pypelines/versions.py
+pypelines.egg-info/PKG-INFO
+pypelines.egg-info/SOURCES.txt
+pypelines.egg-info/dependency_links.txt
+pypelines.egg-info/top_level.txt
+tests/__init__.py
+tests/tests.py
\ No newline at end of file
diff --git a/pypelines.egg-info/dependency_links.txt b/pypelines.egg-info/dependency_links.txt
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/pypelines.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/pypelines.egg-info/top_level.txt b/pypelines.egg-info/top_level.txt
new file mode 100644
index 0000000..35c4292
--- /dev/null
+++ b/pypelines.egg-info/top_level.txt
@@ -0,0 +1,2 @@
+pypelines
+tests
diff --git a/pypelines/__init__.py b/pypelines/__init__.py
index 38f5db9..159c556 100644
--- a/pypelines/__init__.py
+++ b/pypelines/__init__.py
@@ -3,4 +3,5 @@ __version__ = "0.0.1"
from .pipe import *
from .pipeline import *
from .step import *
+from .disk import *
from .versions import *
\ No newline at end of file
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diff --git a/pypelines/__pycache__/pipe.cpython-311.pyc b/pypelines/__pycache__/pipe.cpython-311.pyc
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diff --git a/pypelines/__pycache__/step.cpython-39.pyc b/pypelines/__pycache__/step.cpython-39.pyc
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diff --git a/pypelines/disk.py b/pypelines/disk.py
index cfd1cfb..c22822d 100644
--- a/pypelines/disk.py
+++ b/pypelines/disk.py
@@ -1,5 +1,6 @@
-import os
+import os, re
from . sessions import Session
+import pickle
from typing import Callable, Type, Iterable, Protocol, TYPE_CHECKING
@@ -16,9 +17,8 @@ class BaseDiskObject :
disk_version = None
disk_step = None
- def __init__(self, session : Session, step : BaseStep, extra = "") -> None :
+ def __init__(self, session : Session, step : "BaseStep", extra = "") -> None :
- self.step = None
self.session = session
self.step = step
self.extra = extra
@@ -29,7 +29,7 @@ class BaseDiskObject :
"""sets self.disk_version and self.disk_step"""
...
- def save(self, object):
+ def save(self, data : OutputData) -> None:
...
def load(self) -> OutputData:
@@ -42,7 +42,6 @@ class BaseDiskObject :
def version_exist(self, session : Session):
"""returns True if the file found had a stamp for that step corresponding to the current version. False otherwise"""
return self.step.version == self.disk_version
-
class PickleObject(BaseDiskObject) :
@@ -50,35 +49,88 @@ class PickleObject(BaseDiskObject) :
file_prefix = "preproc_data"
extension = "pickle"
current_suffixes = ""
+ remove = True
+ current_disk_file = None
- def make_file_prefix_path(self):
- prefix_path = self.file_prefix + "." + self.step.pipe_name
- rigid_pattern = self.file_prefix
+ def parse_extra(self,extra):
+ extra = extra.strip(".").replace(".",r"\.")
+ return r"\." + extra if extra else ""
- pattern = ""
+ def make_file_name_pattern(self):
- if self.step.pipe.single_step :
- pass
+ steps_patterns = []
- if self.step.use_version :
- pass
+ for key in sorted(self.step.pipe.steps.keys()):
+ step = self.step.pipe.steps[key]
+ steps_patterns.append( fr"(?:{step.step_name})" )
- flexible_pattern = self.f
+ steps_patterns = "|".join(steps_patterns)
- def check_disk(self):
- search_path = os.path.join(self.session.path, self.collection)
+ version_pattern = fr"(?:\.(?P<version>[^\.]*))?"
+ step_pattern = fr"(?:\.(?P<step_name>{steps_patterns}){version_pattern})?"
+ extra = self.parse_extra(self.extra)
+
+ pattern = self.file_prefix + r"\." + self.step.pipe_name + step_pattern + extra + r"\." + self.extension
+ print(pattern)
+ return pattern
+
+ def get_file_name(self):
- def save(self, object):
- ...
+ extra = self.parse_extra(self.extra)
+ version_string = "." + self.step.version if self.step.use_version else ""
+ filename = self.file_prefix + "." + self.step.pipe_name + "." + self.step.step_name + version_string + extra + "." + self.extension
+ return filename
- def load(self) -> OutputData:
- ...
+ def check_disk(self):
+ search_path = os.path.join(self.session.path, os.path.sep.join(self.collection))
+ print(search_path)
+ matching_files = files(search_path, re_pattern = self.make_file_name_pattern(), relative = True, levels = 0)
+ print(matching_files)
+ if len(matching_files):
+ keys = ["step_name","version"]
+ expected_values = {"step_name" : self.step.step_name, "version" : self.step.version if self.step.use_version else None}
+ pattern = re.compile(self.make_file_name_pattern())
+ match_datas = []
+ for index, file in enumerate(matching_files) :
+ match = pattern.search(file)
+ match_data = {}
+ for key in keys :
+ match_data[key] = match.group(key)
+ #TODO : catch here with KeyError and return an error that is more explicit, saying key is not present in the pattern
+ if expected_values == match_data :
+ self.current_disk_file = os.path.join(search_path, matching_files[index])
+ return True
+ match_datas.append(match_data)
+ else :
+ if len(match_datas) == 1:
+ print(f"A single partial match was found. Please make sure it is consistant with expected behaviour. Expected : {expected_values} , Found : {match_datas[0]}")
+ self.current_disk_file = os.path.join(search_path, matching_files[0])
+ return True
+ print(f"More than one partial match were found. Cannot auto select. Expected : {expected_values} , Found : {match_datas}")
+ return False
+ return False
+
+ def get_full_path(self):
+ full_path = os.path.join(self.session.path, os.path.sep.join(self.collection), self.get_file_name() )
+ return full_path
+
+ def save(self, data : OutputData):
+ new_full_path = self.get_full_path()
+ with open(new_full_path, "wb") as f :
+ pickle.dump(data, f)
+ if self.current_disk_file is not None and self.current_disk_file != new_full_path and self.remove :
+ os.remove(self.current_disk_file)
+ self.current_disk_file = new_full_path
+ def load(self) -> OutputData:
+ if self.current_disk_file is None :
+ raise IOError("Could not find a file to load. Either no file was found on disk, or you forgot to run 'check_disk()'")
+ with open(self.current_disk_file, "rb") as f :
+ return pickle.load(f)
import natsort
-from . import extract
def files(input_path, re_pattern = None, relative = False,levels = -1, get = "files", parts = "all", sort = True):
"""
@@ -103,11 +155,11 @@ def files(input_path, re_pattern = None, relative = False,levels = -1, get = "fi
for subdir in os.listdir(_input_path):
fullpath = os.path.join(_input_path,subdir)
if os.path.isfile(fullpath):
- if (get == "all" or get == "files") and (re_pattern is None or extract.qregexp(re_pattern,fullpath)):
+ if (get == "all" or get == "files") and (re_pattern is None or qregexp(re_pattern,fullpath)):
output_list.append(os.path.normpath(fullpath))
else :
- if (get == "all" or get == "dirs" or get == "folders") and (re_pattern is None or extract.qregexp(re_pattern,fullpath)):
+ if (get == "all" or get == "dirs" or get == "folders") and (re_pattern is None or qregexp(re_pattern,fullpath)):
output_list.append(os.path.normpath(fullpath))
if current_level < levels:
current_level += 1
@@ -129,4 +181,62 @@ def files(input_path, re_pattern = None, relative = False,levels = -1, get = "fi
+def qregexp(regex, input_line, groupidx=None, matchid=None , case=False):
+ """
+ Simplified implementation for matching regular expressions. Utility for python's built_in module re .
+
+ Tip:
+ Design your patterns easily at [Regex101](https://regex101.com/)
+
+ Args:
+ input_line (str): Source on wich the pattern will be searched.
+ regex (str): Regex pattern to match on the source.
+ **kwargs (optional):
+ - groupidx : (``int``)
+ group index in case there is groups. Defaults to None (first group returned)
+ - matchid : (``int``)
+ match index in case there is multiple matchs. Defaults to None (first match returned)
+ - case : (``bool``)
+ `False` / `True` : case sensitive regexp matching (default ``False``)
+
+ Returns:
+ Bool , str: False or string containing matched content.
+
+ Warning:
+ This function returns only one group/match.
+
+ """
+
+ if case :
+ matches = re.finditer(regex, input_line, re.MULTILINE|re.IGNORECASE)
+ else :
+ matches = re.finditer(regex, input_line, re.MULTILINE)
+
+ if matchid is not None :
+ matchid = matchid +1
+
+ for matchnum, match in enumerate(matches, start = 1):
+
+ if matchid is not None :
+ if matchnum == matchid :
+ if groupidx is not None :
+ for groupx, groupcontent in enumerate(match.groups()):
+ if groupx == groupidx :
+ return groupcontent
+ return False
+
+ else :
+ MATCH = match.group()
+ return MATCH
+
+ else :
+ if groupidx is not None :
+ for groupx, groupcontent in enumerate(match.groups()):
+ if groupx == groupidx :
+ return groupcontent
+ return False
+ else :
+ MATCH = match.group()
+ return MATCH
+ return False
\ No newline at end of file
diff --git a/pypelines/examples.py b/pypelines/examples.py
index bc46c65..763bb8b 100644
--- a/pypelines/examples.py
+++ b/pypelines/examples.py
@@ -6,7 +6,7 @@ from .step import stepmethod
class ExamplePipeline(BasePipeline):
...
-example_pipeline = ExamplePipeline()
+example_pipeline = ExamplePipeline("example")
@example_pipeline.register_pipe
class ExamplePipe(PicklePipe):
diff --git a/pypelines/feature_test.ipynb b/pypelines/feature_test.ipynb
new file mode 100644
index 0000000..f8fdc7a
--- /dev/null
+++ b/pypelines/feature_test.ipynb
@@ -0,0 +1,1000 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from pypelines import BasePipeline, BaseStep, BasePipe, PickleObject, Session, stepmethod\n",
+ "from pypelines.examples import example_pipeline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'compress_videos': <compress_videos.step StepObject>,\n",
+ " 'suite2p': <BasePipe.suite2p PipeObject>,\n",
+ " 'trials_roi_df': <trials_roi_df.aggregate StepObject>,\n",
+ " 'trials_df': <trials_df.initial StepObject>,\n",
+ " 'rois_df': <BasePipe.rois_df PipeObject>,\n",
+ " 'figures': <BasePipe.figures PipeObject>}"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pline = BasePipeline(\"preproc_data\")\n",
+ "\n",
+ "\n",
+ "@pline.register_pipe \n",
+ "class compress_videos(BasePipe) :\n",
+ " \n",
+ " single_step = True\n",
+ " \n",
+ " @stepmethod(requires = [\"rois_df.step1\",\"trials_roi_df.aggregate\"])\n",
+ " def step(self):\n",
+ " \"\"\"zaea\n",
+ " \"\"\"\n",
+ " #comment here comment there\n",
+ "\n",
+ " monfion = \"est fat\"\n",
+ " \n",
+ " return \"blabla\" \n",
+ "\n",
+ "@pline.register_pipe\n",
+ "class suite2p(BasePipe) :\n",
+ "\n",
+ " @stepmethod(requires = [\"compress_videos.step\"])\n",
+ " def do_this(self,*args,**kwargs):\n",
+ " print(self)\n",
+ " print(args)\n",
+ " print(kwargs)\n",
+ " \n",
+ " @stepmethod(requires = [\"suite2p.do_this\"])\n",
+ " def step2(self,):\n",
+ " return \"something\"\n",
+ "\n",
+ "@pline.register_pipe \n",
+ "class trials_roi_df(BasePipe) :\n",
+ " \n",
+ " single_step = True\n",
+ " \n",
+ " @stepmethod()\n",
+ " def aggregate(self):\n",
+ " \"\"\"zaea\n",
+ " \"\"\"\n",
+ " #comment here comment there\n",
+ "\n",
+ " monfion = \"est fat\"\n",
+ " \n",
+ " return \"blabla\" \n",
+ "\n",
+ "@pline.register_pipe# this wrapper is called after the internal wrappers, so we will be able to finish utility things with it. # this step instanciates a registered class into the Pipeline, then returns the uninstanciated class in case they must be used by other pipelines.\n",
+ "class trials_df(BasePipe):\n",
+ "\n",
+ " single_step = True\n",
+ " use_versions = True # by default, if more than one worker is registered, this is set to true, except is explicitly set to false like this.\n",
+ "\n",
+ " \"\"\"arguments that the Pipe will use : \n",
+ "\n",
+ " - extra = info related to anything the user may want. It can be a simple string, a dict with keys related to folder location, or anything else that may help the user find the location of the file.\n",
+ " - session = info related to the base location of the files. In pandas series format. It must contain a path attribute/item, and an alias item. subject etc are somewhat optionnal for my case i would say. \n",
+ " I will have to see if i can extend acessors externally (plugin methodo ?) to suit my purposes.\n",
+ " - version = a version must be a string (a hash, whatever length, but a string) that allows to look up in a versions.json file to match what was the associated worker step, and it's relation in the hierarchy of steps.\n",
+ " \"\"\"\n",
+ "\n",
+ " @stepmethod()\n",
+ " def initial(self):\n",
+ " print(self) #this is a pipe instance, not a step, we will need to see if this is a good idea or not\n",
+ "\n",
+ "@pline.register_pipe\n",
+ "class rois_df(BasePipe) :\n",
+ "\n",
+ " @stepmethod(requires = [\"trials_df.initial\"], version = \"1\")\n",
+ " def step1(self):\n",
+ " pass\n",
+ "\n",
+ " @stepmethod(requires = [\"suite2p.step2\"], version = \"5\")\n",
+ " def step2(self):\n",
+ " return \"something\"\n",
+ " \n",
+ "@pline.register_pipe\n",
+ "class figures(BasePipe) :\n",
+ "\n",
+ " @stepmethod(requires = [\"compress_videos.step\"])\n",
+ " def step1(self):\n",
+ " pass\n",
+ "\n",
+ " @stepmethod(requires = [\"figures.step1\"])\n",
+ " def step2(self):\n",
+ " pass\n",
+ "\n",
+ "\n",
+ "pline.pipes"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "C:\\test\\wm32\\2023-08-25\\001\\preprocessing_saves\n",
+ "preproc_data\\.suite2p(?:\\.(?P<step_name>(?:do_this)|(?:step2))(?:\\.(?P<version>[^\\.]*))?)?\\.pickle\n",
+ "['preproc_data.suite2p.do_this.pickle']\n",
+ "preproc_data\\.suite2p(?:\\.(?P<step_name>(?:do_this)|(?:step2))(?:\\.(?P<version>[^\\.]*))?)?\\.pickle\n",
+ "A single partial match was found. Please make sure it is consistant with expected behaviour. Expected : {'step_name': 'do_this', 'version': ''} , Found : {'step_name': 'do_this', 'version': None}\n"
+ ]
+ }
+ ],
+ "source": [
+ "session = Session(subject=\"wm32\",date=\"2023-08-25\",number=1,path=r\"C:\\test\", auto_path = True)\n",
+ "disk = PickleObject(session, pline.suite2p.do_this, extra = \"\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "preproc_data\\.suite2p(?:\\.(?P<step_name>(?:do_this)|(?:step2))(?:\\.(?P<version>[^\\.]*))?)?\\.pickle\n",
+ "preproc_data\\.suite2p(?:\\.(?P<step_name>(?:do_this)|(?:step2))(?:\\.(?P<version>[^\\.]*))?)?\\.pickle\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(disk.make_file_name_pattern())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "C:\\test\\wm32\\2023-08-25\\001\\preprocessing_saves\n",
+ "preproc_data\\.suite2p(?:\\.(?P<step_name>(?:do_this)|(?:step2))(?:\\.(?P<version>[^\\.]*))?)?\\.pickle\n",
+ "['preproc_data.suite2p.do_this.pickle', 'preproc_data.suite2p.pickle']\n",
+ "preproc_data\\.suite2p(?:\\.(?P<step_name>(?:do_this)|(?:step2))(?:\\.(?P<version>[^\\.]*))?)?\\.pickle\n",
+ "More than one partial match were found. Cannot auto select. Expected : {'step_name': 'do_this', 'version': ''} , Found : [{'step_name': 'do_this', 'version': None}, {'step_name': None, 'version': None}]\n",
+ "False\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(disk.check_disk())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(disk.save(\"caca\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(disk.save(\"caca\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(disk.save(\"caca\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "None\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(disk.save(\"caca\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "caca\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(disk.load())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[('a', <dict_keyiterator object at 0x00000188E1C2BA60>, deque([0, 1]), deque([-1, -1]))]\n",
+ "['b', 'c']\n",
+ "<dict_keyiterator object at 0x00000188E1C2BA60>\n",
+ "b\n",
+ "c\n",
+ "<dict_keyiterator object at 0x00000188E1C2A020>\n",
+ "c\n",
+ "d\n",
+ "h\n",
+ "<dict_keyiterator object at 0x00000188E2DCF0B0>\n",
+ "h\n",
+ "<dict_keyiterator object at 0x00000188E308EC00>\n",
+ "<dict_keyiterator object at 0x00000188E308EB10>\n",
+ "[('e', <dict_keyiterator object at 0x00000188E2DCF0B0>, deque([0]), deque([-1]))]\n",
+ "['f']\n",
+ "<dict_keyiterator object at 0x00000188E2DCF0B0>\n",
+ "f\n",
+ "<dict_keyiterator object at 0x00000188E1C2BA60>\n",
+ "c\n",
+ "g\n",
+ "<dict_keyiterator object at 0x00000188E1C2A020>\n",
+ "h\n",
+ "<dict_keyiterator object at 0x00000188E308EB10>\n",
+ "h\n",
+ "<dict_keyiterator object at 0x00000188E1C2BA60>\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import networkx as nx\n",
+ "from collections import deque\n",
+ "\n",
+ "def tree_layout(G):\n",
+ "\n",
+ " roots = [node for node in G.nodes if G.in_degree(node) == 0]\n",
+ " if not roots:\n",
+ " print(\"Error: graph has no roots!\")\n",
+ " return None\n",
+ "\n",
+ " pos = {}\n",
+ " base_x = 0\n",
+ " next_x = 0\n",
+ " for root in roots:\n",
+ " qx = deque([next_x + xt for xt in range(0, G.out_degree(root))])\n",
+ " qy = deque([0 - 1]*G.out_degree(root))\n",
+ " next_x = base_x\n",
+ "\n",
+ " visited = {root}\n",
+ " deque_content = [(root, iter(G[root]), qx, qy)]\n",
+ " print(deque_content)\n",
+ " print(list(iter(G[root])))\n",
+ " queue = deque(deque_content)\n",
+ "\n",
+ " while queue:\n",
+ " parent, children, qx, qy = queue.popleft()\n",
+ " print(children)\n",
+ " for child in children:\n",
+ " print(child)\n",
+ " #child = next(children)\n",
+ " if child not in visited:\n",
+ " x = qx.popleft()\n",
+ " y = qy.popleft()\n",
+ " pos[child] = (x, y)\n",
+ " visited.add(child)\n",
+ " qx_child = deque([x+ xt for xt in range(0, G.out_degree(child))])\n",
+ " qy_child = deque([y - 1]*G.out_degree(child))\n",
+ " queue.append((child, iter(G[child]), qx_child, qy_child))\n",
+ " #except StopIteration:\n",
+ " \n",
+ "\n",
+ " pos[root] = (base_x, max([val[1] for val in pos.values()])+1) # place the root\n",
+ " base_x = base_x + max([val[0] for val in pos.values()]) + 1\n",
+ "\n",
+ " return pos\n",
+ "\n",
+ "# Use the function like this:\n",
+ "G = nx.DiGraph()\n",
+ "G.add_edge('a', 'b')\n",
+ "G.add_edge('a', 'c')\n",
+ "G.add_edge('b', 'c')\n",
+ "G.add_edge('b', 'd')\n",
+ "G.add_edge('b', 'h')\n",
+ "G.add_edge('f', 'c')\n",
+ "G.add_edge('c', 'h')\n",
+ "G.add_edge('e', 'f')\n",
+ "G.add_edge('f', 'g')\n",
+ "G.add_edge('g', 'h')\n",
+ "pos = tree_layout(G)\n",
+ "nx.draw(G, pos, with_labels=True)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "NodeView(('prefetch.step', 'rois_df.step1', 'blabliblou.herewego', 'suite2p.do_this', 'suite2p.step2', 'trials_df.initial', 'rois_df.step2', 'side_step.step1', 'side_step.step2'))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[('blabliblou.herewego', <dict_keyiterator object at 0x0000017FC214FAB0>, deque([0]), deque([-1]))]\n",
+ "['prefetch.step']\n",
+ "<dict_keyiterator object at 0x0000017FC214FAB0>\n",
+ "prefetch.step\n",
+ "<dict_keyiterator object at 0x0000017FC1FD02C0>\n",
+ "suite2p.do_this\n",
+ "side_step.step1\n",
+ "<dict_keyiterator object at 0x0000017FBFEF7AB0>\n",
+ "suite2p.step2\n",
+ "<dict_keyiterator object at 0x0000017FBFEF5F30>\n",
+ "side_step.step2\n",
+ "<dict_keyiterator object at 0x0000017FC1FD02C0>\n",
+ "rois_df.step2\n",
+ "<dict_keyiterator object at 0x0000017FBFEF7AB0>\n",
+ "<dict_keyiterator object at 0x0000017FBFEF5F30>\n",
+ "[('trials_df.initial', <dict_keyiterator object at 0x0000017FBFEF7AB0>, deque([0]), deque([-1]))]\n",
+ "['rois_df.step1']\n",
+ "<dict_keyiterator object at 0x0000017FBFEF7AB0>\n",
+ "rois_df.step1\n",
+ "<dict_keyiterator object at 0x0000017FBFEF5F30>\n",
+ "prefetch.step\n",
+ "<dict_keyiterator object at 0x0000017FC214FAB0>\n",
+ "suite2p.do_this\n",
+ "side_step.step1\n",
+ "<dict_keyiterator object at 0x0000017FBFEF5F30>\n",
+ "suite2p.step2\n",
+ "<dict_keyiterator object at 0x0000017FBFF489A0>\n",
+ "side_step.step2\n",
+ "<dict_keyiterator object at 0x0000017FC214FAB0>\n",
+ "rois_df.step2\n",
+ "<dict_keyiterator object at 0x0000017FBFEF5F30>\n",
+ "<dict_keyiterator object at 0x0000017FBFF489A0>\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from networkx import draw\n",
+ "g = pline.get_graph()[1]\n",
+ "display(g.nodes)\n",
+ "pos = tree_layout(g)\n",
+ "draw(g, pos= pos, with_labels = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "NodeView(('prefetch.step', 'other_steps.step1', 'suite2p.do_this', 'suite2p.step2', 'demoPipeClass.initial', 'other_steps.step2', 'side_step.step1', 'side_step.step2'))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from networkx import draw\n",
+ "g = pline.get_graph()[1]\n",
+ "display(g.nodes)\n",
+ "#pos = tree_layout(g)\n",
+ "draw(g, with_labels = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "NodeView(('prefetch.step', 'other_steps.step1', 'suite2p.do_this', 'suite2p.step2', 'demoPipeClass.initial', 'other_steps.step2', 'side_step.step1', 'side_step.step2'))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "ename": "NetworkXError",
+ "evalue": "Node 'other_steps.step1' has no position.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:425\u001b[0m, in \u001b[0;36mdraw_networkx_nodes\u001b[1;34m(G, pos, nodelist, node_size, node_color, node_shape, alpha, cmap, vmin, vmax, ax, linewidths, edgecolors, label, margins)\u001b[0m\n",
+ "\u001b[0;32m 424\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
+ "\u001b[1;32m--> 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;49;00m v \u001b[39min\u001b[39;49;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:425\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n",
+ "\u001b[0;32m 424\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
+ "\u001b[1;32m--> 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\n",
+ "\u001b[1;31mKeyError\u001b[0m: 'other_steps.step1'\n",
+ "\n",
+ "The above exception was the direct cause of the following exception:\n",
+ "\n",
+ "\u001b[1;31mNetworkXError\u001b[0m Traceback (most recent call last)\n",
+ "\u001b[1;32mc:\\Users\\tjostmou\\Documents\\Python\\__packages__\\Pypelines\\pypelines\\feature_test.ipynb Cell 6\u001b[0m line \u001b[0;36m5\n",
+ "\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m display(g\u001b[39m.\u001b[39mnodes)\n",
+ "\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m pos \u001b[39m=\u001b[39m tree_layout(g)\n",
+ "\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m draw(g, pos\u001b[39m=\u001b[39;49m pos, with_labels \u001b[39m=\u001b[39;49m \u001b[39mTrue\u001b[39;49;00m)\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:121\u001b[0m, in \u001b[0;36mdraw\u001b[1;34m(G, pos, ax, **kwds)\u001b[0m\n",
+ "\u001b[0;32m 118\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mwith_labels\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m kwds:\n",
+ "\u001b[0;32m 119\u001b[0m kwds[\u001b[39m\"\u001b[39m\u001b[39mwith_labels\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mlabels\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m kwds\n",
+ "\u001b[1;32m--> 121\u001b[0m draw_networkx(G, pos\u001b[39m=\u001b[39;49mpos, ax\u001b[39m=\u001b[39;49max, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n",
+ "\u001b[0;32m 122\u001b[0m ax\u001b[39m.\u001b[39mset_axis_off()\n",
+ "\u001b[0;32m 123\u001b[0m plt\u001b[39m.\u001b[39mdraw_if_interactive()\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:303\u001b[0m, in \u001b[0;36mdraw_networkx\u001b[1;34m(G, pos, arrows, with_labels, **kwds)\u001b[0m\n",
+ "\u001b[0;32m 300\u001b[0m \u001b[39mif\u001b[39;00m pos \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+ "\u001b[0;32m 301\u001b[0m pos \u001b[39m=\u001b[39m nx\u001b[39m.\u001b[39mdrawing\u001b[39m.\u001b[39mspring_layout(G) \u001b[39m# default to spring layout\u001b[39;00m\n",
+ "\u001b[1;32m--> 303\u001b[0m draw_networkx_nodes(G, pos, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mnode_kwds)\n",
+ "\u001b[0;32m 304\u001b[0m draw_networkx_edges(G, pos, arrows\u001b[39m=\u001b[39marrows, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39medge_kwds)\n",
+ "\u001b[0;32m 305\u001b[0m \u001b[39mif\u001b[39;00m with_labels:\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:427\u001b[0m, in \u001b[0;36mdraw_networkx_nodes\u001b[1;34m(G, pos, nodelist, node_size, node_color, node_shape, alpha, cmap, vmin, vmax, ax, linewidths, edgecolors, label, margins)\u001b[0m\n",
+ "\u001b[0;32m 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\u001b[1;32m--> 427\u001b[0m \u001b[39mraise\u001b[39;00m nx\u001b[39m.\u001b[39mNetworkXError(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNode \u001b[39m\u001b[39m{\u001b[39;00merr\u001b[39m}\u001b[39;00m\u001b[39m has no position.\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n",
+ "\u001b[0;32m 429\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(alpha, Iterable):\n",
+ "\u001b[0;32m 430\u001b[0m node_color \u001b[39m=\u001b[39m apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax)\n",
+ "\n",
+ "\u001b[1;31mNetworkXError\u001b[0m: Node 'other_steps.step1' has no position."
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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UVERTU1N0dHQcc91Xv/rVmDhxYtxyyy0ndJ1Dhw5Fb2/vkBcAAJyMssJ3//790d/fH3V1dUPG6+rqoqur66hrXnrppXjiiSdi3bp1J3ydtra2qK2tHXzV19eXs00AADjCqD7V4cCBAzF//vxYt25dTJgw4YTXLVu2LHp6egZfe/fuHcVdAgCQwVnlTJ4wYUJUVlZGd3f3kPHu7u6YNGnSEfN//vOfx1tvvRVz5swZHBsYGPjdhc86K1577bW4+OKLj1hXKpWiVCqVszUAADiusu74VlVVxcyZM6O9vX1wbGBgINrb26OxsfGI+Zdeemm88sor0dnZOfj67Gc/G9dff310dnb6CgMAAGOmrDu+EREtLS2xcOHCmDVrVsyePTtWr14dBw8ejEWLFkVExIIFC2Lq1KnR1tYW1dXVcdlllw1Zf/7550dEHDEOAACjqezwnTdvXuzbty9WrFgRXV1dMWPGjNi8efPgD7zt2bMnKir8gXAAAJxexhVFUZzqTbyf3t7eqK2tjZ6enqipqTnV2wEAYJSNRv+5NQsAQArCFwCAFIQvAAApCF8AAFIQvgAApCB8AQBIQfgCAJCC8AUAIAXhCwBACsIXAIAUhC8AACkIXwAAUhC+AACkIHwBAEhB+AIAkILwBQAgBeELAEAKwhcAgBSELwAAKQhfAABSEL4AAKQgfAEASEH4AgCQgvAFACAF4QsAQArCFwCAFIQvAAApCF8AAFIQvgAApCB8AQBIQfgCAJCC8AUAIAXhCwBACsIXAIAUhC8AACkIXwAAUhC+AACkIHwBAEhB+AIAkILwBQAgBeELAEAKwhcAgBSELwAAKQhfAABSEL4AAKQgfAEASEH4AgCQgvAFACAF4QsAQArCFwCAFIQvAAApCF8AAFIQvgAApCB8AQBIQfgCAJCC8AUAIAXhCwBACsIXAIAUhC8AACkIXwAAUhC+AACkIHwBAEhB+AIAkILwBQAgBeELAEAKwhcAgBSELwAAKQhfAABSEL4AAKQgfAEASEH4AgCQgvAFACAF4QsAQArCFwCAFIQvAAApCF8AAFIQvgAApCB8AQBIQfgCAJCC8AUAIAXhCwBACsIXAIAUhC8AACkIXwAAUhC+AACkMKzwXbNmTUybNi2qq6ujoaEhtm7desy569ati2uvvTbGjx8f48ePj6ampuPOBwCA0VB2+G7YsCFaWlqitbU1tm/fHtOnT4/m5uZ4++23jzp/y5YtcdNNN8WPfvSj6OjoiPr6+vjMZz4Tv/zlL0968wAAcKLGFUVRlLOgoaEhrrrqqnj00UcjImJgYCDq6+vjzjvvjKVLl77v+v7+/hg/fnw8+uijsWDBghO6Zm9vb9TW1kZPT0/U1NSUs10AAM5Ao9F/Zd3x7evri23btkVTU9Mf3qCiIpqamqKjo+OE3uPdd9+N9957Ly644IJjzjl06FD09vYOeQEAwMkoK3z3798f/f39UVdXN2S8rq4uurq6Tug97r777pgyZcqQeP5jbW1tUVtbO/iqr68vZ5sAAHCEMX2qw6pVq2L9+vXx3HPPRXV19THnLVu2LHp6egZfe/fuHcNdAgDwQXRWOZMnTJgQlZWV0d3dPWS8u7s7Jk2adNy1Dz30UKxatSp++MMfxhVXXHHcuaVSKUqlUjlbAwCA4yrrjm9VVVXMnDkz2tvbB8cGBgaivb09Ghsbj7nuwQcfjAceeCA2b94cs2bNGv5uAQBgmMq64xsR0dLSEgsXLoxZs2bF7NmzY/Xq1XHw4MFYtGhRREQsWLAgpk6dGm1tbRER8c///M+xYsWKePrpp2PatGmD3wX+0Ic+FB/60IdG8KMAAMCxlR2+8+bNi3379sWKFSuiq6srZsyYEZs3bx78gbc9e/ZERcUfbiR/85vfjL6+vvibv/mbIe/T2toaX/nKV05u9wAAcILKfo7vqeA5vgAAuZzy5/gCAMCZSvgCAJCC8AUAIAXhCwBACsIXAIAUhC8AACkIXwAAUhC+AACkIHwBAEhB+AIAkILwBQAgBeELAEAKwhcAgBSELwAAKQhfAABSEL4AAKQgfAEASEH4AgCQgvAFACAF4QsAQArCFwCAFIQvAAApCF8AAFIQvgAApCB8AQBIQfgCAJCC8AUAIAXhCwBACsIXAIAUhC8AACkIXwAAUhC+AACkIHwBAEhB+AIAkILwBQAgBeELAEAKwhcAgBSELwAAKQhfAABSEL4AAKQgfAEASEH4AgCQgvAFACAF4QsAQArCFwCAFIQvAAApCF8AAFIQvgAApCB8AQBIQfgCAJCC8AUAIAXhCwBACsIXAIAUhC8AACkIXwAAUhC+AACkIHwBAEhB+AIAkILwBQAgBeELAEAKwhcAgBSELwAAKQhfAABSEL4AAKQgfAEASEH4AgCQgvAFACAF4QsAQArCFwCAFIQvAAApCF8AAFIQvgAApCB8AQBIQfgCAJCC8AUAIAXhCwBACsIXAIAUhC8AACkIXwAAUhC+AACkIHwBAEhB+AIAkILwBQAgBeELAEAKwhcAgBSGFb5r1qyJadOmRXV1dTQ0NMTWrVuPO/973/teXHrppVFdXR2XX355bNq0aVibBQCA4So7fDds2BAtLS3R2toa27dvj+nTp0dzc3O8/fbbR53/8ssvx0033RS33HJL7NixI+bOnRtz586Nn/3sZye9eQAAOFHjiqIoylnQ0NAQV111VTz66KMRETEwMBD19fVx5513xtKlS4+YP2/evDh48GD84Ac/GBz7y7/8y5gxY0asXbv2hK7Z29sbtbW10dPTEzU1NeVsFwCAM9Bo9N9Z5Uzu6+uLbdu2xbJlywbHKioqoqmpKTo6Oo66pqOjI1paWoaMNTc3x/PPP3/M6xw6dCgOHTo0+Ouenp6I+N3fAAAAPvh+331l3qM9rrLCd//+/dHf3x91dXVDxuvq6mLXrl1HXdPV1XXU+V1dXce8TltbW9x///1HjNfX15ezXQAAznD/8z//E7W1tSPyXmWF71hZtmzZkLvE77zzTnz4wx+OPXv2jNgH58zR29sb9fX1sXfvXl91Scj55+b8c3P+ufX09MSFF14YF1xwwYi9Z1nhO2HChKisrIzu7u4h493d3TFp0qSjrpk0aVJZ8yMiSqVSlEqlI8Zra2v9g59YTU2N80/M+efm/HNz/rlVVIzc03fLeqeqqqqYOXNmtLe3D44NDAxEe3t7NDY2HnVNY2PjkPkRES+++OIx5wMAwGgo+6sOLS0tsXDhwpg1a1bMnj07Vq9eHQcPHoxFixZFRMSCBQti6tSp0dbWFhERd911V1x33XXx8MMPx4033hjr16+Pn/70p/H444+P7CcBAIDjKDt8582bF/v27YsVK1ZEV1dXzJgxIzZv3jz4A2x79uwZckv66quvjqeffjruvffeuOeee+Iv/uIv4vnnn4/LLrvshK9ZKpWitbX1qF9/4IPP+efm/HNz/rk5/9xG4/zLfo4vAACciUbu28IAAHAaE74AAKQgfAEASEH4AgCQwmkTvmvWrIlp06ZFdXV1NDQ0xNatW487/3vf+15ceumlUV1dHZdffnls2rRpjHbKaCjn/NetWxfXXnttjB8/PsaPHx9NTU3v+88Lp7dyf///3vr162PcuHExd+7c0d0go6rc83/nnXdi8eLFMXny5CiVSnHJJZf4d8AZrNzzX716dXz0ox+Nc845J+rr62PJkiXx29/+dox2y0j58Y9/HHPmzIkpU6bEuHHj4vnnn3/fNVu2bIlPfvKTUSqV4iMf+Ug89dRT5V+4OA2sX7++qKqqKp588sniv/7rv4rbbrutOP/884vu7u6jzv/JT35SVFZWFg8++GDx6quvFvfee29x9tlnF6+88soY75yRUO7533zzzcWaNWuKHTt2FDt37iz+7u/+rqitrS3++7//e4x3zkgo9/x/78033yymTp1aXHvttcVf//Vfj81mGXHlnv+hQ4eKWbNmFTfccEPx0ksvFW+++WaxZcuWorOzc4x3zkgo9/y/853vFKVSqfjOd75TvPnmm8ULL7xQTJ48uViyZMkY75yTtWnTpmL58uXFs88+W0RE8dxzzx13/u7du4tzzz23aGlpKV599dXiG9/4RlFZWVls3ry5rOueFuE7e/bsYvHixYO/7u/vL6ZMmVK0tbUddf7nPve54sYbbxwy1tDQUPz93//9qO6T0VHu+f+xw4cPF+edd17x7W9/e7S2yCgazvkfPny4uPrqq4tvfetbxcKFC4XvGazc8//mN79ZXHTRRUVfX99YbZFRVO75L168uPj0pz89ZKylpaW45pprRnWfjK4TCd8vf/nLxSc+8YkhY/PmzSuam5vLutYp/6pDX19fbNu2LZqamgbHKioqoqmpKTo6Oo66pqOjY8j8iIjm5uZjzuf0NZzz/2PvvvtuvPfee3HBBReM1jYZJcM9/69+9asxceLEuOWWW8Zim4yS4Zz/97///WhsbIzFixdHXV1dXHbZZbFy5cro7+8fq20zQoZz/ldffXVs27Zt8OsQu3fvjk2bNsUNN9wwJnvm1Bmp9iv7T24bafv374/+/v7BP/nt9+rq6mLXrl1HXdPV1XXU+V1dXaO2T0bHcM7/j919990xZcqUI35DcPobzvm/9NJL8cQTT0RnZ+cY7JDRNJzz3717d/zHf/xHfP7zn49NmzbFG2+8EV/84hfjvffei9bW1rHYNiNkOOd/8803x/79++NTn/pUFEURhw8fjjvuuCPuueeesdgyp9Cx2q+3tzd+85vfxDnnnHNC73PK7/jCyVi1alWsX78+nnvuuaiurj7V22GUHThwIObPnx/r1q2LCRMmnOrtcAoMDAzExIkT4/HHH4+ZM2fGvHnzYvny5bF27dpTvTXGwJYtW2LlypXx2GOPxfbt2+PZZ5+NjRs3xgMPPHCqt8YZ4pTf8Z0wYUJUVlZGd3f3kPHu7u6YNGnSUddMmjSprPmcvoZz/r/30EMPxapVq+KHP/xhXHHFFaO5TUZJuef/85//PN56662YM2fO4NjAwEBERJx11lnx2muvxcUXXzy6m2bEDOf3/+TJk+Pss8+OysrKwbGPfexj0dXVFX19fVFVVTWqe2bkDOf877vvvpg/f37ceuutERFx+eWXx8GDB+P222+P5cuXR0WF+3kfVMdqv5qamhO+2xtxGtzxraqqipkzZ0Z7e/vg2MDAQLS3t0djY+NR1zQ2Ng6ZHxHx4osvHnM+p6/hnH9ExIMPPhgPPPBAbN68OWbNmjUWW2UUlHv+l156abzyyivR2dk5+PrsZz8b119/fXR2dkZ9ff1Ybp+TNJzf/9dcc0288cYbg//BExHx+uuvx+TJk0XvGWY45//uu+8eEbe//4+g3/2MFB9UI9Z+5f3c3ehYv359USqViqeeeqp49dVXi9tvv704//zzi66urqIoimL+/PnF0qVLB+f/5Cc/Kc4666zioYceKnbu3Fm0trZ6nNkZrNzzX7VqVVFVVVU888wzxa9+9avB14EDB07VR+AklHv+f8xTHc5s5Z7/nj17ivPOO6/4h3/4h+K1114rfvCDHxQTJ04svva1r52qj8BJKPf8W1tbi/POO6/4t3/7t2L37t3Fv//7vxcXX3xx8bnPfe5UfQSG6cCBA8WOHTuKHTt2FBFRPPLII8WOHTuKX/ziF0VRFMXSpUuL+fPnD87//ePM/umf/qnYuXNnsWbNmjP3cWZFURTf+MY3igsvvLCoqqoqZs+eXfznf/7n4F+77rrrioULFw6Z/93vfre45JJLiqqqquITn/hEsXHjxjHeMSOpnPP/8Ic/XETEEa/W1tax3zgjotzf//8/4XvmK/f8X3755aKhoaEolUrFRRddVHz9618vDh8+PMa7ZqSUc/7vvfde8ZWvfKW4+OKLi+rq6qK+vr744he/WPzv//7v2G+ck/KjH/3oqP8u//15L1y4sLjuuuuOWDNjxoyiqqqquOiii4p//dd/Lfu644rC/xsAAOCD75R/xxcAAMaC8AUAIAXhCwBACsIXAIAUhC8AACkIXwAAUhC+AACkIHwBAEhB+AIAkILwBQAgBeELAEAKwhcAgBT+HzgRrWHQVr4zAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from networkx import draw\n",
+ "g = pline.get_graph()[1]\n",
+ "display(g.nodes)\n",
+ "pos = tree_layout(g)\n",
+ "draw(g, pos= pos, with_labels = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "NodeView(('prefetch.step', 'other_steps.step1', 'suite2p.do_this', 'suite2p.step2', 'demoPipeClass.initial', 'other_steps.step2', 'side_step.step1', 'side_step.step2'))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "ename": "NetworkXError",
+ "evalue": "Node 'other_steps.step1' has no position.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:425\u001b[0m, in \u001b[0;36mdraw_networkx_nodes\u001b[1;34m(G, pos, nodelist, node_size, node_color, node_shape, alpha, cmap, vmin, vmax, ax, linewidths, edgecolors, label, margins)\u001b[0m\n",
+ "\u001b[0;32m 424\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
+ "\u001b[1;32m--> 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;49;00m v \u001b[39min\u001b[39;49;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:425\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n",
+ "\u001b[0;32m 424\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
+ "\u001b[1;32m--> 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\n",
+ "\u001b[1;31mKeyError\u001b[0m: 'other_steps.step1'\n",
+ "\n",
+ "The above exception was the direct cause of the following exception:\n",
+ "\n",
+ "\u001b[1;31mNetworkXError\u001b[0m Traceback (most recent call last)\n",
+ "\u001b[1;32mc:\\Users\\tjostmou\\Documents\\Python\\__packages__\\Pypelines\\pypelines\\feature_test.ipynb Cell 6\u001b[0m line \u001b[0;36m5\n",
+ "\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m display(g\u001b[39m.\u001b[39mnodes)\n",
+ "\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m pos \u001b[39m=\u001b[39m tree_layout(g)\n",
+ "\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m draw(g, pos\u001b[39m=\u001b[39;49m pos, with_labels \u001b[39m=\u001b[39;49m \u001b[39mTrue\u001b[39;49;00m)\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:121\u001b[0m, in \u001b[0;36mdraw\u001b[1;34m(G, pos, ax, **kwds)\u001b[0m\n",
+ "\u001b[0;32m 118\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mwith_labels\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m kwds:\n",
+ "\u001b[0;32m 119\u001b[0m kwds[\u001b[39m\"\u001b[39m\u001b[39mwith_labels\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mlabels\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m kwds\n",
+ "\u001b[1;32m--> 121\u001b[0m draw_networkx(G, pos\u001b[39m=\u001b[39;49mpos, ax\u001b[39m=\u001b[39;49max, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n",
+ "\u001b[0;32m 122\u001b[0m ax\u001b[39m.\u001b[39mset_axis_off()\n",
+ "\u001b[0;32m 123\u001b[0m plt\u001b[39m.\u001b[39mdraw_if_interactive()\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:303\u001b[0m, in \u001b[0;36mdraw_networkx\u001b[1;34m(G, pos, arrows, with_labels, **kwds)\u001b[0m\n",
+ "\u001b[0;32m 300\u001b[0m \u001b[39mif\u001b[39;00m pos \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+ "\u001b[0;32m 301\u001b[0m pos \u001b[39m=\u001b[39m nx\u001b[39m.\u001b[39mdrawing\u001b[39m.\u001b[39mspring_layout(G) \u001b[39m# default to spring layout\u001b[39;00m\n",
+ "\u001b[1;32m--> 303\u001b[0m draw_networkx_nodes(G, pos, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mnode_kwds)\n",
+ "\u001b[0;32m 304\u001b[0m draw_networkx_edges(G, pos, arrows\u001b[39m=\u001b[39marrows, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39medge_kwds)\n",
+ "\u001b[0;32m 305\u001b[0m \u001b[39mif\u001b[39;00m with_labels:\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:427\u001b[0m, in \u001b[0;36mdraw_networkx_nodes\u001b[1;34m(G, pos, nodelist, node_size, node_color, node_shape, alpha, cmap, vmin, vmax, ax, linewidths, edgecolors, label, margins)\u001b[0m\n",
+ "\u001b[0;32m 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\u001b[1;32m--> 427\u001b[0m \u001b[39mraise\u001b[39;00m nx\u001b[39m.\u001b[39mNetworkXError(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNode \u001b[39m\u001b[39m{\u001b[39;00merr\u001b[39m}\u001b[39;00m\u001b[39m has no position.\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n",
+ "\u001b[0;32m 429\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(alpha, Iterable):\n",
+ "\u001b[0;32m 430\u001b[0m node_color \u001b[39m=\u001b[39m apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax)\n",
+ "\n",
+ "\u001b[1;31mNetworkXError\u001b[0m: Node 'other_steps.step1' has no position."
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from networkx import draw\n",
+ "g = pline.get_graph()[1]\n",
+ "display(g.nodes)\n",
+ "pos = tree_layout(g)\n",
+ "draw(g, pos= pos, with_labels = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "NodeView(('prefetch.step', 'other_steps.step1', 'suite2p.do_this', 'suite2p.step2', 'demoPipeClass.initial', 'other_steps.step2', 'side_step.step1', 'side_step.step2'))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "ename": "NetworkXError",
+ "evalue": "Node 'other_steps.step1' has no position.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:425\u001b[0m, in \u001b[0;36mdraw_networkx_nodes\u001b[1;34m(G, pos, nodelist, node_size, node_color, node_shape, alpha, cmap, vmin, vmax, ax, linewidths, edgecolors, label, margins)\u001b[0m\n",
+ "\u001b[0;32m 424\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
+ "\u001b[1;32m--> 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;49;00m v \u001b[39min\u001b[39;49;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:425\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n",
+ "\u001b[0;32m 424\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
+ "\u001b[1;32m--> 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\n",
+ "\u001b[1;31mKeyError\u001b[0m: 'other_steps.step1'\n",
+ "\n",
+ "The above exception was the direct cause of the following exception:\n",
+ "\n",
+ "\u001b[1;31mNetworkXError\u001b[0m Traceback (most recent call last)\n",
+ "\u001b[1;32mc:\\Users\\tjostmou\\Documents\\Python\\__packages__\\Pypelines\\pypelines\\feature_test.ipynb Cell 6\u001b[0m line \u001b[0;36m5\n",
+ "\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m display(g\u001b[39m.\u001b[39mnodes)\n",
+ "\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m pos \u001b[39m=\u001b[39m tree_layout(g)\n",
+ "\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m draw(g, pos\u001b[39m=\u001b[39;49m pos, with_labels \u001b[39m=\u001b[39;49m \u001b[39mTrue\u001b[39;49;00m)\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:121\u001b[0m, in \u001b[0;36mdraw\u001b[1;34m(G, pos, ax, **kwds)\u001b[0m\n",
+ "\u001b[0;32m 118\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mwith_labels\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m kwds:\n",
+ "\u001b[0;32m 119\u001b[0m kwds[\u001b[39m\"\u001b[39m\u001b[39mwith_labels\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mlabels\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m kwds\n",
+ "\u001b[1;32m--> 121\u001b[0m draw_networkx(G, pos\u001b[39m=\u001b[39;49mpos, ax\u001b[39m=\u001b[39;49max, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n",
+ "\u001b[0;32m 122\u001b[0m ax\u001b[39m.\u001b[39mset_axis_off()\n",
+ "\u001b[0;32m 123\u001b[0m plt\u001b[39m.\u001b[39mdraw_if_interactive()\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:303\u001b[0m, in \u001b[0;36mdraw_networkx\u001b[1;34m(G, pos, arrows, with_labels, **kwds)\u001b[0m\n",
+ "\u001b[0;32m 300\u001b[0m \u001b[39mif\u001b[39;00m pos \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+ "\u001b[0;32m 301\u001b[0m pos \u001b[39m=\u001b[39m nx\u001b[39m.\u001b[39mdrawing\u001b[39m.\u001b[39mspring_layout(G) \u001b[39m# default to spring layout\u001b[39;00m\n",
+ "\u001b[1;32m--> 303\u001b[0m draw_networkx_nodes(G, pos, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mnode_kwds)\n",
+ "\u001b[0;32m 304\u001b[0m draw_networkx_edges(G, pos, arrows\u001b[39m=\u001b[39marrows, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39medge_kwds)\n",
+ "\u001b[0;32m 305\u001b[0m \u001b[39mif\u001b[39;00m with_labels:\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:427\u001b[0m, in \u001b[0;36mdraw_networkx_nodes\u001b[1;34m(G, pos, nodelist, node_size, node_color, node_shape, alpha, cmap, vmin, vmax, ax, linewidths, edgecolors, label, margins)\u001b[0m\n",
+ "\u001b[0;32m 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\u001b[1;32m--> 427\u001b[0m \u001b[39mraise\u001b[39;00m nx\u001b[39m.\u001b[39mNetworkXError(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNode \u001b[39m\u001b[39m{\u001b[39;00merr\u001b[39m}\u001b[39;00m\u001b[39m has no position.\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n",
+ "\u001b[0;32m 429\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(alpha, Iterable):\n",
+ "\u001b[0;32m 430\u001b[0m node_color \u001b[39m=\u001b[39m apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax)\n",
+ "\n",
+ "\u001b[1;31mNetworkXError\u001b[0m: Node 'other_steps.step1' has no position."
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from networkx import draw\n",
+ "g = pline.get_graph()[1]\n",
+ "display(g.nodes)\n",
+ "pos = tree_layout(g)\n",
+ "draw(g, pos= pos, with_labels = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "NodeView(('prefetch.step', 'other_steps.step1', 'suite2p.do_this', 'suite2p.step2', 'demoPipeClass.initial', 'other_steps.step2', 'side_step.step1', 'side_step.step2'))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "ename": "NetworkXError",
+ "evalue": "Node 'other_steps.step1' has no position.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:425\u001b[0m, in \u001b[0;36mdraw_networkx_nodes\u001b[1;34m(G, pos, nodelist, node_size, node_color, node_shape, alpha, cmap, vmin, vmax, ax, linewidths, edgecolors, label, margins)\u001b[0m\n",
+ "\u001b[0;32m 424\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
+ "\u001b[1;32m--> 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;49;00m v \u001b[39min\u001b[39;49;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:425\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n",
+ "\u001b[0;32m 424\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
+ "\u001b[1;32m--> 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\n",
+ "\u001b[1;31mKeyError\u001b[0m: 'other_steps.step1'\n",
+ "\n",
+ "The above exception was the direct cause of the following exception:\n",
+ "\n",
+ "\u001b[1;31mNetworkXError\u001b[0m Traceback (most recent call last)\n",
+ "\u001b[1;32mc:\\Users\\tjostmou\\Documents\\Python\\__packages__\\Pypelines\\pypelines\\feature_test.ipynb Cell 6\u001b[0m line \u001b[0;36m5\n",
+ "\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m display(g\u001b[39m.\u001b[39mnodes)\n",
+ "\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m pos \u001b[39m=\u001b[39m tree_layout(g)\n",
+ "\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m draw(g, pos\u001b[39m=\u001b[39;49m pos, with_labels \u001b[39m=\u001b[39;49m \u001b[39mTrue\u001b[39;49;00m)\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:121\u001b[0m, in \u001b[0;36mdraw\u001b[1;34m(G, pos, ax, **kwds)\u001b[0m\n",
+ "\u001b[0;32m 118\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mwith_labels\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m kwds:\n",
+ "\u001b[0;32m 119\u001b[0m kwds[\u001b[39m\"\u001b[39m\u001b[39mwith_labels\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mlabels\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m kwds\n",
+ "\u001b[1;32m--> 121\u001b[0m draw_networkx(G, pos\u001b[39m=\u001b[39;49mpos, ax\u001b[39m=\u001b[39;49max, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n",
+ "\u001b[0;32m 122\u001b[0m ax\u001b[39m.\u001b[39mset_axis_off()\n",
+ "\u001b[0;32m 123\u001b[0m plt\u001b[39m.\u001b[39mdraw_if_interactive()\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:303\u001b[0m, in \u001b[0;36mdraw_networkx\u001b[1;34m(G, pos, arrows, with_labels, **kwds)\u001b[0m\n",
+ "\u001b[0;32m 300\u001b[0m \u001b[39mif\u001b[39;00m pos \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+ "\u001b[0;32m 301\u001b[0m pos \u001b[39m=\u001b[39m nx\u001b[39m.\u001b[39mdrawing\u001b[39m.\u001b[39mspring_layout(G) \u001b[39m# default to spring layout\u001b[39;00m\n",
+ "\u001b[1;32m--> 303\u001b[0m draw_networkx_nodes(G, pos, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mnode_kwds)\n",
+ "\u001b[0;32m 304\u001b[0m draw_networkx_edges(G, pos, arrows\u001b[39m=\u001b[39marrows, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39medge_kwds)\n",
+ "\u001b[0;32m 305\u001b[0m \u001b[39mif\u001b[39;00m with_labels:\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:427\u001b[0m, in \u001b[0;36mdraw_networkx_nodes\u001b[1;34m(G, pos, nodelist, node_size, node_color, node_shape, alpha, cmap, vmin, vmax, ax, linewidths, edgecolors, label, margins)\u001b[0m\n",
+ "\u001b[0;32m 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\u001b[1;32m--> 427\u001b[0m \u001b[39mraise\u001b[39;00m nx\u001b[39m.\u001b[39mNetworkXError(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNode \u001b[39m\u001b[39m{\u001b[39;00merr\u001b[39m}\u001b[39;00m\u001b[39m has no position.\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n",
+ "\u001b[0;32m 429\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(alpha, Iterable):\n",
+ "\u001b[0;32m 430\u001b[0m node_color \u001b[39m=\u001b[39m apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax)\n",
+ "\n",
+ "\u001b[1;31mNetworkXError\u001b[0m: Node 'other_steps.step1' has no position."
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from networkx import draw\n",
+ "g = pline.get_graph()[1]\n",
+ "display(g.nodes)\n",
+ "pos = tree_layout(g)\n",
+ "draw(g, pos= pos, with_labels = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "NodeView(('prefetch.step', 'other_steps.step1', 'suite2p.do_this', 'suite2p.step2', 'demoPipeClass.initial', 'other_steps.step2', 'side_step.step1', 'side_step.step2'))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "ename": "NetworkXError",
+ "evalue": "Node 'other_steps.step1' has no position.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:425\u001b[0m, in \u001b[0;36mdraw_networkx_nodes\u001b[1;34m(G, pos, nodelist, node_size, node_color, node_shape, alpha, cmap, vmin, vmax, ax, linewidths, edgecolors, label, margins)\u001b[0m\n",
+ "\u001b[0;32m 424\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
+ "\u001b[1;32m--> 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;49;00m v \u001b[39min\u001b[39;49;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:425\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n",
+ "\u001b[0;32m 424\u001b[0m \u001b[39mtry\u001b[39;00m:\n",
+ "\u001b[1;32m--> 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\n",
+ "\u001b[1;31mKeyError\u001b[0m: 'other_steps.step1'\n",
+ "\n",
+ "The above exception was the direct cause of the following exception:\n",
+ "\n",
+ "\u001b[1;31mNetworkXError\u001b[0m Traceback (most recent call last)\n",
+ "\u001b[1;32mc:\\Users\\tjostmou\\Documents\\Python\\__packages__\\Pypelines\\pypelines\\feature_test.ipynb Cell 6\u001b[0m line \u001b[0;36m5\n",
+ "\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m display(g\u001b[39m.\u001b[39mnodes)\n",
+ "\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m pos \u001b[39m=\u001b[39m tree_layout(g)\n",
+ "\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m draw(g, pos\u001b[39m=\u001b[39;49m pos, with_labels \u001b[39m=\u001b[39;49m \u001b[39mTrue\u001b[39;49;00m)\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:121\u001b[0m, in \u001b[0;36mdraw\u001b[1;34m(G, pos, ax, **kwds)\u001b[0m\n",
+ "\u001b[0;32m 118\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mwith_labels\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m kwds:\n",
+ "\u001b[0;32m 119\u001b[0m kwds[\u001b[39m\"\u001b[39m\u001b[39mwith_labels\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mlabels\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m kwds\n",
+ "\u001b[1;32m--> 121\u001b[0m draw_networkx(G, pos\u001b[39m=\u001b[39;49mpos, ax\u001b[39m=\u001b[39;49max, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n",
+ "\u001b[0;32m 122\u001b[0m ax\u001b[39m.\u001b[39mset_axis_off()\n",
+ "\u001b[0;32m 123\u001b[0m plt\u001b[39m.\u001b[39mdraw_if_interactive()\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:303\u001b[0m, in \u001b[0;36mdraw_networkx\u001b[1;34m(G, pos, arrows, with_labels, **kwds)\u001b[0m\n",
+ "\u001b[0;32m 300\u001b[0m \u001b[39mif\u001b[39;00m pos \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
+ "\u001b[0;32m 301\u001b[0m pos \u001b[39m=\u001b[39m nx\u001b[39m.\u001b[39mdrawing\u001b[39m.\u001b[39mspring_layout(G) \u001b[39m# default to spring layout\u001b[39;00m\n",
+ "\u001b[1;32m--> 303\u001b[0m draw_networkx_nodes(G, pos, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mnode_kwds)\n",
+ "\u001b[0;32m 304\u001b[0m draw_networkx_edges(G, pos, arrows\u001b[39m=\u001b[39marrows, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39medge_kwds)\n",
+ "\u001b[0;32m 305\u001b[0m \u001b[39mif\u001b[39;00m with_labels:\n",
+ "\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:427\u001b[0m, in \u001b[0;36mdraw_networkx_nodes\u001b[1;34m(G, pos, nodelist, node_size, node_color, node_shape, alpha, cmap, vmin, vmax, ax, linewidths, edgecolors, label, margins)\u001b[0m\n",
+ "\u001b[0;32m 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m nodelist])\n",
+ "\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\u001b[1;32m--> 427\u001b[0m \u001b[39mraise\u001b[39;00m nx\u001b[39m.\u001b[39mNetworkXError(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNode \u001b[39m\u001b[39m{\u001b[39;00merr\u001b[39m}\u001b[39;00m\u001b[39m has no position.\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n",
+ "\u001b[0;32m 429\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(alpha, Iterable):\n",
+ "\u001b[0;32m 430\u001b[0m node_color \u001b[39m=\u001b[39m apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax)\n",
+ "\n",
+ "\u001b[1;31mNetworkXError\u001b[0m: Node 'other_steps.step1' has no position."
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from networkx import draw\n",
+ "g = pline.get_graph()[1]\n",
+ "display(g.nodes)\n",
+ "pos = tree_layout(g)\n",
+ "draw(g, pos= pos, with_labels = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "NodeView(('prefetch.step', 'other_steps.step1', 'suite2p.do_this', 'suite2p.step2', 'demoPipeClass.initial', 'other_steps.step2', 'side_step.step1', 'side_step.step2'))"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "ename": "NetworkXError",
+ "evalue": "Node 'other_steps.step1' has no position.",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:425\u001b[0m, in \u001b[0;36mdraw_networkx_nodes\u001b[1;34m(G, pos, nodelist, node_size, node_color, node_shape, alpha, cmap, vmin, vmax, ax, linewidths, edgecolors, label, margins)\u001b[0m\n\u001b[0;32m 424\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;49;00m v \u001b[39min\u001b[39;49;00m nodelist])\n\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:425\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 424\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m--> 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m nodelist])\n\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n",
+ "\u001b[1;31mKeyError\u001b[0m: 'other_steps.step1'",
+ "\nThe above exception was the direct cause of the following exception:\n",
+ "\u001b[1;31mNetworkXError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32mc:\\Users\\tjostmou\\Documents\\Python\\__packages__\\Pypelines\\pypelines\\feature_test.ipynb Cell 6\u001b[0m line \u001b[0;36m5\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m display(g\u001b[39m.\u001b[39mnodes)\n\u001b[0;32m <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m pos \u001b[39m=\u001b[39m tree_layout(g)\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/tjostmou/Documents/Python/__packages__/Pypelines/pypelines/feature_test.ipynb#W4sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m draw(g, pos\u001b[39m=\u001b[39;49m pos, with_labels \u001b[39m=\u001b[39;49m \u001b[39mTrue\u001b[39;49;00m)\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:121\u001b[0m, in \u001b[0;36mdraw\u001b[1;34m(G, pos, ax, **kwds)\u001b[0m\n\u001b[0;32m 118\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mwith_labels\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m kwds:\n\u001b[0;32m 119\u001b[0m kwds[\u001b[39m\"\u001b[39m\u001b[39mwith_labels\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mlabels\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m kwds\n\u001b[1;32m--> 121\u001b[0m draw_networkx(G, pos\u001b[39m=\u001b[39;49mpos, ax\u001b[39m=\u001b[39;49max, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwds)\n\u001b[0;32m 122\u001b[0m ax\u001b[39m.\u001b[39mset_axis_off()\n\u001b[0;32m 123\u001b[0m plt\u001b[39m.\u001b[39mdraw_if_interactive()\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:303\u001b[0m, in \u001b[0;36mdraw_networkx\u001b[1;34m(G, pos, arrows, with_labels, **kwds)\u001b[0m\n\u001b[0;32m 300\u001b[0m \u001b[39mif\u001b[39;00m pos \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 301\u001b[0m pos \u001b[39m=\u001b[39m nx\u001b[39m.\u001b[39mdrawing\u001b[39m.\u001b[39mspring_layout(G) \u001b[39m# default to spring layout\u001b[39;00m\n\u001b[1;32m--> 303\u001b[0m draw_networkx_nodes(G, pos, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mnode_kwds)\n\u001b[0;32m 304\u001b[0m draw_networkx_edges(G, pos, arrows\u001b[39m=\u001b[39marrows, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39medge_kwds)\n\u001b[0;32m 305\u001b[0m \u001b[39mif\u001b[39;00m with_labels:\n",
+ "File \u001b[1;32mc:\\Users\\tjostmou\\anaconda3\\envs\\Inflow\\Lib\\site-packages\\networkx\\drawing\\nx_pylab.py:427\u001b[0m, in \u001b[0;36mdraw_networkx_nodes\u001b[1;34m(G, pos, nodelist, node_size, node_color, node_shape, alpha, cmap, vmin, vmax, ax, linewidths, edgecolors, label, margins)\u001b[0m\n\u001b[0;32m 425\u001b[0m xy \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39masarray([pos[v] \u001b[39mfor\u001b[39;00m v \u001b[39min\u001b[39;00m nodelist])\n\u001b[0;32m 426\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m--> 427\u001b[0m \u001b[39mraise\u001b[39;00m nx\u001b[39m.\u001b[39mNetworkXError(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mNode \u001b[39m\u001b[39m{\u001b[39;00merr\u001b[39m}\u001b[39;00m\u001b[39m has no position.\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[0;32m 429\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(alpha, Iterable):\n\u001b[0;32m 430\u001b[0m node_color \u001b[39m=\u001b[39m apply_alpha(node_color, alpha, nodelist, cmap, vmin, vmax)\n",
+ "\u001b[1;31mNetworkXError\u001b[0m: Node 'other_steps.step1' has no position."
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from networkx import draw\n",
+ "g = pline.get_graph()[1]\n",
+ "display(g.nodes)\n",
+ "pos = tree_layout(g)\n",
+ "draw(g, pos= pos, with_labels = True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'initial': <demoPipeClass.initial StepObject>}"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pline.demoPipeClass.steps"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Inflow",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.11.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pypelines/pipe.py b/pypelines/pipe.py
index 8934883..28fa983 100644
--- a/pypelines/pipe.py
+++ b/pypelines/pipe.py
@@ -1,6 +1,7 @@
from . step import BaseStep
from . multisession import BaseMultisessionAccessor
from . sessions import Session
+from . disk import PickleObject
from functools import wraps
import inspect
@@ -10,37 +11,37 @@ from typing import Callable, Type, Iterable, Protocol, TYPE_CHECKING
if TYPE_CHECKING:
from .pipeline import BasePipeline
-class PipeMetaclass(type):
+# class PipeMetaclass(type):
- def __new__(cls : Type, pipe_name : str, bases : Iterable[Type], attributes : dict) -> Type:
- return super().__new__(cls, pipe_name, bases, attributes)
+# def __new__(cls : Type, pipe_name : str, bases : Iterable[Type], attributes : dict) -> Type:
+# return super().__new__(cls, pipe_name, bases, attributes)
- def __init__(cls : Type, pipe_name : str, bases : Iterable[Type], attributes : dict) -> None:
+# def __init__(cls : Type, pipe_name : str, bases : Iterable[Type], attributes : dict) -> None:
- steps = getattr(cls,"steps",{})
+# steps = getattr(cls,"steps",{})
- for name, attribute in attributes.items():
- if getattr(attribute, "is_step", False):
- steps[name] = PipeMetaclass.step_with_attributes(attribute , pipe_name , name)
+# for name, attribute in attributes.items():
+# if getattr(attribute, "is_step", False):
+# steps[name] = PipeMetaclass.step_with_attributes(attribute , pipe_name , name)
- setattr(cls,"steps",steps)
+# setattr(cls,"steps",steps)
- @staticmethod
- def step_with_attributes(step : BaseStep, pipe_name : str, step_name : str) -> BaseStep:
+# @staticmethod
+# def step_with_attributes(step : BaseStep, pipe_name : str, step_name : str) -> BaseStep:
- setattr(step, "pipe_name", pipe_name)
- setattr(step, "step_name", step_name)
+# setattr(step, "pipe_name", pipe_name)
+# setattr(step, "step_name", step_name)
- return step
+# return step
-class BasePipe(metaclass = PipeMetaclass):
+class BasePipe :#(metaclass = PipeMetaclass):
# this class must implements only the logic to link blocks together.
# It is agnostic about what way data is stored, and the way the blocks function.
# Hence it is designed to be overloaded, and cannot be used as is.
- use_versions = True
single_step = False
step_class = BaseStep
+ disk_class = PickleObject
multisession_class = BaseMultisessionAccessor
def __init__(self, parent_pipeline : "BasePipeline") -> None :
@@ -48,25 +49,36 @@ class BasePipe(metaclass = PipeMetaclass):
self.multisession = self.multisession_class(self)
self.pipeline = parent_pipeline
self.pipe_name = self.__class__.__name__
- print(self.pipe_name)
+ #print(self.pipe_name)
+
+ self.steps = {}
+ for (step_name, step) in inspect.getmembers( self , predicate = inspect.ismethod ):
+ if getattr(step, "is_step", False):
+ self.steps[step_name] = step
+
+ if len(self.steps) < 1 :
+ raise ValueError(f"You should register at least one step class with @stepmethod in {self.pipe_name} class. { self.steps = }")
if len(self.steps) > 1 and self.single_step:
- raise ValueError(f"Cannot set single_step to True if you registered more than one step inside {self.pipe_name} class")
+ raise ValueError(f"Cannot set single_step to True if you registered more than one step inside {self.pipe_name} class. { self.steps = }")
+ #if self.single_step is None :
+ # self.single_step = False if len(self.steps) > 1 else True
+
# this loop allows to populate self.steps from the now instanciated version of the step method.
# Using only instanciated version is important to be able to use self into it later,
# without confusing ourselved with uninstanciated versions in the steps dict
- for step_name, _ in self.steps.items():
- step = getattr(self , step_name) # get the instanciated step method from name.
+ for step_name, step in self.steps.items():
step = self.step_class(self.pipeline, self, step, step_name)
- self.steps[step_name] = step
+ self.steps[step_name] = step #replace the bound_method by a step_class using that bound method, so that we attach the necessary components to it.
setattr(self, step_name, step)
# attaches itself to the parent pipeline
if self.single_step :
step = list(self.steps.values())[0]
self.pipeline.pipes[self.pipe_name] = step
+ step.steps = self.steps #just add steps to this step serving as a pipe, so that it behaves similarly to a pipe for some pipelines function requiring this attribute to exist.
setattr(self.pipeline, self.pipe_name, step)
else :
self.pipeline.pipes[self.pipe_name] = self
diff --git a/pypelines/pipeline.py b/pypelines/pipeline.py
index 01af955..78c97fd 100644
--- a/pypelines/pipeline.py
+++ b/pypelines/pipeline.py
@@ -7,16 +7,20 @@ if TYPE_CHECKING:
class BasePipeline:
- pipes = {}
+ def __init__(self,name):
+ self.pipeline_name = name
+ self.pipes = {}
+ self.resolved = False
def register_pipe(self, pipe_class : type) -> type:
"""Wrapper to instanciate and attache a a class inheriting from BasePipe it to the Pipeline instance.
The Wraper returns the class without changing it."""
- pipe_class(self)
-
+ instance = pipe_class(self)
+ #print(f"Added instance of Pipe {instance.pipe_name} to instance of Pipeline {self.__class__.__name__} {self = }")
+ self.resolved = False
return pipe_class
- def resolve(self, instance_name : str) :
+ def resolve_instance(self, instance_name : str) :
pipe_name , step_name = instance_name.split(".")
try :
pipe = self.pipes[pipe_name]
@@ -25,32 +29,64 @@ class BasePipeline:
return pipe.steps[step_name]
except KeyError :
raise KeyError(f"No instance {instance_name} has been registered to the pipeline")
+
+ def resolve(self):
+ if self.resolved:
+ return
+
+ for pipe in self.pipes.values() :
+
+ for step in pipe.steps.values() :
+ instanciated_requires = []
+ for req in step.requires :
+ if isinstance(req,str):
+ req = self.resolve_instance(req)
+ instanciated_requires.append(req)
+ step.requires = instanciated_requires
+
+ self.resolved = True
def get_requirement_stack(self, instance, required_steps = None, parents = None, max_recursion = 100):
+
if required_steps is None :
required_steps = []
if parents is None:
parents = []
-
- if isinstance(instance,str):
- instance = self.resolve(instance)
-
+
+ self.resolve()
+
if instance in parents :
raise RecursionError(f"Circular import : {parents[-1]} requires {instance} wich exists in parents hierarchy : {parents}")
parents.append(instance)
if len(parents) > max_recursion :
raise ValueError("Too much recursion, unrealistic number of pipes chaining. Investigate errors or increase max_recursion")
- instanciated_requires = []
for requirement in instance.requires:
required_steps, requirement = self.get_requirement_stack(requirement, required_steps, parents, max_recursion)
if not requirement in required_steps:
required_steps.append(requirement)
- instanciated_requires.append(requirement)
-
- instance.requires = instanciated_requires
+
parents.pop(-1)
- return required_steps, instance
\ No newline at end of file
+ return required_steps, instance
+
+ def get_graph(self):
+ from networkx import DiGraph
+
+ self.resolve()
+
+ callable_graph = DiGraph()
+ display_graph = DiGraph()
+ for pipe in self.pipes.values() :
+
+ for step in pipe.steps.values() :
+ callable_graph.add_node(step)
+ display_graph.add_node(step.full_name)
+ for req in step.requires :
+ callable_graph.add_edge(req, step)
+ display_graph.add_edge(req.full_name, step.full_name)
+
+ return callable_graph, display_graph
+
\ No newline at end of file
diff --git a/pypelines/step.py b/pypelines/step.py
index e2caa58..fd32cc2 100644
--- a/pypelines/step.py
+++ b/pypelines/step.py
@@ -8,37 +8,50 @@ from typing import Callable, Type, Iterable, Protocol, TYPE_CHECKING
if TYPE_CHECKING:
from .pipeline import BasePipeline
from .pipe import BasePipe
+ from .disk import BaseDiskObject
-def stepmethod(requires = [], version = None):
+
+def stepmethod(requires=[], version=None):
# This method allows to register class methods inheriting of BasePipe as steps.
# It basically just step an "is_step" stamp on the method that are defined as steps.
# This stamp will later be used in the metaclass __new__ to set additionnal usefull attributes to those methods
def registrate(function):
-
function.requires = [requires] if not isinstance(requires, list) else requires
function.is_step = True
function.use_version = False if version is None else True
function.version = version
return function
+
return registrate
-class BaseStep:
- def __init__(self, pipeline : "BasePipeline", pipe : "BasePipe", step : "BaseStep", step_name : str):
- self.pipeline = pipeline # save an instanciated access to the pipeline parent
- self.pipe = pipe # save an instanciated access to the pipe parent
- self.step = step # save an instanciated access to the step function (undecorated)
+class BaseStep:
+ def __init__(
+ self,
+ pipeline: "BasePipeline",
+ pipe: "BasePipe",
+ step: "BaseStep",
+ step_name: str,
+ ):
+ self.pipeline = pipeline # save an instanciated access to the pipeline parent
+ self.pipe = pipe # save an instanciated access to the pipe parent
+ self.step = (
+ step # save an instanciated access to the step function (undecorated)
+ )
self.pipe_name = pipe.pipe_name
self.step_name = step_name
+ self.full_name = f"{self.pipe_name}.{self.step_name}"
self.use_version = self.step.use_version
self.version = self.step.version
self.single_step = self.pipe.single_step
self.requires = self.step.requires
self.is_step = True
-
- self.requirement_stack = partial( self.pipeline.get_requirement_stack, instance = self )
- self.step_version = partial( self.pipe.step_version, step = self )
+
+ self.requirement_stack = partial(
+ self.pipeline.get_requirement_stack, instance=self
+ )
+ # self.step_version = partial( self.pipe.step_version, step = self )
update_wrapper(self, self.step)
self._make_wrapped_functions()
@@ -55,39 +68,51 @@ class BaseStep:
self.make_wrapped_generate()
def make_wrapped_save(self):
- self.save = self.pipe.dispatcher(self._version_wrapper(self.pipe.file_saver))
-
+ def wrapper(session, data, extra=""):
+ disk_object = self.pipe.disk_class(session, self, extra=extra)
+ disk_object.check_disk()
+ return disk_object.save(data)
+
+ self.save = self.pipe.dispatcher(wrapper)
+
def make_wrapped_load(self):
- self.load = self.pipe.dispatcher(self._version_wrapper(self.pipe.file_loader))
-
+ def wrapper(session, extra=""):
+ disk_object = self.pipe.disk_class(session, self, extra=extra)
+ disk_object.check_disk()
+ return disk_object.load()
+
+ self.load = self.pipe.dispatcher(wrapper)
+
def make_wrapped_generate(self):
- self.generate = loggedmethod(
- self._version_wrapper(
- self.pipe.dispatcher(
- self._load_or_generate_wrapper(
- self._save_after_generate_wrapper(
- self.pipe.pre_run_wrapper(self.step)
- )
- )
- )
+ def wrapper(session, *args, extra="", **kwargs):
+ disk_object = self.pipe.disk_class(session, self, extra=extra)
+ return loggedmethod(
+ self._load_or_generate_wrapper(
+ self._save_after_generate_wrapper(
+ self.pipe.pre_run_wrapper(self.step), disk_object
+ ),
+ disk_object,
)
- )
-
+ )(session, *args, extra = extra, **kwargs)
+ self.generate = self.pipe.dispatcher(wrapper)
def step_current_version(self) -> str:
- #simply returns the current string of the version that is in the config file.
+ # simply returns the current string of the version that is in the config file.
return "version"
...
def _version_wrapper(self, function):
@wraps(function)
- def wrapper(*args,**kwargs):
+ def wrapper(*args, **kwargs):
version = self.step_current_version(self)
return function(*args, version=version, **kwargs)
+
return wrapper
- def _load_or_generate_wrapper(self, function: Callable):
+ def _load_or_generate_wrapper(
+ self, function: Callable, disk_object: "BaseDiskObject"
+ ):
"""
Decorator to load instead of calculating if not refreshing and saved data exists
"""
@@ -114,7 +139,7 @@ class BaseStep:
kwargs = kwargs.copy()
extra = kwargs.get("extra", "")
- version = kwargs.get("version", "")
+ # version = kwargs.get("version", "")
skipping = kwargs.pop("skip", False)
# we raise if file not found only if skipping is True
refresh = kwargs.get("refresh", False)
@@ -136,35 +161,38 @@ class BaseStep:
)
if not refresh:
- if skipping and self.pipe.file_checker(session_details, extra=extra, version=version):
- logger.load_info(
+ if disk_object.check_disk() and skipping:
+ logger.info(
f"File exists for {self.pipe_name}{'.' + extra if extra else ''}. Loading and processing have been skipped"
)
return None
logger.debug(f"Trying to load saved data")
try:
- result = self.pipe.file_loader(session_details, extra=extra, version=version)
- logger.load_info(
+ result = (
+ disk_object.load()
+ ) # self.pipe.file_loader(session_details, extra=extra, version=version)
+ logger.info(
f"Found and loaded {self.pipe_name}{'.' + extra if extra else ''} file. Processing has been skipped "
)
return result
except IOError:
- logger.load_info(
+ logger.info(
f"Could not find or load {self.pipe_name}{'.' + extra if extra else ''} saved file."
)
- logger.load_info(
+ logger.info(
f"Performing the computation to generate {self.pipe_name}{'.' + extra if extra else ''}. Hold tight."
)
return function(session_details, *args, **kwargs)
return wrap
- def _save_after_generate_wrapper(self, function: Callable):
+ def _save_after_generate_wrapper(
+ self, function: Callable, disk_object: "BaseDiskObject"
+ ):
# decorator to load instead of calculating if not refreshing and saved data exists
@wraps(function)
def wrap(session, *args, **kwargs):
-
logger = logging.getLogger("save_pipeline")
kwargs = kwargs.copy()
@@ -176,11 +204,12 @@ class BaseStep:
if session is not None:
if save_pipeline:
# we overwrite inside saver, if file exists and save_pipeline is True
- self.pipe.file_saver(session, result, extra=extra, version=version)
+ disk_object.save(result)
+ # self.pipe.file_saver(session, result, extra=extra, version=version)
else:
logger.warning(
f"Cannot guess data saving location for {self.pipe_name}: 'session_details' argument must be supplied."
)
return result
- return wrap
\ No newline at end of file
+ return wrap
diff --git a/scripts/self_install_local_editmode.cmd b/scripts/self_install_local_editmode.cmd
new file mode 100644
index 0000000..a552cdc
--- /dev/null
+++ b/scripts/self_install_local_editmode.cmd
@@ -0,0 +1,4 @@
+call conda activate Inflow
+cd ..
+pip install -e .
+PAUSE
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 96e1e45..52084d4 100644
--- a/setup.py
+++ b/setup.py
@@ -18,14 +18,14 @@ def get_version(rel_path):
raise RuntimeError('Unable to find version string.')
setup(
- name= 'analines',
+ name= 'pypelines',
version= get_version(Path('pypelines', '__init__.py')),
packages=find_packages(),
url= 'https://gitlab.pasteur.fr/haisslab/data-management/pypelines',
license= 'MIT',
author= 'Timothé Jost-MOUSSEAU',
author_email= 'timothe.jost-mousseau@pasteur.com',
- description= 'Framework to organize pprocessing files outputs.',
+ description= 'Framework to organize processing code outputs to/from disk, processing chaining and versionning with a common easy to use api',
classifiers=[
'Development Status :: 3 - Alpha',
'Intended Audience :: Developers',
@@ -39,10 +39,6 @@ setup(
'Programming Language :: Python :: 3.11',
],
install_requires=[
- "numpy>=1.23",
- "opencv-python>=4.6",
- "ffmpeg>=1.4",
- "tifffile>=2022.10"
],
entry_points={},
scripts={},
diff --git a/tests/__pycache__/tests.cpython-311.pyc b/tests/__pycache__/tests.cpython-311.pyc
index b7fd70566d32d16f28c1d314b77bf84174b64f1a..0ea4c0a8ee5a68e3022394f6a8c3bd49ac3e10a5 100644
GIT binary patch
delta 19
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delta 19
ZcmaFL|CFC=IWI340}wo8+Q@aA9RN4I1w#M;
--
GitLab