Merge latest upstream changes

.github/workflows/run_tests.yml

@@ -30,6 +30,8 @@ jobs:
           activate-environment: caiman
           conda-solver: libmamba
           miniforge-version: latest
+          channels: conda-forge
+          conda-remove-defaults: "true"
 
       - name: Install OS Dependencies
         shell: bash -l {0}

README.md

@@ -16,7 +16,7 @@ There are two primary ways to install Caiman.
 The easiest route is to install the miniforge distribution of Anaconda, and use that to install the rest using prebuilt packages. Most users should take this path.
 
 ## Route B
-The alternative route is to make sure you have a working compiler, create a python virtualenv, grab the caiman sources, and use pip to populate the virtualenv and build Caiman. This route is not as tested and is not presently documented; it is a standard pip-based install.
+The alternative route is to make sure you have a working compiler, create a python virtualenv, grab the caiman sources, and use pip to populate the virtualenv and build Caiman. This route is not as tested and is not presently documented; it is a standard pip-based install (although it will invoke your C++ compiler to build some components).
 
 # Quick start (Route A)
 Follow these three steps to get started quickly, from installation to working through a demo notebook. If you do not already have conda installed, [you can find it here](https://github.com/conda-forge/miniforge). The miniforge distribution of conda is preferred; it will require fewer steps and likely encounter fewer issues. If you are using a different distro of conda, you will likely need to add `-c conda-forge` to the commands you use to make your environment.
@@ -62,6 +62,11 @@ Jupyter will open. Navigate to demos/notebooks/ and click on `demo_pipeline.ipyn
 
 > `<your home>` in the first line is your home directory, its location depdnding on your OS/computer. On Linux/Mac it is `~` while on Windows it will be something like `C:\Users\your_user_name\` 
 
+# Quick Start (Route B)
+This differs from the quick start above in two ways:
+* For the first step only, go to [this doc](https://github.com/flatironinstitute/CaImAn/blob/main/docs/source/Installation.rst) and run through the parts of section 1B relevant to your operating system. After that, steps 2 and onward are the same
+* You will probably want to manually set some environment variables before any use of caiman; see [here](https://github.com/conda-forge/caiman-feedstock/blob/main/recipe/activate.sh) for a Linux/OSX example, or [here](https://github.com/conda-forge/caiman-feedstock/blob/main/recipe/activate.bat) for a Windows example. Either make a note of this or modify your dotfiles/configuration to do it for you.
+
 ## For installation help
 Caiman should install easily on Linux, Mac, and Windows. If you run into problems, we have a dedicated [installation page](./docs/source/Installation.rst). If you don't find what you need there, [create an issue](https://github.com/flatironinstitute/Caiman/issues) on GitHub.
 
@@ -163,7 +168,6 @@ Special thanks to the following people for letting us use their datasets in demo
 
 Also a special thanks to:
 * Eric Thompson, for various strong contributions to code and demos, both before and during his employment at the Flatiron Institute.
-* Cai Changjia, for Volpy
 * Ethan Blackwood, for several contributions in various areas
 
 # License

VERSION

@@ -1 +1 @@
-1.11.5
+1.12.2

bin/caiman_gui.py

@@ -16,6 +16,23 @@
 from caiman.source_extraction.cnmf.cnmf import load_CNMF
 from caiman.source_extraction.cnmf.params import CNMFParams
 
+##############################
+# This is a tool that can help you visualise dumps of the CNMF object,
+# generated as demonstrated in demo_pipeline.
+#
+# If you're already using it, that's fine, but there are better tools
+# out there (such as the Mesmerize-vis package). To use, point it at the
+# hdf5 file that CLI demo or jupyter notebook made, or have your own code
+# call the save method on the CNMF object, and then feed that (and then the
+# mmap file) into this script when it asks, and you'll see your ROIs and
+# be able to experiment with thresholding (and re-save with those different
+# thresholds).
+#
+# mesmerize-vis offers a better approach to these things, and is much more
+# recent and better maintained.
+
+
+
 # Always start by initializing Qt (only once per application)
 app = QtWidgets.QApplication([])
 
@@ -66,7 +83,7 @@ def make_color_img(img, gain=255, min_max=None, out_type=np.uint8):
     M = FileDialog()
     d, f = os.path.split(cnm_obj.mmap_file)
     cnm_obj.mmap_file = M.getOpenFileName(caption='Load memory mapped file',
-                                          directory=d, filter=f + ';;*.mmap')[0]
+                                          filter=f + ';;*.mmap')[0]
 
 if fpath[-3:] == 'nwb':
     mov = caiman.load(cnm_obj.mmap_file,
@@ -405,23 +422,23 @@ def move(event):
 
 
 ## PARAMS
-params = [{'name': 'min_cnn_thr', 'type': 'float', 'value': 0.99, 'limits': (0, 1),'step':0.01},
-            {'name': 'cnn_lowest', 'type': 'float', 'value': 0.1, 'limits': (0, 1),'step':0.01},
-            {'name': 'rval_thr', 'type': 'float', 'value': 0.85, 'limits': (-1, 1),'step':0.01},
-            {'name': 'rval_lowest', 'type': 'float', 'value': -1, 'limits': (-1, 1),'step':0.01},
-            {'name': 'min_SNR', 'type': 'float', 'value': 2, 'limits': (0, 20),'step':0.1},
-            {'name': 'SNR_lowest', 'type': 'float', 'value': 0, 'limits': (0, 20),'step':0.1},
-            {'name': 'RESET', 'type': 'action'},
+params = [{'name': 'min_cnn_thr',       'type': 'float', 'value': 0.99, 'limits': (0, 1),  'step':0.01},
+            {'name': 'cnn_lowest',      'type': 'float', 'value': 0.1,  'limits': (0, 1),  'step':0.01},
+            {'name': 'rval_thr',        'type': 'float', 'value': 0.85, 'limits': (-1, 1), 'step':0.01},
+            {'name': 'rval_lowest',     'type': 'float', 'value': -1,   'limits': (-1, 1), 'step':0.01},
+            {'name': 'min_SNR',         'type': 'float', 'value': 2,    'limits': (0, 20), 'step':0.1},
+            {'name': 'SNR_lowest',      'type': 'float', 'value': 0,    'limits': (0, 20), 'step':0.1},
+            {'name': 'RESET',           'type': 'action'},
             {'name': 'SHOW BACKGROUND', 'type': 'action'},
-            {'name': 'SHOW NEURONS', 'type': 'action'}
+            {'name': 'SHOW NEURONS',    'type': 'action'}
             ]
 
 ## Create tree of Parameter objects
 pars = Parameter.create(name='params', type='group', children=params) 
 
 
 params_action = [{'name': 'Filter components', 'type': 'bool', 'value': True, 'tip': "Filter components"},          
-                 {'name': 'View components', 'type': 'list', 'values': ['All','Accepted',
+                 {'name': 'View components',   'type': 'list', 'limits': ['All','Accepted',
                                                        'Rejected', 'Unassigned'], 'value': 'All'},
                  {'name': 'ADD GROUP', 'type': 'action'},
                  {'name': 'REMOVE GROUP', 'type': 'action'},

caiman/__init__.py

@@ -1,10 +1,11 @@
 #!/usr/bin/env python
 
-import pkg_resources
+import importlib.metadata
+
 from caiman.base.movies import movie, load, load_movie_chain, _load_behavior, play_movie
 from caiman.base.timeseries import concatenate
 from caiman.cluster import start_server, stop_server
 from caiman.mmapping import load_memmap, save_memmap, save_memmap_each, save_memmap_join
 from caiman.summary_images import local_correlations
 
-__version__ = pkg_resources.get_distribution('caiman').version
+__version__ = importlib.metadata.version('caiman')