upgrade bokeh to v3

Author: bjarthur

README.md

@@ -462,7 +462,8 @@ Workstation](#another-workstation) to specify the path to the SongExplorer
 executable.  If modifying your .bashrc file to export a modified PATH is not an
 option, then specify the `-env` flag in your configuration.py:
 
-	cluster_cmd="bsub -env 'all, PATH=<path-to-unzipped-executable>/songexplorer/bin:<paths-to-everything-else>'"
+    cluster_cmd="bsub -env 'all, \
+        PATH=<path-to-unzipped-executable>/songexplorer/bin:<paths-to-everything-else>'"
 
 
 # Tutorial #
@@ -1714,7 +1715,10 @@ documentation showing how to call it.  Here, for example, is the interface for
     # e.g.
     # time-freq-threshold.py \
     #     --filename=`pwd`/groundtruth-data/round2/20161207T102314_ch1_p1.wav \
-    #     --parameters={"time_sigma":"9,4", "time_smooth_ms":"6.4", "frequency_n_ms":"25.6", "frequency_nw":"4", "frequency_p":"0.1,1.0", "frequency_smooth_ms":"25.6", "time_sigma_robust":"median"} \
+    #     --parameters={"time_sigma":"9,4", "time_smooth_ms":"6.4", \
+                        "frequency_n_ms":"25.6", "frequency_nw":"4", \
+                        "frequency_p":"0.1,1.0", "frequency_smooth_ms":"25.6", \
+                        "time_sigma_robust":"median"} \
     #     --audio_tic_rate=2500 \
     #     --audio_nchannels=1 \
     #     --audio_read_plugin=load-wav \
@@ -2000,7 +2004,8 @@ library, set XLA_FLAGS in your environment, and install cuda-nvcc:
     $ conda activate songexplorer
     $ mkdir -p $CONDA_PREFIX/lib/nvvm/libdevice/
     $ cp -p $CONDA_PREFIX/lib/libdevice.10.bc $CONDA_PREFIX/lib/nvvm/libdevice/
-    $ echo 'export XLA_FLAGS=--xla_gpu_cuda_data_dir=$CONDA_PREFIX/lib' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
+    $ echo 'export XLA_FLAGS=--xla_gpu_cuda_data_dir=$CONDA_PREFIX/lib' \
+            >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
     $ conda install -c nvidia cuda-nvcc
 
 To upload to the [Janelia forge](https://anaconda.org/janelia):
@@ -2035,9 +2040,10 @@ asset:
 To upload a tarball to Github, compress the conda environment and drag and drop
 it into the assets section of the releases page:
 
-    $ cd $CONDA_PREFIX/envs
+    $ cd $CONDA_PREFIX/..
     $ tar czf songexplorer-<version>-<architecture>.tar.gz songexplorer
-    $ cat songexplorer-<version>-<architecture>.tar.gz | split --bytes=2GB - songexplorer-<version>-<architecture>.tar.gz.
+    $ cat songexplorer-<version>-<architecture>.tar.gz | \
+          split --bytes=2GB - songexplorer-<version>-<architecture>.tar.gz.
 
 After downloading, some users will need to re-install some pip dependencies
 (e.g. tensorflow-metal on MacOS) as they are not in general relocatable:
@@ -2075,7 +2081,8 @@ To build an image, change to a local (i.e. not NFS mounted; e.g.
 
     $ git clone https://github.com/JaneliaSciComp/SongExplorer.git
     $ rm -rf songexplorer/.git
-    $ sudo singularity build -s [-B /opt:/mnt] [--no-cleanup] songexplorer.img songexplorer/install/singularity.def
+    $ sudo singularity build -s [-B /opt:/mnt] [--no-cleanup] \
+          songexplorer.img songexplorer/install/singularity.def
 
 To confirm that the image works:
 

configuration.py

@@ -33,7 +33,7 @@
 
 # GUI
 gui_nlabels=7
-gui_gui_width_pix=1250
+gui_gui_width_pix=1350
 #gui_label_palette="Category10_10"
 # https://graphicdesign.stackexchange.com/questions/3682/where-can-i-find-a-large-palette-set-of-contrasting-colors-for-coloring-many-d
 gui_label_palette="('#0075dc','#993f00','#4c005c','#191919','#005c31','#2bce48','#ffcc99','#808080','#94ffb5','#8f7c00','#9dcc00','#c20088','#003380','#ffa405','#ffa8bb','#426600','#ff0010','#5ef1f2','#00998f','#e0ff66','#740aff','#990000','#ffff80','#ffff00','#ff5005')"

install/conda/songexplorer/meta.yaml

@@ -19,7 +19,7 @@ requirements:
     - cudatoolkit=11  # [win]
     - cudnn=8  # [win]
     - cuda-nvcc=11  # [win]
-    - bokeh >=2,<3
+    - bokeh
     - matplotlib-base
     - natsort 
     - scikit-image
@@ -30,7 +30,7 @@ requirements:
     - portion
     - matplotlib-venn
     - psutil
-    - nodejs >=10.13.10
+    - nodejs >=18
     - aitch >=0.2.2
     - opentsne
     - libssh2 [win]

src/UMAP.py

@@ -25,14 +25,14 @@
 
 def _callback(p,M,V,C):
     C.time.sleep(0.5)
-    V.cluster_parameters[p].css_classes = []
+    V.cluster_parameters[p].stylesheets = [""]
     M.save_state_callback()
     V.buttons_update()
 
 def pca_fraction_callback(n,M,V,C):
     pca_fraction = float(V.cluster_parameters['pca-fraction'].value)
     if not 0 < pca_fraction <= 1:
-        V.cluster_parameters['pca-fraction'].css_classes = ['changed']
+        V.cluster_parameters['pca-fraction'].stylesheets = M.changed_style
         V.cluster_parameters['pca-fraction'].value = str(min(1, max(0, pca_fraction)))
         bokehlog.info("WARNING:  `PCA fraction` must be between 0 and 1")
         if V.bokeh_document:
@@ -43,7 +43,7 @@ def pca_fraction_callback(n,M,V,C):
 def positive_callback(key,n,M,V,C):
     value = float(V.cluster_parameters[key].value)
     if value <= 0:
-        V.cluster_parameters[key].css_classes = ['changed']
+        V.cluster_parameters[key].stylesheets = M.changed_style
         V.cluster_parameters[key].value = "1"
         bokehlog.info("WARNING:  `"+key+"` must be positive")
         if V.bokeh_document:

src/architecture-plugin.py

@@ -11,17 +11,17 @@
 # optional callbacks can be used to validate user input
 def _callback(p,M,V,C):
     C.time.sleep(0.5)
-    V.model_parameters[p].css_classes = []
+    V.model_parameters[p].stylesheets = []
     M.save_state_callback()
     V.buttons_update()
 
 def callback(n,M,V,C):
     # M, V, C are the model, view, and controller in src/gui
     # access the hyperparameters below with the V.model_parameters dictionary
-    # the value is stored in .value, and the appearance can be controlled with .css_classes
+    # the value is stored in .value, and the appearance can be controlled with .stylesheets
     if int(V.model_parameters['a-bounded-value'].value) < 0:
         #bokehlog.info("a-bounded-value = "+str(V.model_parameters['a-bounded-value'].value))  # uncomment to debug
-        V.model_parameters['a-bounded-value'].css_classes = ['changed']
+        V.model_parameters['a-bounded-value'].stylesheets = [".bk-input { background-color: #FFA500; }"]
         V.model_parameters['a-bounded-value'].value = "0"
         if V.bokeh_document:  # if interactive
             V.bokeh_document.add_next_tick_callback(lambda: _callback('a-bounded-value',M,V,C))

src/autoencoder.py

@@ -13,7 +13,7 @@
 def _callback(ps,M,V,C):
     C.time.sleep(0.5)
     for p in ps:
-        V.model_parameters[p].css_classes = []
+        V.model_parameters[p].stylesheets = [""]
     M.save_state_callback()
     V.buttons_update()
 
@@ -22,15 +22,15 @@ def window_callback(n,M,V,C):
     changed, window_sec2 = M.next_pow2_sec(float(V.model_parameters['window'].value) * M.time_scale)
     if changed:
         bokehlog.info("WARNING:  adjusting `window ("+M.time_units+")` to be a power of two in tics")
-        V.model_parameters['window'].css_classes = ['changed']
+        V.model_parameters['window'].stylesheets = M.changed_style
         V.model_parameters['window'].value = str(window_sec2 / M.time_scale)
         ps.append('window')
     mel, _ = V.model_parameters['mel_dct'].value.split(',')
     nfreqs = round(window_sec2*M.audio_tic_rate/2+1)
     if int(mel) != nfreqs:
         changed=True
         bokehlog.info("WARNING:  adjusting `mel & DCT` to both be equal to the number of frequencies")
-        V.model_parameters['mel_dct'].css_classes = ['changed']
+        V.model_parameters['mel_dct'].stylesheets = M.changed_style
         V.model_parameters['mel_dct'].value = str(nfreqs)+','+str(nfreqs)
         ps.append('mel_dct')
     if changed:
@@ -70,7 +70,7 @@ def stride_callback(n,M,V,C):
                 else:
                     stride_sec2 = "-1"
                     bokehlog.info("ERROR:  downsampling achieved by `stride after` is prime")
-        V.model_parameters['stride'].css_classes = ['changed']
+        V.model_parameters['stride'].stylesheets = M.changed_style
         V.model_parameters['stride'].value = str(stride_sec2 * M.time_scale)
         if V.bokeh_document:
             V.bokeh_document.add_next_tick_callback(lambda: _callback(['stride_sec'],M,V,C))
@@ -84,7 +84,7 @@ def mel_dct_callback(n,M,V,C):
     mel, dct = V.model_parameters['mel_dct'].value.split(',')
     if int(dct) > int(mel):
         bokehlog.info("WARNING:  adjusting `mel & DCT` such that DCT is less than or equal to mel")
-        V.model_parameters['mel_dct'].css_classes = ['changed']
+        V.model_parameters['mel_dct'].stylesheets = M.changed_style
         V.model_parameters['mel_dct'].value = mel+','+mel
         if V.bokeh_document:
             V.bokeh_document.add_next_tick_callback(lambda: _callback(['mel_dct'],M,V,C))
@@ -103,7 +103,7 @@ def range_callback(n,M,V,C):
     if lo > hi:  lo=0;
     if V.model_parameters['range'].value != str(lo/M.freq_scale)+'-'+str(hi/M.freq_scale):
         bokehlog.info("WARNING:  adjusting `range ("+M.freq_units+")` such that lower bound is not negative and the higher bound less than the Nyquist frequency.")
-        V.model_parameters['range'].css_classes = ['changed']
+        V.model_parameters['range'].stylesheets = M.changed_style
         V.model_parameters['range'].value = str(lo/M.freq_scale)+'-'+str(hi/M.freq_scale)
         if V.bokeh_document:
             V.bokeh_document.add_next_tick_callback(lambda: _callback(['range'],M,V,C))
@@ -124,7 +124,7 @@ def dilate_stride_callback(key,n,M,V,C):
     dilate = set(dilate_time + dilate_freq)
     if stride & dilate:
         bokehlog.info("WARNING:  adjusting `"+key+"` so that the convolutional layers with strides do not overlap with those that dilate")
-        V.model_parameters[key].css_classes = ['changed']
+        V.model_parameters[key].stylesheets = M.changed_style
         tmp = set(parse_layers(V.model_parameters[key].value, nconvlayers))
         V.model_parameters[key].value = esrap_layers(list(tmp - (stride & dilate)), nconvlayers)
         if V.bokeh_document:
@@ -141,7 +141,7 @@ def dilate_stride_callback(key,n,M,V,C):
 #        while downsampled_rate != round(downsampled_rate):
 #            stride_time.pop()
 #            downsampled_rate = M.audio_tic_rate / 2 ** min(nconvlayers, len(stride_time))
-#        V.model_parameters['stride_time'].css_classes = ['changed']
+#        V.model_parameters['stride_time'].stylesheets = M.changed_style
 #        V.model_parameters['stride_time'].value = esrap_layers(stride_time, nconvlayers)
 #        if V.bokeh_document:
 #            V.bokeh_document.add_next_tick_callback(lambda: _callback(['stride_time'],M,V,C))
@@ -159,7 +159,7 @@ def dilate_stride_callback(key,n,M,V,C):
 #            downsampled_rate = M.audio_tic_rate / 2 ** min(this_nconvlayers, len(stride_time))
 #            if downsampled_rate == round(downsampled_rate):
 #                break
-#        V.model_parameters['nconvlayers'].css_classes = ['changed']
+#        V.model_parameters['nconvlayers'].stylesheets = M.changed_style
 #        V.model_parameters['nconvlayers'].value = str(this_nconvlayers)
 #        if V.bokeh_document:
 #            V.bokeh_document.add_next_tick_callback(lambda: _callback(['nconvlayers'],M,V,C))

src/cluster-plugin.py

@@ -18,17 +18,17 @@
 # optional callbacks can be used to validate user input
 def _callback(p,M,V,C):
     C.time.sleep(0.5)
-    V.cluster_parameters[p].css_classes = []
+    V.cluster_parameters[p].stylesheets = [""]
     M.save_state_callback()
     V.buttons_update()
 
 def callback(n,M,V,C):
     # M, V, C are the model, view, and controller in src/gui
     # access the hyperparameters below with the V.detect_parameters dictionary
-    # the value is stored in .value, and the appearance can be controlled with .css_classes
+    # the value is stored in .value, and the appearance can be controlled with .stylesheets
     if int(V.detect_parameters['a-bounded-value'].value) < 0:
         #bokehlog.info("a-bounded-value = "+str(V.detect_parameters['a-bounded-value'].value))  # uncomment to debug
-        V.detect_parameters['a-bounded-value'].css_classes = ['changed']
+        V.detect_parameters['a-bounded-value'].stylesheets = [".bk-input { background-color: #FFA500; }"]
         V.detect_parameters['a-bounded-value'].value = "0"
         if V.bokeh_document:  # if interactive V.bokeh_document.add_next_tick_callback(lambda: _callback('a-bounded-value',M,V,C))
             V.bokeh_document.add_next_tick_callback(lambda: _callback('a-bounded-value',M,V,C))

src/convolutional.py

@@ -13,7 +13,7 @@
 def _callback(ps,M,V,C):
     C.time.sleep(0.5)
     for p in ps:
-        V.model_parameters[p].css_classes = []
+        V.model_parameters[p].stylesheets = [""]
     M.save_state_callback()
     V.buttons_update()
 
@@ -22,15 +22,15 @@ def window_callback(n,M,V,C):
     changed, window_sec2 = M.next_pow2_sec(float(V.model_parameters['window'].value) * M.time_scale)
     if changed:
         bokehlog.info("WARNING:  adjusting `window ("+M.time_units+")` to be a power of two in tics")
-        V.model_parameters['window'].css_classes = ['changed']
+        V.model_parameters['window'].stylesheets = M.changed_style
         V.model_parameters['window'].value = str(window_sec2 / M.time_scale)
         ps.append('window')
     mel, _ = V.model_parameters['mel_dct'].value.split(',')
     nfreqs = round(window_sec2*M.audio_tic_rate/2+1)
     if int(mel) != nfreqs:
         changed=True
         bokehlog.info("WARNING:  adjusting `mel & DCT` to both be equal to the number of frequencies")
-        V.model_parameters['mel_dct'].css_classes = ['changed']
+        V.model_parameters['mel_dct'].stylesheets = M.changed_style
         V.model_parameters['mel_dct'].value = str(nfreqs)+','+str(nfreqs)
         ps.append('mel_dct')
     if changed:
@@ -70,7 +70,7 @@ def stride_callback(n,M,V,C):
                 else:
                     stride_sec2 = "-1"
                     bokehlog.info("ERROR:  downsampling achieved by `stride after` is prime")
-        V.model_parameters['stride'].css_classes = ['changed']
+        V.model_parameters['stride'].stylesheets = M.changed_style
         V.model_parameters['stride'].value = str(stride_sec2 * M.time_scale)
         if V.bokeh_document:
             V.bokeh_document.add_next_tick_callback(lambda: _callback(['stride_sec'],M,V,C))
@@ -84,7 +84,7 @@ def mel_dct_callback(n,M,V,C):
     mel, dct = V.model_parameters['mel_dct'].value.split(',')
     if int(dct) > int(mel):
         bokehlog.info("WARNING:  adjusting `mel & DCT` such that DCT is less than or equal to mel")
-        V.model_parameters['mel_dct'].css_classes = ['changed']
+        V.model_parameters['mel_dct'].stylesheets = M.changed_style
         V.model_parameters['mel_dct'].value = mel+','+mel
         if V.bokeh_document:
             V.bokeh_document.add_next_tick_callback(lambda: _callback(['mel_dct'],M,V,C))
@@ -103,7 +103,7 @@ def range_callback(n,M,V,C):
     if lo > hi:  lo=0;
     if V.model_parameters['range'].value != str(lo/M.freq_scale)+'-'+str(hi/M.freq_scale):
         bokehlog.info("WARNING:  adjusting `range ("+M.freq_units+")` such that lower bound is not negative and the higher bound less than the Nyquist frequency.")
-        V.model_parameters['range'].css_classes = ['changed']
+        V.model_parameters['range'].stylesheets = M.changed_style
         V.model_parameters['range'].value = str(lo/M.freq_scale)+'-'+str(hi/M.freq_scale)
         if V.bokeh_document:
             V.bokeh_document.add_next_tick_callback(lambda: _callback(['range'],M,V,C))
@@ -124,7 +124,7 @@ def dilate_stride_callback(key,n,M,V,C):
     dilate = set(dilate_time + dilate_freq)
     if stride & dilate:
         bokehlog.info("WARNING:  adjusting `"+key+"` so that the convolutional layers with strides do not overlap with those that dilate")
-        V.model_parameters[key].css_classes = ['changed']
+        V.model_parameters[key].stylesheets = M.changed_style
         tmp = set(parse_layers(V.model_parameters[key].value, nconvlayers))
         V.model_parameters[key].value = esrap_layers(list(tmp - (stride & dilate)), nconvlayers)
         if V.bokeh_document:
@@ -141,7 +141,7 @@ def stride_time_callback(n,M,V,C):
         while downsampled_rate != round(downsampled_rate):
             stride_time.pop()
             downsampled_rate = M.audio_tic_rate / 2 ** min(nconvlayers, len(stride_time))
-        V.model_parameters['stride_time'].css_classes = ['changed']
+        V.model_parameters['stride_time'].stylesheets = M.changed_style
         V.model_parameters['stride_time'].value = esrap_layers(stride_time, nconvlayers)
         if V.bokeh_document:
             V.bokeh_document.add_next_tick_callback(lambda: _callback(['stride_time'],M,V,C))
@@ -159,7 +159,7 @@ def nlayers_callback(n,M,V,C):
             downsampled_rate = M.audio_tic_rate / 2 ** min(this_nconvlayers, len(stride_time))
             if downsampled_rate == round(downsampled_rate):
                 break
-        V.model_parameters['nconvlayers'].css_classes = ['changed']
+        V.model_parameters['nconvlayers'].stylesheets = M.changed_style
         V.model_parameters['nconvlayers'].value = str(this_nconvlayers)
         if V.bokeh_document:
             V.bokeh_document.add_next_tick_callback(lambda: _callback(['nconvlayers'],M,V,C))