dsb-lab · gatocor · Oct 5, 2025 · Jul 4, 2024 · Jul 9, 2024 · Jul 10, 2024
diff --git a/Manifest.toml b/Manifest.toml
diff --git a/Project.toml b/Project.toml
@@ -1,16 +1,15 @@
 name = "CellBasedModels"
 uuid = "388cb286-f2b1-4654-a3bb-2e137a39c658"
 authors = ["Gabriel Torregrosa Cortés"]
-version = "0.1.0"
+version = "0.1.1"
 
 [deps]
+Accessors = "7d9f7c33-5ae7-4f3b-8dc6-eff91059b697"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
-GLMakie = "e9467ef8-e4e7-5192-8a1a-b1aee30e663a"
-GeometryBasics = "5c1252a2-5f33-56bf-86c9-59e7332b4326"
 JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
@@ -22,19 +21,17 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 
 [compat]
-julia = "1.8"
 CSV = "0.10, 1"
-CUDA = "4"
+CUDA = "4, 5"
 DataFrames = "1, 2"
 DifferentialEquations = "7"
 Distributions = "0.25, 1"
-GLMakie = "0.8"
-GeometryBasics = "0.4"
 JLD2 = "0.4, 1"
 MacroTools = "0.5"
 OrderedCollections = "1"
 ProgressMeter = "1.7"
 SpecialFunctions = "2"
+julia = "1.8"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

diff --git a/docker/README.md b/docker/README.md
@@ -2,47 +2,60 @@
 
 The dockerfile makes an image that contains a set of relevant packages for the use with CellBasedModels. A list can be seen in `environment.yaml`.
 
-Constructed images are already deposited in [dsblab/cellbasedmodels](https://hub.docker.com/repository/docker/dsblab/cellbasedmodels), so there is not need of building the image except if you want to extend further capabilities.
+Constructed images are already deposited in [https://hub.docker.com/r/dsblab/cellbasedmodels](https://hub.docker.com/r/dsblab/cellbasedmodels), so there is not need of building the image except if you want to extend further capabilities.
 
 # Running the image
 
+> **NOTE Multithreading** 
+> Docker images are by default exposed to all the threads but you have to provide the number of threads to the execution yu have to define the variable `JULIA_NUM_THREADS` inside the docker as shown in the examples below. More information can be seen in the [documentation](https://docs.julialang.org/en/v1/manual/multi-threading/).
+
+
 ## Interactive Shell
 
 Start the container in interactive format.
 
 ```shell
-docker run -it \
+docker run -it --rm \
     --mount type=bind,source="$(pwd)",target=/home \
-    dsblab/cellbasedmodels:v0.1.0 julia
+    dsblab/cellbasedmodels:v0.1.0 julia --threads 4
 ```
 
 ## Script
 
 To execute directly a bash script simply
 
 ```shell
-docker run \
+docker run --rm \
     --mount type=bind,source="$(pwd)",target=/home \
     dsblab/cellbasedmodels:v0.1.0 /bin/bash -c "<bash_script.sh>"
 ```
 
 and a julia script
 
 ```shell
-docker run \
+docker run --rm \
     --mount type=bind,source="$(pwd)",target=/home \
-    dsblab/cellbasedmodels:v0.1.0 /bin/bash -c "julia <julia_script.py>"
+    dsblab/cellbasedmodels:v0.1.0 /bin/bash -c "export JULIA_NUM_THREADS=4; julia <julia_script.py>"
 ```
 
 ## Jupyter lab
 
 If you want to work interactively with a jupyter notebook.
 
 ```shell
-docker run -it \
+docker run -it --rm \
     -p 8888:8888 \
     --mount type=bind,source="$(pwd)",target=/home \
-    dsblab/cellbasedmodels:v0.1.0
+    dsblab/cellbasedmodels:v0.1.0 /bin/bash -c "export JULIA_NUM_THREADS=4; jupyter lab --allow-root --ip 0.0.0.0"
 ```
 
+# Comments on docker flags
+
+There are many flags for dockers. In here I briefly resume some of the ones used in the examples above.
+
+ - `-it`: Says the docker you are going to use it in interactive mode.
+ - `--rm`: Remove the image once finished the job.
+ - `-p`: Which port to show the connection
+ - `-mount type=X,...`: Mount the folder so its content is accessible inside the docker. 
+
 You can then view the Jupyter Notebook by opening `http://localhost:8888` in your browser, or `http://<DOCKER-MACHINE-IP>:8888` if you are using a Docker.
diff --git a/docker/build.sh b/docker/build.sh
@@ -2,5 +2,5 @@
 
 version=v0.1.0
 
-# docker build . -t dsblab/cellbasedmodels:$version
+docker build . -t dsblab/cellbasedmodels:$version
 docker push dsblab/cellbasedmodels:$version
diff --git a/docker/install.jl b/docker/install.jl
@@ -2,7 +2,6 @@ using Pkg
 
 packages = [
     "IJulia",
-    "CellBasedModels",
     "CSV",
     "DataFrames",
     "Plots",
@@ -17,4 +16,13 @@ packages = [
 for package in packages
     Pkg.add(package)
     eval(string("using ",package))
+end
+
+packages = [
+    ("CellBasedModels","develop"),
+]
+
+for (package, version) in packages
+    Pkg.add(Pkg.PackageSpec(name=package; rev=version))
+    eval(string("using ",package))
 end
diff --git a/docs/src/Usage_Agent.md b/docs/src/Usage_Agent.md
@@ -446,7 +446,7 @@ Once defined the rules, you can define specific algorithms for the computation o
 - `platform` argument to define the type of platform in which you want to evolve the model.
 - `agentAlg`, `modelAlg` and `mediumAlg` to define the integrator that you want to use. You can use our own integrators provided in the submodule `CBMIntegrators` that may be faster in general but in complex cases with stiff operation or in which you need high precission, you can always use the integrators from `DifferentialEquations.jl` suite. 
 - `agentSolveArgs`, `modelSolveArgs` and `mediumSolveArgs` to define additional arguments required by the integrators to work. This arguments are the ones present when definind a problem in `DifferentialEquations.jl`. 
-- `neighbors` algorithm. This defines the way of computing neighbors. This step is one of the most cost expensive in ABMs and the correct selection of algorithm can really afect your computational efficientcy. We provide several possible algorithms in the submodule `CBMNeighbors`.
+- `neighborsAlg` algorithm. This defines the way of computing neighbors. This step is one of the most cost expensive in ABMs and the correct selection of algorithm can really afect your computational efficientcy. We provide several possible algorithms in the submodule `CBMNeighbors`.
 
 You should have seen some of this arguments declared in the code above.
 

diff --git a/docs/src/Usage_Community.md b/docs/src/Usage_Community.md
@@ -237,6 +237,7 @@ Once we have defined a model and constructed an initial community, we can evolve
 
 In the following schema we describe the basic collection of functions provided by the package to evolve the model, where some functions perform automatically all the step that are described on the right.
 
+```@raw html 
 <table>
     <thead>
         <th colspan=3> Functions </th>
@@ -293,6 +294,7 @@ In the following schema we describe the basic collection of functions provided b
         </tr>
     </tbody>
 </table>
+```
 
 Depending on the control that the user wants to have on the call of the evolution functions, they can decide to use one of the three column schemas proposed above.
 

diff --git a/docs/src/assets/Development.gif b/docs/src/assets/Development.gif
diff --git a/docs/src/assets/development.gif b/docs/src/assets/development.gif
diff --git a/examples/Aggregation.ipynb b/examples/Aggregation.ipynb
diff --git a/examples/Bacteries.ipynb b/examples/Bacteries.ipynb
diff --git a/examples/CellSorting.ipynb b/examples/CellSorting.ipynb
diff --git a/examples/Development.ipynb b/examples/Development.ipynb
diff --git a/examples/Implementation.ipynb b/examples/Implementation.ipynb