Update requirements

.github/workflows/docs.yml

@@ -29,7 +29,7 @@ jobs:
 
       - uses: actions/setup-python@v5.3.0
         with:
-          python-version: "3.10"
+          python-version: "3.12"
 
       - uses: actions/cache@v4.2.0
         id: venv-cache

docs/effect-size.md

@@ -144,7 +144,7 @@ The following effect size distributions are supported in tstrait, and please ref
 
 Effect sizes can be simulated in tstrait by using {py:func}`tstrait.sim_trait`. In the example below,
 we will be simulating effect sizes of 5 causal sites from a simulated tree sequence data in
-[msprime](msprime:sec_intro).
+{ref}`msprime<msprime:sec_intro>`.
 
 ```{code-cell}
 
@@ -203,8 +203,7 @@ The below code is used to extract information of site with ID 0 from `ts` tree s
 # Extract information of site with ID 0
 ts.site(0)
 ```
-
-The details of sites in tree sequences can be found [here](tskit:sec_site_table_definition).
+The details of sites in tree sequences can be found {ref}`here<tskit:sec_site_table_definition>`.
 
 
 (trait_frequency_dependence)=

docs/environment.md

@@ -67,9 +67,9 @@ We will be showing an example of environmental noise simulating by using a simul
 sequence data with 10,000 individuals. The narrow-sense heritability is set to be 0.3.
 
 :::{seealso}
-- [msprime](msprime:sec_intro) for simulating whole genome in tree sequence data.
+- {ref}`msprime:sec_intro` for simulating whole genome in tree sequence data.
 - [](sim_trait_doc) for simulating trait dataframe in tstrait.
-- [](genetic_value_doc) for simulating the genetic value dataframe in tstrait.
+- {ref}`genetic_value_doc` for simulating the genetic value dataframe in tstrait.
 :::
 
 ```{code-cell}

docs/genetic.md

@@ -57,14 +57,15 @@ below.
 In this example, we will be showing how to compute genetic values by using the simulated trait dataframe
 in {py:func}`genetic_value`.
 
-We will be simulating a sample tree sequence data with 5 individuals by using [msprime](msprime:sec_intro),
+We will be simulating a sample tree sequence data with 5 individuals by using {ref}`msprime<msprime:sec_intro>`,
 and we will simulate a trait dataframe with 3 causal sites. The dataframe output of {py:func}`sim_trait`
 can be automatically used as an input of {py:func}`genetic_value`, so there is no need to worry about input
 data requirements.
+data requirements.
 
 :::{seealso}
-- [msprime](msprime:sec_intro) for simulating whole genome in tree sequence data.
-- [](sim_trait_doc) for simulating trait dataframe in tstrait.
+- {ref}`msprime<msprime:sec_intro>` for simulating whole genome in tree sequence data.
+- {ref}`sim_trait_doc` for simulating trait dataframe in tstrait.
 :::
 
 ```{code-cell}

docs/intro.md

@@ -22,7 +22,7 @@ Quantitative trait simulation in tstrait is transparent, and users can control e
 it is possible for the users to simulate their own environmental noise on top of simulated genetic values,
 or even use their own defined effect sizes and causal sites. The tree sequence data structure is widely used in
 various population genetic simulation packages, including [SLiM](https://messerlab.org/slim/),
-[msprime](msprime:sec_intro), and [stdpopsim](stdpopsim:sec_introduction); it is therefore easy for
+{ref}`msprime<msprime:sec_intro>`, and {ref}`stdpopsim<stdpopsim:sec_introduction>`; it is therefore easy for
 users of these packages to add quantitative traits to their results using tstrait.
 
 ## Tree Sequence resources
@@ -31,9 +31,9 @@ To learn more about tree sequences:
 
 - The [tskit website](https://tskit.dev/) provides [learning materials](https://tskit.dev/learn/) explaining
   what tree sequences are, and includes tutorials, publications and videos.
-- The [PySLiM manual](pyslim:sec_introduction) explains how forward genetic simulation can be create
+  - The {ref}`PySLiM manual<pyslim:sec_introduction>` explains how forward genetic simulation can be create
   tree sequences.
-- The [msprime manual](msprime:sec_intro) details an efficient backward-time genetic simulator that outputs
+- The {ref}`msprime manual<msprime:sec_intro>` details an efficient backward-time genetic simulator that outputs
   tree sequences.
-- The [tskit tutorials](tskit-tutorials:sec_intro) explain how to analyze succinct tree sequences
+- The {ref}`tskit tutorials<tskit-tutorials:sec_intro>` explain how to analyze succinct tree sequences
   by using [tskit](https://tskit.dev/).

docs/quick-start.md

@@ -22,7 +22,7 @@ This page provides a quick overview of tstrait. We will be using {func}`sim_phen
 demonstrate how quantitative traits can be simulated in tstrait.
 
 To work with the examples, you will need to install
-[msprime](msprime:sec_intro) and {mod}`matplotlib <matplotlib>` in
+{ref}`msprime<msprime:sec_intro>` and {ref}`matplotlib <matplotlib>` in
 addition to tstrait.
 
 **Learning Objectives**
@@ -71,7 +71,7 @@ ts = msprime.sim_mutations(ts, rate=1e-8, random_seed=101)
 ts.num_individuals
 ```
 
-Here, we have simulated a sample tree sequence with 10,000 individuals in [msprime](msprime:sec_intro).
+Here, we have simulated a sample tree sequence with 10,000 individuals in {ref}`msprime<msprime:sec_intro>`.
 We will be using it in {func}`sim_phenotype` to simulate quantitative traits.
 
 ```{code-cell}

requirements/CI-complete/requirements.txt

@@ -2,6 +2,6 @@ msprime
 numba
 numpy
 pandas
-pytest==6.2.5
-pytest-cov==3.0.0
+pytest==8.3.5
+pytest-cov==6.0.0
 tskit>=0.5.5

requirements/CI-docs/requirements.txt

@@ -1,10 +1,8 @@
-jupyter-book==0.15.1
-matplotlib==3.7.2
-msprime==1.2.0
-numba==0.57.1
-numpy==1.21.6 # for numba
-numpydoc==1.5.0
-pandas==2.0.3
-sphinx==5.0.2
-sphinx-issues==3.0.1
-tskit==0.5.5
+jupyter-book==1.0.4.post1
+matplotlib==3.10.1
+msprime==1.3.3
+numba==0.61.0
+numpydoc==1.8.0
+pandas==2.2.3
+sphinx-issues==5.0.0
+tskit==0.6.0