From fdd1a81b5e0b57ec46cdf64de5c7ad13524caa24 Mon Sep 17 00:00:00 2001
From: daikitag <48062118+daikitag@users.noreply.github.com>
Date: Mon, 4 Dec 2023 17:14:15 +0900
Subject: [PATCH] DOC: Minor changes

Minor changes to documentation
---
 docs/effect-size.md | 7 ++-----
 docs/environment.md | 8 ++++----
 docs/genetic.md     | 3 +--
 docs/multi-trait.md | 4 +++-
 docs/quick-start.md | 6 ++----
 5 files changed, 12 insertions(+), 16 deletions(-)

diff --git a/docs/effect-size.md b/docs/effect-size.md
index 37b08d9..f638307 100644
--- a/docs/effect-size.md
+++ b/docs/effect-size.md
@@ -177,7 +177,6 @@ a normal distribution trait model with 1,000 causal sites.
 ```{code-cell}
 
 import matplotlib.pyplot as plt
-
 model = tstrait.trait_model(distribution="normal", mean=0, var=1)
 trait_df = tstrait.sim_trait(ts, num_causal=1000, model=model, random_seed=1)
 trait_df.head()
@@ -238,8 +237,8 @@ frequency dependent model.
 
 The frequency dependence architecture is still an ongoing research topic. While the
 frequency dependence model can be used for any trait models in tstrait, it is
-suggested that you use the normal distribution with mean 0 as a trait model and
-`alpha` to be -1/2 to conduct simulations that are widely used in
+suggested that you use the trait model with mean 0 and `alpha` to be -1/2 to conduct
+simulations that are widely used in
 simulation-based research projects (See
 [Speed et al. (2017)](https://doi.org/10.1038/ng.3865) for details).
 
@@ -266,8 +265,6 @@ it in your trait simulation.
 
 ```{code-cell}
 
-import matplotlib.pyplot as plt
-
 # trait.sim_trait(ts, num_causal=1000, model=model, random_seed=1)
 # also works here
 trait_df = tstrait.sim_trait(ts, num_causal=1000, model=model, 
diff --git a/docs/environment.md b/docs/environment.md
index e935226..ea0bbf6 100644
--- a/docs/environment.md
+++ b/docs/environment.md
@@ -122,10 +122,10 @@ there are several options available for the user.
 
 ### Simulating from the output of {py:func}`genetic_value`
 
-The output of {py:func}`genetic_value` only includes genetic values, and it doesn't
-simulate environmental noise. For example, if the user wants to simulate
-environmental noise from a normal distribution with mean 0 and variance 1, it
-would be possible to run the following code:
+The output of {py:func}`genetic_value` only includes relevant information regarding
+genetic values, and it doesn't simulate environmental noise. For example, if the
+user wants to simulate environmental noise from a normal distribution with mean 0
+and variance 1, it would be possible to run the following code:
 
 ```{code-cell}
 
diff --git a/docs/genetic.md b/docs/genetic.md
index 2175e43..a78043f 100644
--- a/docs/genetic.md
+++ b/docs/genetic.md
@@ -35,8 +35,7 @@ After this genetic value page, you will be able to:
 
 Genetic value is computed in tstrait by using the trait information in the input trait dataframe.
 It uses a tree traversal algorithm to count the number of causal allele in each individual and adds
-the corresponding effect size to individual's genetic value depending on the presence of causal
-mutation in that individual.
+the corresponding effect size to individual's genetic value.
 
 ## Input
 
diff --git a/docs/multi-trait.md b/docs/multi-trait.md
index f359ad0..711e871 100644
--- a/docs/multi-trait.md
+++ b/docs/multi-trait.md
@@ -57,7 +57,6 @@ import numpy as np
 model = tstrait.trait_model(
     distribution="multi_normal", mean=np.zeros(2), cov=np.eye(2)
 )
-model.name
 model.num_trait
 ```
 
@@ -87,6 +86,9 @@ sim_result = tstrait.sim_phenotype(
 )
 
 sim_result.phenotype
+```
+
+```{code-cell}
 sim_result.trait
 ```
 
diff --git a/docs/quick-start.md b/docs/quick-start.md
index e36465e..2af9e68 100644
--- a/docs/quick-start.md
+++ b/docs/quick-start.md
@@ -94,7 +94,6 @@ Simulated traits from {func}`sim_phenotype` can be extracted through `.trait`.
 ```{code-cell}
 
 trait_df = sim_result.trait
-trait_df.columns
 trait_df.head()
 ```
 
@@ -116,9 +115,8 @@ Simulated phenotypes from {func}`sim_phenotype` can be extracted through `.pheno
 
 ```{code-cell}
 
-  phenotype_df = sim_result.phenotype
-  phenotype_df.columns
-  phenotype_df.head()
+phenotype_df = sim_result.phenotype
+phenotype_df.head()
 ```
 
 The `phenotype_df` is a {class}`pandas.DataFrame` object that includes the following 5 columns: