patch errors in docs

Author: Joon-Klaps

README.md

@@ -34,7 +34,7 @@
     - Read UMI deduplication ([`HUMID`](https://humid.readthedocs.io/en/latest/usage.html))
     - Low complexity and quality filtering ([`bbduk`](https://jgi.doe.gov/data-and-tools/software-tools/bbtools/), [`prinseq++`](https://github.com/Adrian-Cantu/PRINSEQ-plus-plus))
     - Host-read removal ([`BowTie2`](http://bowtie-bio.sourceforge.net/bowtie2/))
-3. Metagenomic diveristy mapping
+3. Metagenomic diversity mapping
     - Performs taxonomic classification and/or profiling using one or more of:
         - [`Kraken2`](https://ccb.jhu.edu/software/kraken2/)
         - [`Bracken`](https://ccb.jhu.edu/software/bracken/)(optional)

bin/custom_mpileup.py

@@ -95,7 +95,7 @@ def shannon_entropy(nucleotides: NDArray, total_coverage: NDArray) -> NDArray:
     shannon = np.nan_to_num(shannon)
 
     # Replace -0.0 with 0.0
-    shannon = np.where(shannon == -0.0, 0.0, shannon)
+    shannon = np.where(shannon == -0.0, 0.0, np.round(shannon, 3))
 
     return shannon
 
@@ -104,7 +104,7 @@ def corrected_shannon(shannon: NDArray, total_coverage: NDArray, k: int) -> NDAr
     Correct the Shannon entropy by multiplying it with N/(N+k)
     """
     correction = total_coverage / (total_coverage + k)
-    return shannon * correction
+    return np.round(shannon * correction, 3)
 
 
 def write_csv(matrix: NDArray, prefix: str) -> None:

docs/output.md

@@ -661,7 +661,7 @@ Variant files are visualized in the MultiQC report.
 
 The consensus sequences are generated by [`BCFTools`](http://samtools.github.io/bcftools/bcftools.html) or [`iVar`](https://andersen-lab.github.io/ivar/html/manualpage.html). The consensus sequences are stored in the directory `consensus/` or in the iterations directory `assembly/polishing/iterations/it#/consensus`.
 
-`BCFtools` will use the filtered variants file whereas, `iVar` will redetermine the variants to collapse in the consensus using their own workflow, read more about their differences in the [consensus calling section](./workflow/variant_and_refinement.md#consensus-calling).
+`BCFtools` will use the filtered variants file whereas, `iVar` will redetermine the variants to collapse in the consensus using their own workflow, read more about their differences in the [consensus calling section](./workflow/variant_and_refinement.md#4-consensus-calling).
 
 ???- abstract "Output files - iterations & variants"
 

docs/parameters.md

@@ -51,7 +51,7 @@ Options related to the trimming, low complexity and host removal steps of the re
 | `host_k2_library` | Kraken2 library(s) required to remove host and contamination <details><summary>Help</summary><small>Only used when no host kraken2 database is specified.</small></details>| human |
 | `skip_host_fastqc` | Skip the fastqc step after host & contaminants were removed |  |
 
-## Metagenomic diveristy
+## Metagenomic diversity
 
 Parameters used to determine the metagenomic diversity of the sample
 

docs/usage.md

@@ -90,7 +90,7 @@ An example samplesheet file consisting of both single- and paired-end data may l
 Viralgenie can in addition to constructing de novo consensus genomes map the sample reads to a series of references. These references are provided through the parameter `--mapping_constraints`.
 
 An example mapping constraint samplesheet file consisting of 5 references, may look something like the one below.
-> This is for 5 references, 2 of them being a multi-fasta file, only one of the multi-fasta needs to undergo [reference selection](./workflow/variant_and_refinement.md#selection-of-reference).
+> This is for 5 references, 2 of them being a multi-fasta file, only one of the multi-fasta needs to undergo [reference selection](./workflow/variant_and_refinement.md#1a-selection-of-reference).
 
 
 === "TSV"

docs/workflow/metagenomic_diversity.md

@@ -13,7 +13,7 @@ Viralgenie offers two main tools for the classification of reads and a summary v
 
     Feel free to reach out and suggest more classifiers. However, if the main goal of your project is to establish the presence of a virus within a sample and are therefore only focused on metagenomic diversity, have a look at [taxprofiler](https://nf-co.re/taxprofiler/)
 
-> The read classification can be skipped with the argument `--skip_read_classification`, classifiers should be specified with the parameter `--read_classifiers 'kaiju,kraken2'` (no spaces, no caps). See the [parameters classification section](../parameters.md#read-classification) for all relevant arguments to control the classification steps.
+> The read classification can be skipped with the argument `--skip_read_classification`, classifiers should be specified with the parameter `--read_classifiers 'kaiju,kraken2'` (no spaces, no caps). See the [parameters classification section](../parameters.md#metagenomic-diversity) for all relevant arguments to control the classification steps.
 
 ## Kaiju
 

nextflow_schema.json

@@ -203,7 +203,7 @@
             },
             "fa_icon": "fas fa-bahai"
         },
-        "metagenomic_diveristy": {
+        "metagenomic_diversity": {
             "title": "Metagenomic diversity",
             "type": "object",
             "description": "Parameters used to determine the metagenomic diversity of the sample",
@@ -903,7 +903,7 @@
             "$ref": "#/$defs/preprocessing_options"
         },
         {
-            "$ref": "#/$defs/metagenomic_diveristy"
+            "$ref": "#/$defs/metagenomic_diversity"
         },
         {
             "$ref": "#/$defs/assembly"