From 92868f6d861bd05d558b601bce7af629aa518c4b Mon Sep 17 00:00:00 2001
From: LaraFuhrmann <55209716+LaraFuhrmann@users.noreply.github.com>
Date: Fri, 26 May 2023 11:41:49 +0200
Subject: [PATCH 1/6] collect all mutaiton + all annotated muations + scan
 mutations

---
 workflow/Snakefile | 42 ++++++++++++++++++++++--------------------
 1 file changed, 22 insertions(+), 20 deletions(-)

diff --git a/workflow/Snakefile b/workflow/Snakefile
index c454216..5e1512f 100644
--- a/workflow/Snakefile
+++ b/workflow/Snakefile
@@ -22,6 +22,7 @@ rule all:
     input:
         f"results/mutations_of_interest.csv",
         f"results/all_mutations.csv",
+        f"results/all_mutations.annotated.csv", # note that here deletions are excluded
 
 rule copy_coverage_file:
     input:
@@ -164,7 +165,6 @@ rule run_viloca:
         fname_bad_samples= f"results/samples.bad_coverage.csv",
     output:
         fname_vcf=f"results/{{sample}}/snvs.vcf",
-        #fname_raw=f"results/{{sample}}/variant_calling/snv/raw_snv.tsv",
         dname_work=directory(
             f"results/{{sample}}/variant_calling/"
         ),
@@ -193,38 +193,40 @@ rule annotate_vcf:
     script:
         "./scripts/annotate_vcf.py"
 
-rule mutations_of_interest:
+
+rule collect_all_annotated_mutations:
     input:
-        fname_snvs_vcf=f"results/{{sample}}/snvs.annotated.vcf",
-    params:
-        fname_mutation_definitions=f"resources/mutation_definitions.yaml",
+        vcf_list=[
+            f"results/{sample}/snvs.annotated.vcf"
+            for sample in all_samples
+        ],
     output:
-        fname_mutations=f"results/{{sample}}/mutations_of_interest.csv",
+        fname_result_csv=f"results/all_mutations.annotated.csv",
+    params:
+        params=all_samples,
     conda:
         "envs/annotate_vcf.yaml"
     script:
-        "./scripts/scan_mutations.py"
-
+        "./scripts/collect_all_annotated_mutations.py"
 
-rule collect_mutations_of_interest:
+rule mutations_of_interest:
     input:
-        csv_list=[
-            f"results/{sample}/mutations_of_interest.csv"
-            for sample in all_samples
-        ],
-    output:
-        fname_result_csv=f"results/mutations_of_interest.csv",
+        fname_all_mutations=f"results/all_mutations.annotated.csv",
     params:
-        params=all_samples,
+        fname_mutation_definitions=f"resources/mutation_definitions.yaml",
+        all_samples=all_samples,
+    output:
+        fname_mutations=f"results/mutations_of_interest.csv",
     conda:
-        "envs/viloca.yaml"
+        "envs/annotate_vcf.yaml"
     script:
-        "scripts/collect_mutations.py"
+        "./scripts/scan_mutations.py"
 
-rule collect_all_annotated_mutations:
+
+rule collect_all_mutations:
     input:
         vcf_list=[
-            f"results/{sample}/snvs.annotated.vcf"
+            f"results/{sample}/snvs.vcf"
             for sample in all_samples
         ],
     output:

From 7b013d659e0dbe4df74e6597ad92f90055c2748b Mon Sep 17 00:00:00 2001
From: LaraFuhrmann <55209716+LaraFuhrmann@users.noreply.github.com>
Date: Fri, 26 May 2023 11:42:40 +0200
Subject: [PATCH 2/6] scan for positions of interest including samples that
 show non of them

---
 workflow/scripts/scan_mutations.py | 33 ++++++++++++++----------------
 1 file changed, 15 insertions(+), 18 deletions(-)

diff --git a/workflow/scripts/scan_mutations.py b/workflow/scripts/scan_mutations.py
index 3ff7674..76cd8a4 100644
--- a/workflow/scripts/scan_mutations.py
+++ b/workflow/scripts/scan_mutations.py
@@ -1,6 +1,6 @@
 """
 Input:
-    -- snvs.vcf outputted by VILOCA
+    -- csv files of all mutations
     -- yaml with mutation definitions
 
 Output:
@@ -8,7 +8,6 @@
 """
 import yaml
 import pandas as pd
-from fuc import pyvcf
 
 def parse_yaml(fname_yaml):
 
@@ -19,32 +18,30 @@ def parse_yaml(fname_yaml):
     return dict_mut
 
 
-
-
-
-def main(fname_vcf, fname_csv, fname_yaml):
+def main(fname_muts, fname_csv, fname_yaml, all_samples):
 
     # read mutations
     dict_mut = parse_yaml(fname_yaml)
     positions_of_interest = [int(x) for x in list(dict_mut.keys())]
 
-    if fname_vcf.split(".")[-1]=="vcf":
-        # vcf into dataframe
-        df_vcf = pyvcf.VcfFrame.from_file(fname_vcf).df
-        df_mut = df_vcf[df_vcf['POS'].isin(positions_of_interest)]
+    df_muts = pd.read_csv(fname_muts)
+
+    # filter for positions of interest
+    df_muts = df_muts[df_muts['POS'].isin(positions_of_interest)]
 
-    elif  fname_vcf.split(".")[-1]=="tsv":
-        df_vcf = pd.read_csv(fname_vcf, sep='\t')
-        if df_vcf.shape[0]>0:
-            df_mut = df_vcf[df_vcf['Pos'].isin(positions_of_interest)]
-        else:
-            df_mut = df_vcf
+    # for samples that don't have positins of interest we need to add an empty lind
+    for sample_name in all_samples:
+        if sample_name not in df_muts['sample'].unique():
+            # create empty row
+            df_muts = df_muts.append({'sample':sample_name}, ignore_index=True)
+            #df_muts = pd.concat([df_muts, pd.DataFrame({"sample": sample_name})])
 
-    df_mut.to_csv(fname_csv)
+    df_muts.to_csv(fname_csv)
 
 if __name__ == "__main__":
     main(
-        snakemake.input.fname_snvs_vcf,
+        snakemake.input.fname_all_mutations,
         snakemake.output.fname_mutations,
         snakemake.params.fname_mutation_definitions,
+        snakemake.params.all_samples,
     )

From 9a62c4eec764f1820033bf087cde93a17f3bb578 Mon Sep 17 00:00:00 2001
From: LaraFuhrmann <55209716+LaraFuhrmann@users.noreply.github.com>
Date: Fri, 26 May 2023 11:45:09 +0200
Subject: [PATCH 3/6] add description

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c1e74ee..e378357 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,4 @@
 # wastewater-sample-processing-VILOCA
-wastewater-sample-processing-VILOCA
+
+To run this workflow, precise directory paths and samples to analyse in config directory.
+Then use script run_workflow.sh to run on Euler cluster.

From 875d16aefb4f96c0957a5bcc4f188be33edc027d Mon Sep 17 00:00:00 2001
From: LaraFuhrmann <55209716+LaraFuhrmann@users.noreply.github.com>
Date: Tue, 30 May 2023 11:20:27 +0200
Subject: [PATCH 4/6] scan mutations: don't list bad samples in the results  as
 they haven't been processes

---
 workflow/Snakefile                 |  1 +
 workflow/scripts/scan_mutations.py | 16 +++++++++++-----
 2 files changed, 12 insertions(+), 5 deletions(-)

diff --git a/workflow/Snakefile b/workflow/Snakefile
index 5e1512f..bbcdc98 100644
--- a/workflow/Snakefile
+++ b/workflow/Snakefile
@@ -212,6 +212,7 @@ rule collect_all_annotated_mutations:
 rule mutations_of_interest:
     input:
         fname_all_mutations=f"results/all_mutations.annotated.csv",
+        fname_bad_samples=f"results/samples.bad_coverage.csv",
     params:
         fname_mutation_definitions=f"resources/mutation_definitions.yaml",
         all_samples=all_samples,
diff --git a/workflow/scripts/scan_mutations.py b/workflow/scripts/scan_mutations.py
index 76cd8a4..86bb9e6 100644
--- a/workflow/scripts/scan_mutations.py
+++ b/workflow/scripts/scan_mutations.py
@@ -18,7 +18,11 @@ def parse_yaml(fname_yaml):
     return dict_mut
 
 
-def main(fname_muts, fname_csv, fname_yaml, all_samples):
+def main(fname_muts, fname_csv, fname_yaml, all_samples, fname_bad_samples):
+
+    # get bad samples --> those should not occurr in the results as they haven't
+    # been processed and we cannot say anything about them
+    bad_samples = pd.read_csv(fname_bad_samples)['sample'].values.tolist()
 
     # read mutations
     dict_mut = parse_yaml(fname_yaml)
@@ -31,10 +35,11 @@ def main(fname_muts, fname_csv, fname_yaml, all_samples):
 
     # for samples that don't have positins of interest we need to add an empty lind
     for sample_name in all_samples:
-        if sample_name not in df_muts['sample'].unique():
-            # create empty row
-            df_muts = df_muts.append({'sample':sample_name}, ignore_index=True)
-            #df_muts = pd.concat([df_muts, pd.DataFrame({"sample": sample_name})])
+        if sample_name not in bad_samples:
+            if sample_name not in df_muts['sample'].unique():
+                # create empty row
+                df_muts = df_muts.append({'sample':sample_name}, ignore_index=True)
+                #df_muts = pd.concat([df_muts, pd.DataFrame({"sample": sample_name})])
 
     df_muts.to_csv(fname_csv)
 
@@ -44,4 +49,5 @@ def main(fname_muts, fname_csv, fname_yaml, all_samples):
         snakemake.output.fname_mutations,
         snakemake.params.fname_mutation_definitions,
         snakemake.params.all_samples,
+        snakemake.input.fname_bad_samples,
     )

From ab215681469b6c7eb286ab3b9cb09dfbb7193749 Mon Sep 17 00:00:00 2001
From: LaraFuhrmann <55209716+LaraFuhrmann@users.noreply.github.com>
Date: Wed, 31 May 2023 10:15:48 +0200
Subject: [PATCH 5/6] [update] file paths

---
 workflow/notebook/visualize_mutations.ipynb | 45 ++++++++++++++-------
 1 file changed, 31 insertions(+), 14 deletions(-)

diff --git a/workflow/notebook/visualize_mutations.ipynb b/workflow/notebook/visualize_mutations.ipynb
index 3de7d3d..2c4525c 100644
--- a/workflow/notebook/visualize_mutations.ipynb
+++ b/workflow/notebook/visualize_mutations.ipynb
@@ -16,14 +16,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "2bdaf80e",
    "metadata": {},
    "outputs": [],
    "source": [
     "# import sample name mapping to date and location \n",
     "\n",
-    "fname_sample_names = \"timeline.tsv\"\n",
+    "fname_sample_names = \"../../resources/timeline.tsv\"\n",
     "\n",
     "df_mapping = pd.read_csv(fname_sample_names, sep=\"\\t\")\n",
     "df_mapping['my_sample_name'] = df_mapping[\"sample\"] + \"/\"+df_mapping[\"batch\"]"
@@ -31,14 +31,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "c1747b50",
    "metadata": {},
    "outputs": [],
    "source": [
     "# import mutations of interest\n",
     "\n",
-    "fname_mut = \"mutations_of_interest.csv\"\n",
+    "fname_mut = \"../../results/mutations_of_interest.csv\"\n",
     "\n",
     "df = pd.read_csv(fname_mut)\n",
     "df = df.drop([\"Unnamed: 0.1\", \"index\", \"Unnamed: 0\", \"INFO\", \"INFO_list\"], axis=1)"
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "71fbff95",
    "metadata": {},
    "outputs": [],
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "c4bc4e78",
    "metadata": {},
    "outputs": [],
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "fe26fa40",
    "metadata": {},
    "outputs": [],
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "92cf9bb7",
    "metadata": {},
    "outputs": [
@@ -111,7 +111,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "6f402ba8",
    "metadata": {},
    "outputs": [],
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "2bea73dd",
    "metadata": {},
    "outputs": [],
@@ -167,15 +167,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "1217f6d2",
    "metadata": {},
    "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "zero-size array to reduction operation fmin which has no identity",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m/var/folders/dw/8dl4p6h53cgcmk6cf_09v5f40000gr/T/ipykernel_30566/1307782269.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m111\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m sns.heatmap(df_pivot, \n\u001b[0m\u001b[1;32m      5\u001b[0m                 \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"RdPu\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m                 linewidths=2,)\n",
+      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/seaborn/matrix.py\u001b[0m in \u001b[0;36mheatmap\u001b[0;34m(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, xticklabels, yticklabels, mask, ax, **kwargs)\u001b[0m\n\u001b[1;32m    538\u001b[0m     \"\"\"\n\u001b[1;32m    539\u001b[0m     \u001b[0;31m# Initialize the plotter object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 540\u001b[0;31m     plotter = _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,\n\u001b[0m\u001b[1;32m    541\u001b[0m                           \u001b[0mannot_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbar_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxticklabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    542\u001b[0m                           yticklabels, mask)\n",
+      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/seaborn/matrix.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, cbar, cbar_kws, xticklabels, yticklabels, mask)\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0;31m# Determine good default values for the colormapping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m         self._determine_cmap_params(plot_data, vmin, vmax,\n\u001b[0m\u001b[1;32m    160\u001b[0m                                     cmap, center, robust)\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/seaborn/matrix.py\u001b[0m in \u001b[0;36m_determine_cmap_params\u001b[0;34m(self, plot_data, vmin, vmax, cmap, center, robust)\u001b[0m\n\u001b[1;32m    196\u001b[0m                 \u001b[0mvmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanpercentile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcalc_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m                 \u001b[0mvmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcalc_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mrobust\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/numpy/core/overrides.py\u001b[0m in \u001b[0;36mnanmin\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
+      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/numpy/lib/nanfunctions.py\u001b[0m in \u001b[0;36mnanmin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m    342\u001b[0m         \u001b[0;31m# Fast, but not safe for subclasses of ndarray, or object arrays,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m         \u001b[0;31m# which do not implement isnan (gh-9009), or fmin correctly (gh-8975)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 344\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    345\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m             warnings.warn(\"All-NaN slice encountered\", RuntimeWarning,\n",
+      "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation fmin which has no identity"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
-       "<Figure size 720x720 with 2 Axes>"
+       "<Figure size 720x720 with 1 Axes>"
       ]
      },
      "metadata": {

From 2b14f0d1555efdba21aa951bf9755e1925337d08 Mon Sep 17 00:00:00 2001
From: LaraFuhrmann <55209716+LaraFuhrmann@users.noreply.github.com>
Date: Tue, 11 Jul 2023 11:25:19 +0200
Subject: [PATCH 6/6] updated plot to svg (#2)

---
 workflow/notebook/visualize_mutations.ipynb | 48 +++++++++------------
 1 file changed, 20 insertions(+), 28 deletions(-)

diff --git a/workflow/notebook/visualize_mutations.ipynb b/workflow/notebook/visualize_mutations.ipynb
index 2c4525c..d83b21b 100644
--- a/workflow/notebook/visualize_mutations.ipynb
+++ b/workflow/notebook/visualize_mutations.ipynb
@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "2bdaf80e",
    "metadata": {},
    "outputs": [],
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "71fbff95",
    "metadata": {},
    "outputs": [],
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "c4bc4e78",
    "metadata": {},
    "outputs": [],
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "fe26fa40",
    "metadata": {},
    "outputs": [],
@@ -94,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "92cf9bb7",
    "metadata": {},
    "outputs": [
@@ -111,7 +111,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -122,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "6f402ba8",
    "metadata": {},
    "outputs": [],
@@ -135,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "2bea73dd",
    "metadata": {},
    "outputs": [],
@@ -171,28 +171,11 @@
    "id": "1217f6d2",
    "metadata": {},
    "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "zero-size array to reduction operation fmin which has no identity",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/dw/8dl4p6h53cgcmk6cf_09v5f40000gr/T/ipykernel_30566/1307782269.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_subplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m111\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m sns.heatmap(df_pivot, \n\u001b[0m\u001b[1;32m      5\u001b[0m                 \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"RdPu\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m                 linewidths=2,)\n",
-      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/seaborn/matrix.py\u001b[0m in \u001b[0;36mheatmap\u001b[0;34m(data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, linewidths, linecolor, cbar, cbar_kws, cbar_ax, square, xticklabels, yticklabels, mask, ax, **kwargs)\u001b[0m\n\u001b[1;32m    538\u001b[0m     \"\"\"\n\u001b[1;32m    539\u001b[0m     \u001b[0;31m# Initialize the plotter object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 540\u001b[0;31m     plotter = _HeatMapper(data, vmin, vmax, cmap, center, robust, annot, fmt,\n\u001b[0m\u001b[1;32m    541\u001b[0m                           \u001b[0mannot_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcbar_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxticklabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    542\u001b[0m                           yticklabels, mask)\n",
-      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/seaborn/matrix.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, vmin, vmax, cmap, center, robust, annot, fmt, annot_kws, cbar, cbar_kws, xticklabels, yticklabels, mask)\u001b[0m\n\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m         \u001b[0;31m# Determine good default values for the colormapping\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 159\u001b[0;31m         self._determine_cmap_params(plot_data, vmin, vmax,\n\u001b[0m\u001b[1;32m    160\u001b[0m                                     cmap, center, robust)\n\u001b[1;32m    161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/seaborn/matrix.py\u001b[0m in \u001b[0;36m_determine_cmap_params\u001b[0;34m(self, plot_data, vmin, vmax, cmap, center, robust)\u001b[0m\n\u001b[1;32m    196\u001b[0m                 \u001b[0mvmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanpercentile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcalc_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m                 \u001b[0mvmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnanmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcalc_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvmax\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mrobust\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/numpy/core/overrides.py\u001b[0m in \u001b[0;36mnanmin\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
-      "\u001b[0;32m~/Library/Python/3.9/lib/python/site-packages/numpy/lib/nanfunctions.py\u001b[0m in \u001b[0;36mnanmin\u001b[0;34m(a, axis, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m    342\u001b[0m         \u001b[0;31m# Fast, but not safe for subclasses of ndarray, or object arrays,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    343\u001b[0m         \u001b[0;31m# which do not implement isnan (gh-9009), or fmin correctly (gh-8975)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 344\u001b[0;31m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfmin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduce\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    345\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnan\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m             warnings.warn(\"All-NaN slice encountered\", RuntimeWarning,\n",
-      "\u001b[0;31mValueError\u001b[0m: zero-size array to reduction operation fmin which has no identity"
-     ]
-    },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
+       "<Figure size 720x720 with 2 Axes>"
       ]
      },
      "metadata": {
@@ -220,7 +203,16 @@
     "plt.title(\"Mutation frequency\", fontsize=20)\n",
     "plt.suptitle(\"* = non-synonymous mutations\", x= 0.7,y=-0.01, fontsize=10)\n",
     "plt.tight_layout()\n",
-    "plt.savefig(\"mutation_calls_on_potential_drug_resistance_sites.pdf\")"
+    "plt.savefig(\"mutation_calls_on_potential_drug_resistance_sites.pdf\")\n",
+    "plt.savefig(\"mutation_calls_on_potential_drug_resistance_sites.svg\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b91ee24",
+   "metadata": {},
+   "source": [
+    "### Plotting the posterior"
    ]
   },
   {