Deploy PR #101 preview [skip ci]

Author: github-actions[bot]

pr-101/Parameters/Spectrum.html

@@ -621,10 +621,10 @@ <h2>Example Usage<a class="headerlink" href="#example-usage" title="Link to this
 <div class="sphx-glr-thumbnails"><div class="sphx-glr-thumbcontainer" tooltip="This example shows a spectrum from extracted data. No binning is applied."><img alt="" src="../_images/sphx_glr_plot_spectrum_dia_ms_bokeh_thumb.png" />
 <p><a class="reference internal" href="../gallery/ms_bokeh/plot_spectrum_dia_ms_bokeh.html"><span class="doc">Spectrum of Extracted DIA Data ms_bokeh</span></a></p>
   <div class="sphx-glr-thumbnail-title">Spectrum of Extracted DIA Data ms_bokeh</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows a spectrum.  We can add the ion_annotation and sequence annotation by specifying these columns."><img alt="" src="../_images/sphx_glr_plot_spectrum_ms_bokeh_thumb.png" />
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows a spectrum. We can add the ion_annotation and sequence annotation by specifying these columns."><img alt="" src="../_images/sphx_glr_plot_spectrum_ms_bokeh_thumb.png" />
 <p><a class="reference internal" href="../gallery/ms_bokeh/plot_spectrum_ms_bokeh.html"><span class="doc">Spectrum ms_bokeh</span></a></p>
   <div class="sphx-glr-thumbnail-title">Spectrum ms_bokeh</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="Here we use a dummy spectrum example to investigate spectrum binning. "><img alt="" src="../_images/sphx_glr_plot_investigate_spectrum_binning_ms_matplotlib_thumb.png" />
+</div><div class="sphx-glr-thumbcontainer" tooltip="Here we use a dummy spectrum example to investigate spectrum binning."><img alt="" src="../_images/sphx_glr_plot_investigate_spectrum_binning_ms_matplotlib_thumb.png" />
 <p><a class="reference internal" href="../gallery/ms_matplotlib/plot_investigate_spectrum_binning_ms_matplotlib.html"><span class="doc">Investigate Spctrum Binning ms_matplotlib</span></a></p>
   <div class="sphx-glr-thumbnail-title">Investigate Spctrum Binning ms_matplotlib</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This shows the code required to plot the d-fructose example in the manuscript.  The mzML file is from the the Human Metabolome Database (ID: HMDB0000660)"><img alt="" src="../_images/sphx_glr_plot_manuscript_d_fructose_spectrum_prediction_ms_matplotlib_thumb.png" />
@@ -633,13 +633,13 @@ <h2>Example Usage<a class="headerlink" href="#example-usage" title="Link to this
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example shows a spectrum from extracted data. No binning is applied."><img alt="" src="../_images/sphx_glr_plot_spectrum_dia_ms_matplotlib_thumb.png" />
 <p><a class="reference internal" href="../gallery/ms_matplotlib/plot_spectrum_dia_ms_matplotlib.html"><span class="doc">Spectrum of Extracted DIA Data ms_matplotlib</span></a></p>
   <div class="sphx-glr-thumbnail-title">Spectrum of Extracted DIA Data ms_matplotlib</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows a spectrum.  We can add the ion_annotation and sequence annotation by specifying these columns."><img alt="" src="../_images/sphx_glr_plot_spectrum_ms_matplotlib_thumb.png" />
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows a spectrum. We can add the ion_annotation and sequence annotation by specifying these columns."><img alt="" src="../_images/sphx_glr_plot_spectrum_ms_matplotlib_thumb.png" />
 <p><a class="reference internal" href="../gallery/ms_matplotlib/plot_spectrum_ms_matplotlib.html"><span class="doc">Spectrum ms_matplotlib</span></a></p>
   <div class="sphx-glr-thumbnail-title">Spectrum ms_matplotlib</div>
 </div><div class="sphx-glr-thumbcontainer" tooltip="This example shows a spectrum from extracted data. No binning is applied."><img alt="" src="../_images/sphx_glr_plot_spectrum_dia_ms_plotly_thumb.png" />
 <p><a class="reference internal" href="../gallery/ms_plotly/plot_spectrum_dia_ms_plotly.html"><span class="doc">Spectrum of Extracted DIA Data ms_plotly</span></a></p>
   <div class="sphx-glr-thumbnail-title">Spectrum of Extracted DIA Data ms_plotly</div>
-</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows a spectrum.  We can add the ion_annotation and sequence annotation by specifying these columns."><img alt="" src="../_images/sphx_glr_plot_spectrum_ms_plotly_thumb.png" />
+</div><div class="sphx-glr-thumbcontainer" tooltip="This example shows a spectrum. We can add the ion_annotation and sequence annotation by specifying these columns."><img alt="" src="../_images/sphx_glr_plot_spectrum_ms_plotly_thumb.png" />
 <p><a class="reference internal" href="../gallery/ms_plotly/plot_spectrum_ms_plotly.html"><span class="doc">Spectrum ms_plotly</span></a></p>
   <div class="sphx-glr-thumbnail-title">Spectrum ms_plotly</div>
 </div></div></section>

pr-101/_downloads/02c3fb5f62267b776b91e2ba90235d76/plot_mobilogram_ms_bokeh.py

@@ -6,17 +6,16 @@
 This example shows how to use different approaches.
 """
 
+import os
 import pandas as pd
-import requests
-from io import StringIO
+from pyopenms_viz.util import download_file
 
 pd.options.plotting.backend = "ms_bokeh"
 
-# download the file for example plotting
-url = "https://github.com/OpenMS/pyopenms_viz/releases/download/v0.1.5/ionMobilityTestFeatureDf.tsv"
-response = requests.get(url)
-response.raise_for_status()  # Check for any HTTP errors
-df = pd.read_csv(StringIO(response.text), sep="\t")
+local_path = "ionMobilityTestFeatureDf.tsv"
+url = "https://zenodo.org/records/17904352/files/ionMobilityTestFeatureDf.tsv?download=1"
+download_file(url, local_path)
+df = pd.read_csv(local_path, sep="\t")
 
 df.plot(
     kind="mobilogram",

pr-101/_downloads/055bb6e84f747c63485d2dc9635a2dae/plot_chromatogram_ms_bokeh.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import pandas as pd\nimport requests\nfrom io import StringIO\n\npd.options.plotting.backend = \"ms_bokeh\"\n\n\n# download the file for example plotting\nurl = \"https://github.com/OpenMS/pyopenms_viz/releases/download/v0.1.5/ionMobilityTestChromatogramDf.tsv\"\nresponse = requests.get(url)\nresponse.raise_for_status()  # Check for any HTTP errors\ndf = pd.read_csv(StringIO(response.text), sep=\"\\t\")\n\ndf.plot(\n    kind=\"chromatogram\",\n    x=\"rt\",\n    y=\"int\",\n    by=\"Annotation\",\n    legend_config=dict(bbox_to_anchor=(1, 0.7)),\n)"
+        "import pandas as pd\nfrom pyopenms_viz.util import download_file\n\npd.options.plotting.backend = \"ms_bokeh\"\n\nurl = \"https://zenodo.org/records/17904352/files/ionMobilityTestChromatogramDf.tsv?download=1\"\nlocal_path = \"ionMobilityTestChromatogramDf.tsv\"\ndownload_file(url, local_path)\ndf = pd.read_csv(local_path, sep=\"\\t\")\n\ndf.plot(\n    kind=\"chromatogram\",\n    x=\"rt\",\n    y=\"int\",\n    by=\"Annotation\",\n    legend_config=dict(bbox_to_anchor=(1, 0.7)),\n)"
       ]
     }
   ],

pr-101/_downloads/09f9dd56f5c8755cebde1b45fc070778/plot_manuscript_d_fructose_spectrum_prediction_ms_matplotlib.ipynb

@@ -33,7 +33,7 @@
       },
       "outputs": [],
       "source": [
-        "import requests\nimport zipfile\nimport io\n\n# URL of the ZIP file\nurl = \"https://github.com/OpenMS/pyopenms_viz/releases/download/manuscript/d_fructose_example.zip\"\n\n# Download the ZIP file\nresponse = requests.get(url)\nresponse.raise_for_status()  # Check for request errors\n\n# Unzip the content\nwith zipfile.ZipFile(io.BytesIO(response.content)) as zip_file:\n    zip_file.extractall(\"d_fructose_example\")  # Extract to a directory\n    print(\"Files extracted to 'd_fructose_example'\")"
+        "from pyopenms_viz.util import download_file, unzip_file\n\n# URL of the ZIP file\nurl = \"https://zenodo.org/records/17904512/files/d_fructose_example.zip?download=1\"\n\n# Download and extract the ZIP file\nzip_filename = \"d_fructose_example.zip\"\ndownload_file(url, zip_filename)\nunzip_file(zip_filename, \".\")  # Extract to current directory"
       ]
     },
     {
@@ -87,7 +87,7 @@
       },
       "outputs": [],
       "source": [
-        "import pymzml\n\n# Load the known annotated SMILES and formula into a dataframe\ndata = {\n    \"mz\": [45.0, 61.0, 73.0, 75.0, 87.0],\n    \"formula\": [\"CHO2\", \"C2H5O2\", \"C2HO3\", \"C2H3O3\", \"C3H3O3\"],\n    \"SMILES\": [\"C(=O)O\", \"CC(=O)O\", \"O=CC(=O)O\", \"OCC(=O)O\", \"O=CC(=O)CO\"],\n}\nann_df = pd.DataFrame(data)\n\n# We can use pymzml to load the spectrum data from the mzML file\n\ninput_file = \"./d_fructose_example/d_fructose_example/D-Fructose_lc_msms.mzML\"\n\nrun = pymzml.run.Reader(input_file)\n# Get the first spec\nspec = run.next()\n# Convert to Pandas DataFrame\ndf = pd.DataFrame(spec.centroidedPeaks).rename(columns={0: \"mz\", 1: \"int\"})\ndf[\"rel_int\"] = df[\"int\"] / df[\"int\"].max() * 100\ndf[\"color_peak\"] = \"#1b9e77\"\ni = 1\nfor mz in df[\"mz\"]:\n    if np.floor(mz) in ann_df[\"mz\"].values:\n        # print(f\"{ann_df[['mz']].isin([mz]).values.flatten()}\")\n        df.loc[df.mz == mz, [\"custom_annotation\"]] = ann_df.loc[\n            ann_df[[\"mz\"]].isin([mz]).values.flatten(), [\"formula\"]\n        ].values[0][0]\n        # Apply the formatting function to the 'formula' column\n        df.loc[df.mz == mz, [\"custom_annotation\"]] = df.loc[df.mz == mz][\n            \"custom_annotation\"\n        ].apply(format_formula)\n        i += 1\n# replace the NaN values with empty strings in custom_annotation column\ndf[\"custom_annotation\"] = df[\"custom_annotation\"].fillna(\"\")\ndf.head()"
+        "import pymzml\n\n# Load the known annotated SMILES and formula into a dataframe\ndata = {\n    \"mz\": [45.0, 61.0, 73.0, 75.0, 87.0],\n    \"formula\": [\"CHO2\", \"C2H5O2\", \"C2HO3\", \"C2H3O3\", \"C3H3O3\"],\n    \"SMILES\": [\"C(=O)O\", \"CC(=O)O\", \"O=CC(=O)O\", \"OCC(=O)O\", \"O=CC(=O)CO\"],\n}\nann_df = pd.DataFrame(data)\n\n# We can use pymzml to load the spectrum data from the mzML file\n\ninput_file = \"./d_fructose_example/D-Fructose_lc_msms.mzML\"\n\nrun = pymzml.run.Reader(input_file)\n# Get the first spec\nspec = run.next()\n# Convert to Pandas DataFrame\ndf = pd.DataFrame(spec.centroidedPeaks).rename(columns={0: \"mz\", 1: \"int\"})\ndf[\"rel_int\"] = df[\"int\"] / df[\"int\"].max() * 100\ndf[\"color_peak\"] = \"#1b9e77\"\ni = 1\nfor mz in df[\"mz\"]:\n    if np.floor(mz) in ann_df[\"mz\"].values:\n        # print(f\"{ann_df[['mz']].isin([mz]).values.flatten()}\")\n        df.loc[df.mz == mz, [\"custom_annotation\"]] = ann_df.loc[\n            ann_df[[\"mz\"]].isin([mz]).values.flatten(), [\"formula\"]\n        ].values[0][0]\n        # Apply the formatting function to the 'formula' column\n        df.loc[df.mz == mz, [\"custom_annotation\"]] = df.loc[df.mz == mz][\n            \"custom_annotation\"\n        ].apply(format_formula)\n        i += 1\n# replace the NaN values with empty strings in custom_annotation column\ndf[\"custom_annotation\"] = df[\"custom_annotation\"].fillna(\"\")\ndf.head()"
       ]
     },
     {
@@ -105,7 +105,7 @@
       },
       "outputs": [],
       "source": [
-        "pred_df, annotation_df = parse_cfm_data(\n    \"./d_fructose_example/d_fructose_example/cfm_prediction/cfm_d_fructose_smile_prediction.txt\",\n    \"energy2\",\n)\npred_df[\"rel_int\"] = pred_df[\"int\"] / pred_df[\"int\"].max() * 100\npred_df_long = explode_frag_ids_probabilities(pred_df)\npred_df[\"color_peak\"] = \"#d95f02\"\ni = 1\nfor mz in pred_df[\"mz\"]:\n    if np.floor(mz) in ann_df[\"mz\"].values:\n        pred_df.loc[pred_df.mz == mz, [\"custom_annotation\"]] = f\"f{i}\"\n        i += 1\n\n\n# We also want to create a dataframe with the predictions for each fragment ion with their corresponding probabilities.\npred_ann_df = annotation_df[\n    (annotation_df[\"frag_id\"].isin(pred_df_long.frag_id.values.tolist()))\n]\n# Apply the function to the SMILES column to create a new column 'formula'\npred_ann_df[\"formula\"] = pred_ann_df[\"SMILES\"].apply(smiles_to_formula)\npred_ann_df = pred_ann_df = pred_ann_df.loc[\n    pred_ann_df[\"mz\"].isin(pred_df.dropna().mz.values.tolist())\n]\npred_ann_df.sort_values(\"mz\", inplace=True)\npred_ann_df = pd.merge(pred_ann_df, pred_df_long, on=[\"frag_id\", \"mz\"], how=\"inner\")\npred_ann_df.sort_values([\"mz\", \"probabilities\"], inplace=True, ascending=[True, False])\n# Apply the formatting function to the 'formula' column\npred_ann_df[\"formatted_formula\"] = pred_ann_df[\"formula\"].apply(format_formula)"
+        "pred_df, annotation_df = parse_cfm_data(\n    \"./d_fructose_example/cfm_prediction/cfm_d_fructose_smile_prediction.txt\",\n    \"energy2\",\n)\npred_df[\"rel_int\"] = pred_df[\"int\"] / pred_df[\"int\"].max() * 100\npred_df_long = explode_frag_ids_probabilities(pred_df)\npred_df[\"color_peak\"] = \"#d95f02\"\ni = 1\nfor mz in pred_df[\"mz\"]:\n    if np.floor(mz) in ann_df[\"mz\"].values:\n        pred_df.loc[pred_df.mz == mz, [\"custom_annotation\"]] = f\"f{i}\"\n        i += 1\n\n\n# We also want to create a dataframe with the predictions for each fragment ion with their corresponding probabilities.\npred_ann_df = annotation_df[\n    (annotation_df[\"frag_id\"].isin(pred_df_long.frag_id.values.tolist()))\n]\n# Apply the function to the SMILES column to create a new column 'formula'\npred_ann_df[\"formula\"] = pred_ann_df[\"SMILES\"].apply(smiles_to_formula)\npred_ann_df = pred_ann_df = pred_ann_df.loc[\n    pred_ann_df[\"mz\"].isin(pred_df.dropna().mz.values.tolist())\n]\npred_ann_df.sort_values(\"mz\", inplace=True)\npred_ann_df = pd.merge(pred_ann_df, pred_df_long, on=[\"frag_id\", \"mz\"], how=\"inner\")\npred_ann_df.sort_values([\"mz\", \"probabilities\"], inplace=True, ascending=[True, False])\n# Apply the formatting function to the 'formula' column\npred_ann_df[\"formatted_formula\"] = pred_ann_df[\"formula\"].apply(format_formula)"
       ]
     },
     {

pr-101/_downloads/0f0ff118cf435a491bc3ac61ea475f1f/plot_spyogenes_subplots_ms_plotly.py

@@ -6,40 +6,16 @@
 """
 
 import pandas as pd
-import requests
-import zipfile
 import numpy as np
 from plotly.subplots import make_subplots
+from pyopenms_viz.util import download_file, unzip_file
 
-###### Load/Download Data #######
-
-# URL of the zip file
-url = "https://github.com/OpenMS/pyopenms_viz/releases/download/v0.1.3/spyogenes.zip"
 zip_filename = "spyogenes.zip"
+zip_dir = "spyogenes"
+url = "https://zenodo.org/records/17904512/files/spyogenes.zip?download=1"
 
-# Download the zip file
-try:
-    print(f"Downloading {zip_filename}...")
-    response = requests.get(url)
-    response.raise_for_status()  # Check for any HTTP errors
-
-    # Save the zip file to the current directory
-    with open(zip_filename, "wb") as out:
-        out.write(response.content)
-    print(f"Downloaded {zip_filename} successfully.")
-except requests.RequestException as e:
-    print(f"Error downloading zip file: {e}")
-except IOError as e:
-    print(f"Error writing zip file: {e}")
-
-# Unzipping the file
-try:
-    with zipfile.ZipFile(zip_filename, "r") as zip_ref:
-        # Extract all files to the current directory
-        zip_ref.extractall()
-        print("Unzipped files successfully.")
-except zipfile.BadZipFile as e:
-    print(f"Error unzipping file: {e}")
+download_file(url, zip_filename)
+unzip_file(zip_filename, ".")  # Extract to current directory
 
 annotation_bounds = pd.read_csv(
     "spyogenes/AADGQTVSGGSILYR3_manual_annotations.tsv", sep="\t"

pr-101/_downloads/11e407ed3a83dd7e4a5d693278e54594/plot_spyogenes_subplots_ms_plotly.zip

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import pandas as pd\nimport requests\nfrom io import StringIO\n\npd.options.plotting.backend = \"ms_matplotlib\"\n\n# download the file for example plotting\nurl = \"https://github.com/OpenMS/pyopenms_viz/releases/download/v0.1.5/ionMobilityTestFeatureDf.tsv\"\nresponse = requests.get(url)\nresponse.raise_for_status()  # Check for any HTTP errors\ndf = pd.read_csv(StringIO(response.text), sep=\"\\t\")\n\ndf.plot(\n    kind=\"peakmap\",\n    x=\"rt\",\n    y=\"mz\",\n    z=\"int\",\n    add_marginals=True,\n    aggregate_duplicates=True,\n)"
+        "import pandas as pd\nfrom pyopenms_viz.util import download_file\n\npd.options.plotting.backend = \"ms_matplotlib\"\n\nurl = \"https://zenodo.org/records/17904352/files/ionMobilityTestFeatureDf.tsv?download=1\"\nlocal_path = \"ionMobilityTestFeatureDf.tsv\"\ndownload_file(url, local_path)\ndf = pd.read_csv(local_path, sep=\"\\t\")\n\ndf.plot(\n    kind=\"peakmap\",\n    x=\"rt\",\n    y=\"mz\",\n    z=\"int\",\n    add_marginals=True,\n    aggregate_duplicates=True,\n)"
       ]
     }
   ],