diff --git a/TPCQCVis/reports/TPC_AQC_period_template.ipynb b/TPCQCVis/reports/TPC_AQC_period_template.ipynb index f069ec6..aeefdf6 100644 --- a/TPCQCVis/reports/TPC_AQC_period_template.ipynb +++ b/TPCQCVis/reports/TPC_AQC_period_template.ipynb @@ -29,7 +29,7 @@ { "cell_type": "code", "execution_count": null, - "id": "7afb8d37", + "id": "0f0e02f1", "metadata": {}, "outputs": [], "source": [ @@ -220,18 +220,6 @@ "+ [2022](https://docs.google.com/spreadsheets/d/14vXFYVx3oVE8wgJKmIBhjvAt6NpGp7D6H4AmBM9E0Cw/edit#gid=0), [2023](https://docs.google.com/spreadsheets/d/1YBQLXWwwc3aC3B_PYVpFkTgEP0n6u1ovtYfiCOMWnTc/edit?pli=1#gid=0), [2023_PbPb](https://docs.google.com/spreadsheets/d/1vsl-pkLdUoNXZm6muPyMENWdDSKM7UDtQLcXc2B9QhE/edit#gid=492527515)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "69b3818a", - "metadata": {}, - "outputs": [], - "source": [ - "import datetime\n", - "now = datetime.datetime.now()\n", - "display(Markdown(\"+ Report automatically created on \"+now.strftime(\"%d.%m.%Y at %H:%M:%S\")))" - ] - }, { "cell_type": "markdown", "id": "897badfe", @@ -295,7 +283,7 @@ " [qualities,pad] = checkTrending(trend,canvas=pad,thresholds={\"GOOD\":1.5,\"MEDIUM\":3,\"BAD\":6})\n", " qualityDFClusters[names[iPad]] = qualities\n", " canvas.Update()\n", - "#canvas.Draw()" + "canvas.Draw()" ] }, { @@ -347,7 +335,37 @@ "qualityHistClusters.GetZaxis().SetRangeUser(-0.5,3.5);\n", "qualityHistClusters.GetZaxis().SetTitle(\"Quality\")\n", "qualityHistClusters.LabelsOption(\"v\")\n", - "canvas.Draw()" + "canvas.Draw()\n", + "\n", + "\n", + "data = [] # List to store the row data\n", + "\n", + "for x in range(1, qualityHistClusters.GetNbinsX() + 1): # x-axis bins\n", + " for y in range(1, qualityHistClusters.GetNbinsY() + 1): # y-axis bins\n", + " binContent = qualityHistClusters.GetBinContent(x, y)\n", + " if binContent > 1.5:\n", + " # Getting the labels for x and y axis\n", + " xLabel = qualityHistClusters.GetXaxis().GetBinLabel(x)\n", + " yLabel = qualityHistClusters.GetYaxis().GetBinLabel(y)\n", + "\n", + " # Determine the quality status\n", + " if 1.5 < binContent <= 2.5:\n", + " qualityStatus = \"medium\"\n", + " elif binContent > 2.5:\n", + " qualityStatus = \"bad\"\n", + " else:\n", + " continue # Skip if the value is not in the specified range\n", + "\n", + " # Append the data to the list\n", + " data.append({\n", + " \"X label\": xLabel,\n", + " \"Y label\": yLabel,\n", + " \"Quality\": qualityStatus\n", + " })\n", + "\n", + "# Convert the list of dictionaries to a DataFrame\n", + "clustersquality = pd.DataFrame(data)\n", + "\n" ] }, { @@ -384,7 +402,9 @@ "cell_type": "code", "execution_count": null, "id": "3ac8fbbd", - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "canvas = ROOT.TCanvas(\"QualityMatrixTracks\",\"\",1000,500)\n", @@ -413,7 +433,37 @@ "\n", "# Update the canvas\n", "canvas.Update()\n", - "canvas.Draw()" + "canvas.Draw()\n", + "\n", + "\n", + "data = [] # List to store the row data\n", + "\n", + "for x in range(1, qualityHist.GetNbinsX() + 1): # x-axis bins\n", + " for y in range(1, qualityHist.GetNbinsY() + 1): # y-axis bins\n", + " binContent = qualityHist.GetBinContent(x, y)\n", + " if binContent > 1.5:\n", + " # Getting the labels for x and y axis\n", + " xLabel = qualityHist.GetXaxis().GetBinLabel(x)\n", + " yLabel = qualityHist.GetYaxis().GetBinLabel(y)\n", + "\n", + " # Determine the quality status\n", + " if 1.5 < binContent <= 2.5:\n", + " qualityStatus = \"medium\"\n", + " elif binContent > 2.5:\n", + " qualityStatus = \"bad\"\n", + " else:\n", + " continue # Skip if the value is not in the specified range\n", + "\n", + " # Append the data to the list\n", + " data.append({\n", + " \"X label\": xLabel,\n", + " \"Y label\": yLabel,\n", + " \"Quality\": qualityStatus\n", + " })\n", + "\n", + "# Convert the list of dictionaries to a DataFrame\n", + "tracksquality = pd.DataFrame(data)\n", + "\n" ] }, { @@ -443,7 +493,19 @@ "[qualities,canvas] = checkTrending(trend,canvas=canvas,thresholds={\"GOOD\":1.5,\"MEDIUM\":3,\"BAD\":6})\n", "canvas.SetCanvasSize(1000,400)\n", "qualityDF[objectName] = qualities\n", - "canvas.Draw()" + "canvas.Draw()\n", + "\n", + "data = [] # List to store the data\n", + "\n", + "# Iterate over the elements in qualities\n", + "for bin_index, quality_status in enumerate(qualities, start=1): # Assuming bin indexing starts at 1\n", + " if quality_status in [\"MEDIUM\", \"BAD\"]:\n", + " # Retrieve the X label for the bin\n", + " x_label = trend.GetXaxis().GetBinLabel(bin_index)\n", + " data.append({\"Run\": x_label, \"Quality\": quality_status})\n", + "\n", + "# Convert the list of dictionaries to a DataFrame\n", + "hPhiAsideTrendingQuality = pd.DataFrame(data)" ] }, { @@ -463,14 +525,30 @@ "[qualities,canvas] = checkTrending(trend,canvas=canvas,thresholds={\"GOOD\":1.5,\"MEDIUM\":3,\"BAD\":6})\n", "qualityDF[objectName] = qualities\n", "canvas.SetCanvasSize(1000,400)\n", - "#trend.SetTitle(\"Number of tracks - Trend\")\n", - "canvas.Draw()" + "trend.SetTitle(\"Number of tracks - Trend\")\n", + "canvas.Draw()\n", + "\n", + "data = [] # List to store the data\n", + "\n", + "# Iterate over the elements in qualities\n", + "for bin_index, quality_status in enumerate(qualities, start=1): # Assuming bin indexing starts at 1\n", + " if quality_status in [\"MEDIUM\", \"BAD\"]:\n", + " # Retrieve the X label for the bin\n", + " x_label = trend.GetXaxis().GetBinLabel(bin_index)\n", + " data.append({\"Run\": x_label, \"Quality\": quality_status})\n", + "\n", + "# Convert the list of dictionaries to a DataFrame\n", + "numberofTracksTrendingQuality = pd.DataFrame(data)" ] }, { "cell_type": "markdown", "id": "e0c37372", - "metadata": {}, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, "source": [ "### Mean eta of tracks" ] @@ -493,7 +571,19 @@ "qualityDF[objectName] = qualities\n", "#trend.SetTitle(\"Number of tracks - Trend\")\n", "canvas.SetCanvasSize(1000,400)\n", - "canvas.Draw()" + "canvas.Draw()\n", + "\n", + "data = [] # List to store the data\n", + "\n", + "# Iterate over the elements in qualities\n", + "for bin_index, quality_status in enumerate(qualities, start=1): # Assuming bin indexing starts at 1\n", + " if quality_status in [\"MEDIUM\", \"BAD\"]:\n", + " # Retrieve the X label for the bin\n", + " x_label = trend.GetXaxis().GetBinLabel(bin_index)\n", + " data.append({\"Run\": x_label, \"Quality\": quality_status})\n", + "\n", + "# Convert the list of dictionaries to a DataFrame\n", + "hEtaTrendingQuality = pd.DataFrame(data)" ] }, { @@ -522,7 +612,19 @@ "qualityDF[objectName] = qualities\n", "#trend.SetTitle(\"Number of tracks - Trend\")\n", "canvas.SetCanvasSize(1000,400)\n", - "canvas.Draw()" + "canvas.Draw()\n", + "\n", + "data = [] # List to store the data\n", + "\n", + "# Iterate over the elements in qualities\n", + "for bin_index, quality_status in enumerate(qualities, start=1): # Assuming bin indexing starts at 1\n", + " if quality_status in [\"MEDIUM\", \"BAD\"]:\n", + " # Retrieve the X label for the bin\n", + " x_label = trend.GetXaxis().GetBinLabel(bin_index)\n", + " data.append({\"Run\": x_label, \"Quality\": quality_status})\n", + "\n", + "# Convert the list of dictionaries to a DataFrame\n", + "hPtTrendingQuality = pd.DataFrame(data)" ] }, { @@ -551,7 +653,19 @@ "qualityDF[objectName] = qualities\n", "#trend.SetTitle(\"Number of tracks - Trend\")\n", "canvas.SetCanvasSize(1000,400)\n", - "canvas.Draw()" + "canvas.Draw()\n", + "\n", + "data = [] # List to store the data\n", + "\n", + "# Iterate over the elements in qualities\n", + "for bin_index, quality_status in enumerate(qualities, start=1): # Assuming bin indexing starts at 1\n", + " if quality_status in [\"MEDIUM\", \"BAD\"]:\n", + " # Retrieve the X label for the bin\n", + " x_label = trend.GetXaxis().GetBinLabel(bin_index)\n", + " data.append({\"Run\": x_label, \"Quality\": quality_status})\n", + "\n", + "# Convert the list of dictionaries to a DataFrame\n", + "hSignTrendingQuality = pd.DataFrame(data)" ] }, { @@ -583,7 +697,19 @@ "#trend.GetYaxis().SetRangeUser(45,55)\n", "#trend.SetTitle(\"Number of tracks - Trend\")\n", "canvas.SetCanvasSize(1000,400)\n", - "canvas.Draw()" + "canvas.Draw()\n", + "\n", + "data = [] # List to store the data\n", + "\n", + "# Iterate over the elements in qualities\n", + "for bin_index, quality_status in enumerate(qualities, start=1): # Assuming bin indexing starts at 1\n", + " if quality_status in [\"MEDIUM\", \"BAD\"]:\n", + " # Retrieve the X label for the bin\n", + " x_label = trend.GetXaxis().GetBinLabel(bin_index)\n", + " data.append({\"Run\": x_label, \"Quality\": quality_status})\n", + "\n", + "# Convert the list of dictionaries to a DataFrame\n", + "hdEdxTotMIP_TPCTrendingQuality = pd.DataFrame(data)" ] }, { @@ -612,7 +738,19 @@ "qualityDF[objectName] = qualities\n", "#trend.SetTitle(\"Number of tracks - Trend\")\n", "canvas.SetCanvasSize(1000,400)\n", - "canvas.Draw()" + "canvas.Draw()\n", + "\n", + "data = [] # List to store the data\n", + "\n", + "# Iterate over the elements in qualities\n", + "for bin_index, quality_status in enumerate(qualities, start=1): # Assuming bin indexing starts at 1\n", + " if quality_status in [\"MEDIUM\", \"BAD\"]:\n", + " # Retrieve the X label for the bin\n", + " x_label = trend.GetXaxis().GetBinLabel(bin_index)\n", + " data.append({\"Run\": x_label, \"Quality\": quality_status})\n", + "\n", + "# Convert the list of dictionaries to a DataFrame\n", + "hNClustersAfterCutsTrendingQuality = pd.DataFrame(data)" ] }, { @@ -641,7 +779,19 @@ "qualityDF[objectName] = qualities\n", "#trend.SetTitle(\"Number of tracks - Trend\")\n", "canvas.SetCanvasSize(1000,400)\n", - "canvas.Draw()" + "canvas.Draw()\n", + "\n", + "data = [] # List to store the data\n", + "\n", + "# Iterate over the elements in qualities\n", + "for bin_index, quality_status in enumerate(qualities, start=1): # Assuming bin indexing starts at 1\n", + " if quality_status in [\"MEDIUM\", \"BAD\"]:\n", + " # Retrieve the X label for the bin\n", + " x_label = trend.GetXaxis().GetBinLabel(bin_index)\n", + " data.append({\"Run\": x_label, \"Quality\": quality_status})\n", + "\n", + "# Convert the list of dictionaries to a DataFrame\n", + "hdEdxVsTglTrendingQuality = pd.DataFrame(data)" ] }, { @@ -670,7 +820,19 @@ "qualityDF[objectName] = qualities\n", "#trend.SetTitle(\"Number of tracks - Trend\")\n", "canvas.SetCanvasSize(1000,400)\n", - "canvas.Draw()" + "canvas.Draw()\n", + "\n", + "data = [] # List to store the data\n", + "\n", + "# Iterate over the elements in qualities\n", + "for bin_index, quality_status in enumerate(qualities, start=1): # Assuming bin indexing starts at 1\n", + " if quality_status in [\"MEDIUM\", \"BAD\"]:\n", + " # Retrieve the X label for the bin\n", + " x_label = trend.GetXaxis().GetBinLabel(bin_index)\n", + " data.append({\"Run\": x_label, \"Quality\": quality_status})\n", + "\n", + "# Convert the list of dictionaries to a DataFrame\n", + "hdEdxTotMIPVsSnp_TPCTrendingQuality = pd.DataFrame(data)" ] }, { @@ -703,6 +865,14 @@ "**Number of clusters per pad, A-side:**" ] }, + { + "cell_type": "markdown", + "id": "9de1092e", + "metadata": {}, + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -727,6 +897,14 @@ "**Number of clusters per pad, C-side:**" ] }, + { + "cell_type": "markdown", + "id": "65c00534", + "metadata": {}, + "source": [ + "" + ] + }, { "cell_type": "code", "execution_count": null, @@ -751,6 +929,14 @@ "**Cluster total charge per pad, A-side:**" ] }, + { + "cell_type": "markdown", + "id": "398fc290", + "metadata": {}, + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -774,6 +960,14 @@ "**Cluster total charge per pad, C-side:**" ] }, + { + "cell_type": "markdown", + "id": "f861b5e9", + "metadata": {}, + "source": [ + "" + ] + }, { "cell_type": "code", "execution_count": null, @@ -800,6 +994,14 @@ "**Phi distribution A-side tracks:**" ] }, + { + "cell_type": "markdown", + "id": "abb86f1d", + "metadata": {}, + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -837,6 +1039,14 @@ "**Phi distribution C-side tracks:**" ] }, + { + "cell_type": "markdown", + "id": "c30523c3", + "metadata": {}, + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -874,6 +1084,14 @@ "**Eta distribution:**" ] }, + { + "cell_type": "markdown", + "id": "66c949a2", + "metadata": {}, + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -923,6 +1141,14 @@ " canvas.Draw()" ] }, + { + "cell_type": "markdown", + "id": "03ffcc21", + "metadata": {}, + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -947,6 +1173,14 @@ "**Transverse momentum**" ] }, + { + "cell_type": "markdown", + "id": "5365a42b", + "metadata": {}, + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -974,6 +1208,14 @@ "**Track sign**" ] }, + { + "cell_type": "markdown", + "id": "c1253070", + "metadata": {}, + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1000,6 +1242,14 @@ "**Charge / transverse momentum**" ] }, + { + "cell_type": "markdown", + "id": "c7825a38", + "metadata": {}, + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1026,6 +1276,14 @@ "**Number of clusters per track**" ] }, + { + "cell_type": "markdown", + "id": "27b056d7", + "metadata": {}, + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1053,12 +1311,12 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "a5d008df-abd1-4ed0-a220-8c72289e25d2", + "cell_type": "markdown", + "id": "81f7cbb2", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "\n" + ] }, { "cell_type": "code", @@ -1091,6 +1349,14 @@ "canvas.Draw()" ] }, + { + "cell_type": "markdown", + "id": "be7e248b", + "metadata": {}, + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1130,6 +1396,14 @@ "**nClusters vs eta**" ] }, + { + "cell_type": "markdown", + "id": "edaf185a", + "metadata": {}, + "source": [ + "" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1159,6 +1433,14 @@ "**nClusters vs transverse momentum**" ] }, + { + "cell_type": "markdown", + "id": "fb31cb26", + "metadata": {}, + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1321,6 +1603,18 @@ "#### dEdxTot of MIP" ] }, + { + "cell_type": "markdown", + "id": "74f05922", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1374,6 +1668,14 @@ "**dEdx of MIP vs sector**" ] }, + { + "cell_type": "markdown", + "id": "67812972", + "metadata": {}, + "source": [ + "" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1404,6 +1706,14 @@ "**dEdx of Mip vs nClusters**" ] }, + { + "cell_type": "markdown", + "id": "8170a75c", + "metadata": {}, + "source": [ + "" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1439,6 +1749,14 @@ "**dEdx of Mip vs tan(lambda)**" ] }, + { + "cell_type": "markdown", + "id": "f080c7ff", + "metadata": {}, + "source": [ + "" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1473,6 +1791,14 @@ "**dEdx of Mip vs sin(phi)**" ] }, + { + "cell_type": "markdown", + "id": "80e80f03", + "metadata": {}, + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1507,6 +1833,14 @@ "#### dEdx vs p" ] }, + { + "cell_type": "markdown", + "id": "86dde2b1", + "metadata": {}, + "source": [ + "\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1527,7 +1861,143 @@ { "cell_type": "code", "execution_count": null, - "id": "f7620fc3-74bd-4cbf-971d-7ce9c756e313", + "id": "b08ea383", + "metadata": {}, + "outputs": [], + "source": [ + "from ipywidgets import VBox, Button, Dropdown, HTML, Layout, ButtonStyle\n", + "from IPython.display import FileLink\n", + "\n", + "def append_histogram_if_needed(hNClustersAfterCutsTrendingQuality, hNClustersAfterCuts):\n", + " global run, histogram_names # Reference global or previously defined variables\n", + "\n", + " # Filter the DataFrame for the current run\n", + " filtered_df = hNClustersAfterCutsTrendingQuality[hNClustersAfterCutsTrendingQuality['Run'] == str(run)]\n", + "\n", + " # Check if there are any rows after filtering\n", + " if not filtered_df.empty:\n", + " # Get the quality for the current run\n", + " specific_run_quality = filtered_df['Quality'].iloc[0]\n", + "\n", + " # Add 'hNClustersAfterCuts' only if the quality is 'MEDIUM' or 'BAD'\n", + " if specific_run_quality == 'MEDIUM' or specific_run_quality == 'BAD':\n", + " histogram_names.append(hNClustersAfterCuts)\n", + "\n", + " return histogram_names\n", + "\n", + "\n", + "def create_run_widget(run, histogram_names, worst_quality):\n", + " # Set button color based on the worst quality\n", + " button_style = ButtonStyle()\n", + " if worst_quality == \"BAD\":\n", + " button_style.button_color = 'red'\n", + " elif worst_quality == \"MEDIUM\":\n", + " button_style.button_color = 'yellow'\n", + " elif worst_quality == \"GOOD\":\n", + " button_style.button_color = 'green'\n", + "\n", + " dropdown = Dropdown(options=histogram_names, description=f'Run {run}:')\n", + " button = Button(description=f\"Run {run}\", style=button_style)\n", + " link_output = HTML() # Placeholder for the link\n", + " \n", + " # Initialize link output if there's only one histogram\n", + " if len(histogram_names) == 1:\n", + " link_output.value = f\"Go to histogram {histogram_names[0]}\"\n", + "\n", + "\n", + " def on_button_clicked(b):\n", + " dropdown.layout.display = 'flex' # Show the dropdown\n", + " # Display the link immediately if there's only one histogram\n", + " if len(histogram_names) == 1:\n", + " link_output.value = f\"Go to histogram {histogram_names[0]}\"\n", + " def on_dropdown_change(change):\n", + " if change['type'] == 'change' and change['name'] == 'value':\n", + " # Directly create a link to the anchor\n", + " link_output.value = f\"Go to histogram {change['new']}\"\n", + "\n", + " button.on_click(on_button_clicked)\n", + " dropdown.observe(on_dropdown_change, names='value')\n", + "\n", + " return VBox([button, dropdown, link_output])\n", + "\n", + "\n", + "\n", + "# Base list of histogram names\n", + "#all_histogram_names = ['hPhiCside','h2DRPhinClustersAside','h2DRPhinClustersCside','h2DRPhiqTotAside','h2DRPhiqTotCside','h2DEtaPhi','hQOverPt','h2DNClustersPhiAside','h2DNClustersPhiCside','h2DNClustersEta','h2DNClustersPt','hdEdxTotMIP_OROC3','hdEdxTotMIP_OROC2','hdEdxTotMIP_OROC1','hdEdxTotMIP_IROC','hdEdxTotMIPVsSec_TPC','hdEdxTotMIPVsNcl_TPC','hdEdxTotMIPVsSnp_TPC','hdEdxTotVsP_TPC'] # Add all histograms here\n", + "all_histogram_names = []\n", + "# List to store the widgets\n", + "widgets_list = []\n", + "run_summary = []\n", + "\n", + "for run in runList:\n", + " # Start with a copy of the base list of histogram names for each run\n", + " histogram_names = all_histogram_names.copy()\n", + " worst_quality = \"GOOD\"\n", + "\n", + "# Define a list of DataFrame and histogram name pairs\n", + " df_histogram_pairs = [\n", + " (hEtaTrendingQuality, 'hEta'),\n", + " (hPhiAsideTrendingQuality, 'hPhiAside'),\n", + " (numberofTracksTrendingQuality, 'hPhiCside'),\n", + " (hNClustersAfterCutsTrendingQuality, 'hNClustersAfterCuts'),\n", + " (hdEdxTotMIP_TPCTrendingQuality, 'hdEdxTotMIP_TPC'),\n", + " (hSignTrendingQuality, 'hSign'), \n", + " (hPtTrendingQuality, 'hPt'),\n", + " (hdEdxVsTglTrendingQuality, 'hdEdxVsTgl'),\n", + " (hdEdxTotMIPVsSnp_TPCTrendingQuality, 'hdEdxTotMIPVsSnp_TPC')\n", + "\n", + " ] \n", + " \n", + " \n", + "\n", + "\n", + " # Iterate over each pair to check and append histogram names and determine worst quality\n", + " for df, histogram_name in df_histogram_pairs:\n", + " if not df.empty:\n", + " filtered_df = df[df['Run'] == str(run)]\n", + " if not filtered_df.empty:\n", + " specific_run_quality = filtered_df['Quality'].iloc[0]\n", + " if specific_run_quality == 'MEDIUM' or specific_run_quality == 'BAD':\n", + " histogram_names.append(histogram_name)\n", + " # Update the worst quality if needed\n", + " if specific_run_quality == \"BAD\":\n", + " worst_quality = \"BAD\"\n", + " break # Stop checking if any histogram is BAD\n", + " elif specific_run_quality == \"MEDIUM\" and worst_quality != \"BAD\":\n", + " worst_quality = \"MEDIUM\"\n", + "\n", + " # Create and add the widget for this run to the list\n", + " widgets_list.append(create_run_widget(run, histogram_names, worst_quality))\n", + " \n", + " run_summary.append((run, worst_quality, histogram_names))\n", + "\n", + "with open(\"run_quality_summary.txt\", \"w\") as file:\n", + " for run, quality, histograms in run_summary:\n", + " file.write(f\"Run {run}, Worst Quality: {quality}, Histograms: {', '.join(histograms)}\\n\")\n", + " \n", + "# Create a link to download the file\n", + "download_link = FileLink('run_quality_summary.txt', result_html_prefix=\"Click here to download: \")\n", + "\n", + "# Display the link\n", + "display(download_link)\n", + "\n", + "\n", + "# Display all widgets\n", + "display(VBox(widgets_list))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03cd7c70", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da740a3b", "metadata": {}, "outputs": [], "source": [] @@ -1549,7 +2019,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.9.6" }, "toc": { "base_numbering": 1,