use ssl to open files

bcgsc · Mar 5, 2024 · d6658bc · d6658bc
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commit d6658bc
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Showing 2 changed files with 35 additions and 9 deletions.
diff --git a/Figures/ts_methylation/HR_gene_methylation_HRDetect_correlation.ipynb b/Figures/ts_methylation/HR_gene_methylation_HRDetect_correlation.ipynb
@@ -14,6 +14,8 @@
     "import seaborn as sns\n",
     "from matplotlib.lines import Line2D\n",
     "import warnings\n",
+    "import ssl\n",
+    "from urllib.request import urlopen\n",
     "%matplotlib inline\n",
     "\n",
     "warnings.filterwarnings(\"ignore\")\n",
@@ -349,9 +351,16 @@
     "    filename_methyl_fraction_non_BRC_OVA = r\"https://www.bcgsc.ca/downloads/nanopore_pog/ts_methylation//BRCA1_RAD51C_fraction_of_methylated_sites_non_BRC_OVA.tsv\"\n",
     "    filename_BRCA1_RAD51C_logTPM = r\"https://www.bcgsc.ca/downloads/nanopore_pog/ts_methylation//BRCA1_RAD51C_logTPM.tsv\"\n",
     "    \n",
-    "    df_methyl_BRC_OVA = pd.read_csv(filename_methyl_fraction_BRC_OVA, sep = \"\\t\", index_col = \"tumour_original_source\")\n",
-    "    df_methyl_non_BRC_OVA = pd.read_csv(filename_methyl_fraction_non_BRC_OVA, sep = \"\\t\", index_col = \"tumour_original_source\")\n",
-    "    df_BRCA1_RAD51C_logTPM = pd.read_csv(filename_BRCA1_RAD51C_logTPM, sep = \"\\t\", index_col = \"tumour_original_source\")\n",
+    "    context=ssl.create_default_context()\n",
+    "    context.set_ciphers(\"DEFAULT\")\n",
+    "    \n",
+    "    result_BRC_OVA = urlopen(filename_methyl_fraction_BRC_OVA, context=context)\n",
+    "    result_non_BRC_OVA = urlopen(filename_methyl_fraction_non_BRC_OVA, context=context)\n",
+    "    result_BRCA1_RAD51C_logTPM = urlopen(filename_BRCA1_RAD51C_logTPM, context=context)\n",
+    "    \n",
+    "    df_methyl_BRC_OVA = pd.read_csv(result_BRC_OVA, sep = \"\\t\", index_col = \"tumour_original_source\")\n",
+    "    df_methyl_non_BRC_OVA = pd.read_csv(result_non_BRC_OVA, sep = \"\\t\", index_col = \"tumour_original_source\")\n",
+    "    df_BRCA1_RAD51C_logTPM = pd.read_csv(result_BRCA1_RAD51C_logTPM, sep = \"\\t\", index_col = \"tumour_original_source\")\n",
     "\n",
     "\n",
     "    generate_HRDetect_plot_BRC_OVA(df_methyl_BRC_OVA, df_methyl_non_BRC_OVA, PALETTE, OFFSET_METHYLATED)\n",

diff --git a/Figures/ts_methylation/haplotype_specific_methylation_DNA_repair.ipynb b/Figures/ts_methylation/haplotype_specific_methylation_DNA_repair.ipynb
@@ -11,6 +11,8 @@
     "import numpy as np\n",
     "from matplotlib.pylab import plt\n",
     "from statsmodels.nonparametric.smoothers_lowess import lowess\n",
+    "import ssl\n",
+    "from urllib.request import urlopen\n",
     "%matplotlib inline\n",
     "\n",
     "params = {\"font.family\":\"Arial\",\n",
@@ -73,10 +75,15 @@
     "        files_BRCA1 = BRCA1_files[tumour_type]\n",
     "        logger.info(\"generating BRCA1 haplotype plots for {}\".format(tumour_type))\n",
     "        \n",
+    "        context=ssl.create_default_context()\n",
+    "        context.set_ciphers(\"DEFAULT\")\n",
+    "        \n",
     "        if len(files_BRCA1[0]) >0:\n",
-    "            df_blood = pd.read_csv(files_BRCA1[0], sep = \"\\t\", index_col = [\"gene\",\"start\", \"end\"])\n",
+    "            result_blood = urlopen(files_BRCA1[0], context=context)\n",
+    "            df_blood = pd.read_csv(result_blood, sep = \"\\t\", index_col = [\"gene\",\"start\", \"end\"])\n",
     "        if len(files_BRCA1[1])>0:\n",
-    "            df_tumour = pd.read_csv(files_BRCA1[1], sep = \"\\t\", index_col = [\"gene\",\"start\", \"end\"])\n",
+    "            result_tumour = urlopen(files_BRCA1[1], context=context)\n",
+    "            df_tumour = pd.read_csv(result_tumour, sep = \"\\t\", index_col = [\"gene\",\"start\", \"end\"])\n",
     "\n",
     "    \n",
     "        fig_BRCA1, ax_BRCA1 = plt.subplots(ncols=1, nrows=3, sharex = True, figsize = (8.5, 4.2), dpi = 600)\n",
@@ -217,10 +224,15 @@
     "        \n",
     "        files_RAD51C = RAD51C_files[tumour_type]\n",
     "        \n",
+    "        context=ssl.create_default_context()\n",
+    "        context.set_ciphers(\"DEFAULT\")\n",
+    "        \n",
     "        if len(files_RAD51C[0]) >0:\n",
-    "            df_blood = pd.read_csv(files_RAD51C[0], sep = \"\\t\", index_col = [\"gene\",\"start\", \"end\"])\n",
+    "            result_blood = urlopen(files_RAD51C[0], context=context)\n",
+    "            df_blood = pd.read_csv(result_blood, sep = \"\\t\", index_col = [\"gene\",\"start\", \"end\"])\n",
     "        if len(files_RAD51C[1])>0:\n",
-    "            df_tumour = pd.read_csv(files_RAD51C[1], sep = \"\\t\", index_col = [\"gene\",\"start\", \"end\"])\n",
+    "            result_tumour = urlopen(files_RAD51C[1], context=context)\n",
+    "            df_tumour = pd.read_csv(result_tumour, sep = \"\\t\", index_col = [\"gene\",\"start\", \"end\"])\n",
     "\n",
     "        logger.info(\"generating RAD51C haplotype plots for {}\".format(tumour_type))\n",
     "\n",
@@ -341,8 +353,13 @@
     "\n",
     "    logger.info(\"generating MLH1 haplotype plots\")\n",
     "\n",
-    "    df_blood = pd.read_csv(filename_MLH1_blood, sep = \"\\t\", index_col = [\"gene\",\"start\", \"end\"])\n",
-    "    df_tumour = pd.read_csv(filename_MLH1_tumour, sep = \"\\t\", index_col = [\"gene\",\"start\", \"end\"])\n",
+    "    context=ssl.create_default_context()\n",
+    "    context.set_ciphers(\"DEFAULT\")\n",
+    "    \n",
+    "    result_blood = urlopen(filename_MLH1_blood, context=context)\n",
+    "    df_blood = pd.read_csv(result_blood, sep = \"\\t\", index_col = [\"gene\",\"start\", \"end\"])\n",
+    "    result_tumour = urlopen(filename_MLH1_tumour, context=context)\n",
+    "    df_tumour = pd.read_csv(result_tumour, sep = \"\\t\", index_col = [\"gene\",\"start\", \"end\"])\n",
     "\n",
     "    fig_MLH1, ax_MLH1 = plt.subplots(ncols=1, nrows=2, sharex = True, figsize = (5.2, 4), dpi = 600)\n",
     "    \n",