From f199b8bb3d7a8a8290adfd66dfaad0b8c0b60088 Mon Sep 17 00:00:00 2001 From: Katherine Eaton Date: Sat, 1 May 2021 11:37:10 -0400 Subject: [PATCH] update auspice metadata --- workflow/notebooks/auspice.py.ipynb | 1099 ++++++++++++++++++--------- workflow/notebooks/functions.py | 22 +- 2 files changed, 765 insertions(+), 356 deletions(-) diff --git a/workflow/notebooks/auspice.py.ipynb b/workflow/notebooks/auspice.py.ipynb index 415a85b9..6f12ab57 100644 --- a/workflow/notebooks/auspice.py.ipynb +++ b/workflow/notebooks/auspice.py.ipynb @@ -65,7 +65,7 @@ " WILDCARDS = snakemake.wildcards\n", " project_dir = os.getcwd()\n", "except NameError:\n", - " WILDCARDS = [\"all\", \"chromosome\", \"full\", \"5\"]\n", + " WILDCARDS = [\"all\", \"chromosome\", \"prune\", \"5\"]\n", " project_dir = os.path.dirname(os.path.dirname(os.getcwd()))\n", " \n", "results_dir = os.path.join(project_dir, \"results/\")\n", @@ -190,7 +190,7 @@ " \"0.ANT4\" : [\"0.ANT4\"], \n", " \"0.PE\": [\"0.PE4m\", \"0.PE4m\", \"0.PE4t\", \"0.PE4a\", \"0.PE5\"], \n", " \"0.ANT\": [\"0.ANT1\", \"0.ANT2\",\"0.ANT3\",\"0.ANT5\"], \n", - " \"1.PRE\" : [\"1.PRE1\", \"1.PRE2\", \"1.PRE3\"], \n", + " \"1.PRE\" : [\"1.PRE0\",\"1.PRE1\", \"1.PRE2\", \"1.PRE3\"], \n", " \"1.ANT\": [\"1.ANT1\"], \n", " \"1.IN\": [\"1.IN1\",\"1.IN2\",\"1.IN3\"], \n", " \"1.ORI\" : [\"1.ORI1\", \"1.ORI2\", \"1.ORI3\"],\n", @@ -473,6 +473,12 @@ " if branch_support >= 95 and not cd.is_terminal():\n", " branch_support_char = \"*\"\n", " metadata_df.at[node_name, \"branch_support_char\"] = branch_support_char\n", + "\n", + " if branch_support >= 95:\n", + " metadata_df.at[node_name, \"branch_support_conf_category\"] = \"HIGH\"\n", + " else:\n", + " metadata_df.at[node_name, \"branch_support_conf_category\"] = \"LOW\" \n", + " \n", " \n", " comment_dict = {}\n", " # Mugration Country\n", @@ -529,18 +535,18 @@ { "data": { "text/plain": [ - "strain NA\n", - "date NA\n", - "date_bp NA\n", - "country NA\n", - "province NA\n", - " ... \n", - "mugration_branch_minor 0.PE7\n", - "mugration_country_confidence 0.85\n", - "mugration_province_confidence 0.56\n", - "mugration_branch_major_confidence 0.5\n", - "mugration_branch_minor_confidence 0.31\n", - "Name: NODE0, Length: 36, dtype: object" + "strain NA\n", + "date NA\n", + "date_bp NA\n", + "country NA\n", + "province NA\n", + " ... \n", + "mugration_province_confidence 0.37\n", + "mugration_province_lat_confidence 0.369092\n", + "mugration_province_lon_confidence 0.369092\n", + "mugration_branch_major_confidence 0.5\n", + "mugration_branch_minor_confidence 0.29\n", + "Name: NODE0, Length: 41, dtype: object" ] }, "metadata": {}, @@ -561,6 +567,11 @@ " max_state = state\n", " metadata_df.at[sample,\"mugration_\" + attr] = max_state \n", " metadata_df.at[sample,\"mugration_\" + attr + \"_confidence\"] = round(max_val,2)\n", + " \n", + " # if the attr is country or province, extend this confidence to lat and lon\n", + " if attr == \"country\" or attr == \"province\":\n", + " metadata_df.at[sample,\"mugration_\" + attr + \"_lat_confidence\"] = max_val\n", + " metadata_df.at[sample,\"mugration_\" + attr + \"_lon_confidence\"] = max_val\n", "\n", "display(metadata_df.loc[\"NODE0\"])\n", "#print(list(metadata_df[\"mugration_branch_minor_confidence\"]))" @@ -593,14 +604,24 @@ " # Get data\n", " for rec in metadata_df.iterrows():\n", " node_name = rec[0]\n", + " node_type = rec[1][\"node_type\"]\n", " name = rec[1][attr]\n", - " lat = rec[1][attr + \"_lat\"]\n", - " lon = rec[1][attr + \"_lon\"] \n", + " country = rec[1][\"country\"]\n", + " \n", + " if node_type == \"internal\":\n", + " continue\n", " \n", - " if name == NO_DATA_CHAR: continue\n", + " if attr == \"province\" and name == NO_DATA_CHAR and node_type == \"terminal\" and country != \"Russia\":\n", + " # Use country instead\n", + " name = rec[1][\"country\"]\n", + " lat = rec[1][\"country_lat\"]\n", + " lon = rec[1][\"country_lon\"]\n", + " else:\n", + " lat = rec[1][attr + \"_lat\"]\n", + " lon = rec[1][attr + \"_lon\"] \n", "\n", - " if name not in df.index:\n", "\n", + " if name not in df.index:\n", " df.at[name, \"lat\"] = lat\n", " df.at[name, \"lon\"] = lon\n", " df.at[name, \"size\"] = 1\n", @@ -616,8 +637,17 @@ " for rec in metadata_df.iterrows():\n", " sample = rec[0]\n", " name = rec[1][\"mugration_\" + attr] \n", + " node_type = rec[1][\"node_type\"] \n", + " country = rec[1][\"country\"] \n", + " \n", + " if attr == \"province\" and rec[1][attr] == NO_DATA_CHAR and node_type == \"terminal\" and country != \"Russia\":\n", + " # Use country instead\n", + " name = rec[1][\"country\"]\n", + " metadata_df.at[sample,\"mugration_\" + attr] = name\n", + " \n", " lat = latlon_df[\"lat\"][name]\n", " lon = latlon_df[\"lon\"][name]\n", + " \n", " \n", " metadata_df.at[sample, \"mugration_\" + attr +\"_lat\"] = lat\n", " metadata_df.at[sample, \"mugration_\" + attr +\"_lon\"] = lon\n", @@ -639,7 +669,7 @@ " lon = str(latlon_province_df[\"lon\"][province])\n", " outfile.write(\"province\" + \"\\t\" + province + \"\\t\" + lat + \"\\t\" + lon + \"\\n\")\n", " \n", - "#display(metadata_df)" + "#display(metadata_df[metadata_df[\"continent\"] == \"Africa\"])" ] }, { @@ -668,7 +698,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -1082,14 +1112,23 @@ " timetree_num_date\n", " mugration_country\n", " mugration_country_confidence\n", + " mugration_country_lat\n", + " mugration_country_lat_confidence\n", + " mugration_country_lon\n", + " mugration_country_lon_confidence\n", " mugration_province\n", " mugration_province_confidence\n", + " mugration_province_lat\n", + " mugration_province_lat_confidence\n", + " mugration_province_lon\n", + " mugration_province_lon_confidence\n", " mugration_branch_major\n", " mugration_branch_major_confidence\n", " mugration_branch_minor\n", " mugration_branch_minor_confidence\n", " branch_number\n", " branch_support\n", + " branch_support_conf_category\n", " continent\n", " node_type\n", " geometry_size\n", @@ -1135,23 +1174,41 @@ " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " Reference\n", - " 4.004600e-06\n", + " 3.774600e-06\n", " 1992.0\n", " United States of America\n", - " 1.0\n", + " 1.00\n", + " 39.783730\n", + " 1.000000\n", + " -100.445882\n", + " 1.000000\n", " Colorado\n", " 1.00\n", + " 38.725178\n", + " 1.000000\n", + " -105.607716\n", + " 1.000000\n", " 1.ORI\n", " 1.0\n", " 1.ORI1\n", " 1.0\n", " 1\n", " 100.0\n", + " HIGH\n", " North America\n", " terminal\n", " 1.0\n", @@ -1160,8 +1217,8 @@ " 1992\n", " 0\n", " 29\n", - " 7.31686e-05\n", - " 6.3815e-06\n", + " 6.63842e-05\n", + " 5.7539e-06\n", " [1992.0, 1992.0]\n", " HIGH\n", " HIGH\n", @@ -1170,28 +1227,37 @@ " \n", " \n", " GCA_009909635.1_ASM990963v1_genomic\n", - " 2.120100e-06\n", + " 1.817000e-06\n", " 1923.0\n", " Russia\n", - " 1.0\n", + " 1.00\n", + " 64.686314\n", + " 1.000000\n", + " 97.745306\n", + " 1.000000\n", " Rostov Oblast\n", " 1.00\n", + " 47.622245\n", + " 1.000000\n", + " 40.795794\n", + " 1.000000\n", " 2.MED\n", " 1.0\n", " 2.MED1\n", " 1.0\n", " 2\n", " 100.0\n", + " HIGH\n", " Europe\n", " terminal\n", - " 4.0\n", + " 3.0\n", " SAMN13632815\n", " 9_10\n", " 1923\n", " 0\n", " 98\n", - " 7.30501e-05\n", - " 9.6582e-06\n", + " 6.77518e-05\n", + " 9.4529e-06\n", " [1923.0, 1923.0]\n", " HIGH\n", " HIGH\n", @@ -1200,28 +1266,37 @@ " \n", " \n", " GCA_009669545.1_ASM966954v1_genomic\n", - " 0.000000e+00\n", + " 2.360000e-08\n", " 2006.0\n", " China\n", - " 1.0\n", + " 1.00\n", + " 35.000074\n", + " 1.000000\n", + " 104.999927\n", + " 1.000000\n", " Xinjiang\n", " 1.00\n", + " 42.480495\n", + " 1.000000\n", + " 85.463346\n", + " 1.000000\n", " 0.ANT\n", " 1.0\n", " 0.ANT1\n", " 1.0\n", " 0\n", " 100.0\n", + " HIGH\n", " Asia\n", " terminal\n", - " 105.0\n", + " 8.0\n", " SAMN07722925\n", " 42126\n", " 2006\n", " 0\n", " 15\n", - " 5.41847e-05\n", - " 1.15566e-05\n", + " 5.00169e-05\n", + " 1.11117e-05\n", " [2006.0, 2006.0]\n", " HIGH\n", " HIGH\n", @@ -1229,60 +1304,78 @@ " HIGH\n", " \n", " \n", - " GCA_009669555.1_ASM966955v1_genomic\n", - " 2.356000e-07\n", - " 2005.0\n", + " GCA_009669805.1_ASM966980v1_genomic\n", + " 2.360000e-08\n", + " 1980.0\n", " China\n", - " 1.0\n", + " 1.00\n", + " 35.000074\n", + " 1.000000\n", + " 104.999927\n", + " 1.000000\n", " Xinjiang\n", " 1.00\n", + " 42.480495\n", + " 1.000000\n", + " 85.463346\n", + " 1.000000\n", " 0.ANT\n", " 1.0\n", " 0.ANT1\n", " 1.0\n", " 0\n", " 100.0\n", + " HIGH\n", " Asia\n", " terminal\n", - " 105.0\n", - " SAMN07722924\n", - " 42123\n", - " 2005\n", + " 8.0\n", + " SAMN07722912\n", + " 42092\n", + " 1980\n", " 0\n", - " 16\n", - " 5.47035e-05\n", - " 1.20754e-05\n", - " [2005.0, 2005.0]\n", + " 41\n", + " 5.00169e-05\n", + " 1.11117e-05\n", + " [1980.0, 1980.0]\n", " HIGH\n", " HIGH\n", " HIGH\n", " HIGH\n", " \n", " \n", - " GCA_009669565.1_ASM966956v1_genomic\n", - " 4.711000e-07\n", - " 2005.0\n", - " China\n", - " 1.0\n", - " Xinjiang\n", + " GCA_009296005.1_ASM929600v1_genomic\n", + " 2.359000e-07\n", + " 1953.0\n", + " Russia\n", " 1.00\n", - " 0.ANT\n", + " 64.686314\n", + " 1.000000\n", + " 97.745306\n", + " 1.000000\n", + " Chechnya\n", + " 1.00\n", + " 43.397615\n", + " 1.000000\n", + " 45.698501\n", + " 1.000000\n", + " 2.MED\n", " 1.0\n", - " 0.ANT1\n", + " 2.MED1\n", " 1.0\n", - " 0\n", + " 2\n", " 100.0\n", - " Asia\n", + " HIGH\n", + " Europe\n", " terminal\n", - " 105.0\n", - " SAMN07722923\n", - " 42118\n", - " 2005\n", + " 2.0\n", + " SAMN12991209\n", + " C-25\n", + " 1953\n", " 0\n", - " 16\n", - " 5.4939e-05\n", - " 1.23109e-05\n", - " [2005.0, 2005.0]\n", + " 68\n", + " 6.58604e-05\n", + " 7.5615e-06\n", + " [1953.0, 1953.0]\n", " HIGH\n", " HIGH\n", " HIGH\n", @@ -1317,430 +1410,610 @@ " ...\n", " ...\n", " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " NODE595\n", - " 2.207000e-07\n", - " 1910.0\n", - " Peru\n", - " 1.0\n", - " Cajamarca\n", - " 1.00\n", - " 1.ORI\n", + " NODE188\n", + " 1.179600e-06\n", + " 1990.0\n", + " Kazakhstan\n", + " 0.66\n", + " 47.228609\n", + " 0.662908\n", + " 65.209320\n", + " 0.662908\n", + " West Kazakhstan Region\n", + " 0.36\n", + " 49.556848\n", + " 0.359162\n", + " 50.222741\n", + " 0.359162\n", + " 2.MED\n", " 1.0\n", - " 1.ORI1\n", + " 2.MED1\n", " 1.0\n", " NA\n", - " 13.0\n", + " 100.0\n", + " HIGH\n", " NA\n", " internal\n", - " 18.0\n", + " 2.0\n", " NA\n", " NA\n", " NA\n", " NA\n", " NA\n", - " 7.21466e-05\n", - " 5.3595e-06\n", - " [1885.0, 1929.0]\n", - " HIGH\n", - " HIGH\n", + " 6.85262e-05\n", + " 1.02273e-05\n", + " [1961.0, 1990.0]\n", " HIGH\n", " HIGH\n", + " LOW\n", + " LOW\n", " \n", " \n", - " NODE596\n", - " 2.356000e-07\n", - " 1910.0\n", - " Peru\n", - " 1.0\n", - " Cajamarca\n", - " 1.00\n", - " 1.ORI\n", + " NODE189\n", + " 7.077000e-07\n", + " 1941.0\n", + " Russia\n", + " 0.98\n", + " 64.686314\n", + " 0.984523\n", + " 97.745306\n", + " 0.984523\n", + " Astrakhan Oblast\n", + " 0.54\n", + " 47.187819\n", + " 0.535277\n", + " 47.608851\n", + " 0.535277\n", + " 2.MED\n", " 1.0\n", - " 1.ORI1\n", + " 2.MED1\n", " 1.0\n", " NA\n", - " 13.0\n", + " 100.0\n", + " HIGH\n", " NA\n", " internal\n", - " 18.0\n", + " 3.0\n", " NA\n", " NA\n", " NA\n", " NA\n", " NA\n", - " 7.23822e-05\n", - " 5.5951e-06\n", - " [1885.0, 1929.0]\n", - " HIGH\n", + " 6.7323e-05\n", + " 9.0241e-06\n", + " [1892.0, 1972.0]\n", " HIGH\n", " HIGH\n", " HIGH\n", + " LOW\n", " \n", " \n", - " NODE597\n", - " 2.921000e-07\n", - " 1943.0\n", - " Peru\n", - " 1.0\n", - " Cajamarca\n", + " NODE190\n", + " 9.437000e-07\n", + " 2007.0\n", + " Russia\n", " 1.00\n", - " 1.ORI\n", + " 64.686314\n", + " 0.999691\n", + " 97.745306\n", + " 0.999691\n", + " Astrakhan Oblast\n", + " 0.56\n", + " 47.187819\n", + " 0.560352\n", + " 47.608851\n", + " 0.560352\n", + " 2.MED\n", " 1.0\n", - " 1.ORI1\n", + " 2.MED1\n", " 1.0\n", " NA\n", - " 46.0\n", + " 100.0\n", + " HIGH\n", " NA\n", " internal\n", - " 18.0\n", + " 3.0\n", " NA\n", " NA\n", " NA\n", " NA\n", " NA\n", - " 7.26743e-05\n", - " 5.8872e-06\n", - " [1906.0, 1976.0]\n", - " HIGH\n", + " 6.82667e-05\n", + " 9.9678e-06\n", + " [1985.0, 2014.0]\n", " HIGH\n", " HIGH\n", " HIGH\n", + " LOW\n", " \n", " \n", - " NODE598\n", - " 5.010000e-08\n", - " 1951.0\n", - " Peru\n", - " 1.0\n", - " Cajamarca\n", - " 1.00\n", - " 1.ORI\n", + " NODE191\n", + " 2.360000e-08\n", + " 1964.0\n", + " Russia\n", + " 0.98\n", + " 64.686314\n", + " 0.980724\n", + " 97.745306\n", + " 0.980724\n", + " Republic of Kalmykia\n", + " 0.43\n", + " 46.231302\n", + " 0.428943\n", + " 45.327574\n", + " 0.428943\n", + " 2.MED\n", " 1.0\n", - " 1.ORI1\n", + " 2.MED1\n", " 1.0\n", " NA\n", - " 34.0\n", + " 80.0\n", + " LOW\n", " NA\n", " internal\n", - " 18.0\n", + " 2.0\n", " NA\n", " NA\n", " NA\n", " NA\n", " NA\n", - " 7.21967e-05\n", - " 5.4096e-06\n", - " [1909.0, 1975.0]\n", - " HIGH\n", + " 6.73466e-05\n", + " 9.0477e-06\n", + " [1932.0, 1984.0]\n", " HIGH\n", " HIGH\n", " HIGH\n", + " LOW\n", " \n", " \n", - " NODE599\n", - " 2.248000e-07\n", - " 1967.0\n", - " Peru\n", - " 1.0\n", - " Cajamarca\n", - " 0.99\n", - " 1.ORI\n", + " NODE192\n", + " 2.359000e-07\n", + " 1984.0\n", + " Russia\n", + " 1.00\n", + " 64.686314\n", + " 0.999722\n", + " 97.745306\n", + " 0.999722\n", + " Republic of Kalmykia\n", + " 0.59\n", + " 46.231302\n", + " 0.591156\n", + " 45.327574\n", + " 0.591156\n", + " 2.MED\n", " 1.0\n", - " 1.ORI1\n", + " 2.MED1\n", " 1.0\n", " NA\n", - " 58.0\n", + " 100.0\n", + " HIGH\n", " NA\n", " internal\n", - " 18.0\n", + " 2.0\n", " NA\n", " NA\n", " NA\n", " NA\n", " NA\n", - " 7.24215e-05\n", - " 5.6344e-06\n", - " [1927.0, 1996.0]\n", - " HIGH\n", + " 6.75825e-05\n", + " 9.2836e-06\n", + " [1963.0, 1986.0]\n", " HIGH\n", " HIGH\n", " HIGH\n", + " LOW\n", " \n", " \n", "\n", - "

1201 rows × 27 columns

\n", + "

387 rows × 36 columns

\n", "" ], "text/plain": [ " branch_length timetree_num_date \\\n", "sample \n", - "Reference 4.004600e-06 1992.0 \n", - "GCA_009909635.1_ASM990963v1_genomic 2.120100e-06 1923.0 \n", - "GCA_009669545.1_ASM966954v1_genomic 0.000000e+00 2006.0 \n", - "GCA_009669555.1_ASM966955v1_genomic 2.356000e-07 2005.0 \n", - "GCA_009669565.1_ASM966956v1_genomic 4.711000e-07 2005.0 \n", + "Reference 3.774600e-06 1992.0 \n", + "GCA_009909635.1_ASM990963v1_genomic 1.817000e-06 1923.0 \n", + "GCA_009669545.1_ASM966954v1_genomic 2.360000e-08 2006.0 \n", + "GCA_009669805.1_ASM966980v1_genomic 2.360000e-08 1980.0 \n", + "GCA_009296005.1_ASM929600v1_genomic 2.359000e-07 1953.0 \n", "... ... ... \n", - "NODE595 2.207000e-07 1910.0 \n", - "NODE596 2.356000e-07 1910.0 \n", - "NODE597 2.921000e-07 1943.0 \n", - "NODE598 5.010000e-08 1951.0 \n", - "NODE599 2.248000e-07 1967.0 \n", + "NODE188 1.179600e-06 1990.0 \n", + "NODE189 7.077000e-07 1941.0 \n", + "NODE190 9.437000e-07 2007.0 \n", + "NODE191 2.360000e-08 1964.0 \n", + "NODE192 2.359000e-07 1984.0 \n", "\n", " mugration_country \\\n", "sample \n", "Reference United States of America \n", "GCA_009909635.1_ASM990963v1_genomic Russia \n", "GCA_009669545.1_ASM966954v1_genomic China \n", - "GCA_009669555.1_ASM966955v1_genomic China \n", - "GCA_009669565.1_ASM966956v1_genomic China \n", + "GCA_009669805.1_ASM966980v1_genomic China \n", + "GCA_009296005.1_ASM929600v1_genomic Russia \n", "... ... \n", - "NODE595 Peru \n", - "NODE596 Peru \n", - "NODE597 Peru \n", - "NODE598 Peru \n", - "NODE599 Peru \n", + "NODE188 Kazakhstan \n", + "NODE189 Russia \n", + "NODE190 Russia \n", + "NODE191 Russia \n", + "NODE192 Russia \n", "\n", " mugration_country_confidence \\\n", "sample \n", - "Reference 1.0 \n", - "GCA_009909635.1_ASM990963v1_genomic 1.0 \n", - "GCA_009669545.1_ASM966954v1_genomic 1.0 \n", - "GCA_009669555.1_ASM966955v1_genomic 1.0 \n", - "GCA_009669565.1_ASM966956v1_genomic 1.0 \n", + "Reference 1.00 \n", + "GCA_009909635.1_ASM990963v1_genomic 1.00 \n", + "GCA_009669545.1_ASM966954v1_genomic 1.00 \n", + "GCA_009669805.1_ASM966980v1_genomic 1.00 \n", + "GCA_009296005.1_ASM929600v1_genomic 1.00 \n", "... ... \n", - "NODE595 1.0 \n", - "NODE596 1.0 \n", - "NODE597 1.0 \n", - "NODE598 1.0 \n", - "NODE599 1.0 \n", + "NODE188 0.66 \n", + "NODE189 0.98 \n", + "NODE190 1.00 \n", + "NODE191 0.98 \n", + "NODE192 1.00 \n", + "\n", + " mugration_country_lat \\\n", + "sample \n", + "Reference 39.783730 \n", + "GCA_009909635.1_ASM990963v1_genomic 64.686314 \n", + "GCA_009669545.1_ASM966954v1_genomic 35.000074 \n", + "GCA_009669805.1_ASM966980v1_genomic 35.000074 \n", + "GCA_009296005.1_ASM929600v1_genomic 64.686314 \n", + "... ... \n", + "NODE188 47.228609 \n", + "NODE189 64.686314 \n", + "NODE190 64.686314 \n", + "NODE191 64.686314 \n", + "NODE192 64.686314 \n", + "\n", + " mugration_country_lat_confidence \\\n", + "sample \n", + "Reference 1.000000 \n", + "GCA_009909635.1_ASM990963v1_genomic 1.000000 \n", + "GCA_009669545.1_ASM966954v1_genomic 1.000000 \n", + "GCA_009669805.1_ASM966980v1_genomic 1.000000 \n", + "GCA_009296005.1_ASM929600v1_genomic 1.000000 \n", + "... ... \n", + "NODE188 0.662908 \n", + "NODE189 0.984523 \n", + "NODE190 0.999691 \n", + "NODE191 0.980724 \n", + "NODE192 0.999722 \n", "\n", - " mugration_province \\\n", - "sample \n", - "Reference Colorado \n", - "GCA_009909635.1_ASM990963v1_genomic Rostov Oblast \n", - "GCA_009669545.1_ASM966954v1_genomic Xinjiang \n", - "GCA_009669555.1_ASM966955v1_genomic Xinjiang \n", - "GCA_009669565.1_ASM966956v1_genomic Xinjiang \n", - "... ... \n", - "NODE595 Cajamarca \n", - "NODE596 Cajamarca \n", - "NODE597 Cajamarca \n", - "NODE598 Cajamarca \n", - "NODE599 Cajamarca \n", + " mugration_country_lon \\\n", + "sample \n", + "Reference -100.445882 \n", + "GCA_009909635.1_ASM990963v1_genomic 97.745306 \n", + "GCA_009669545.1_ASM966954v1_genomic 104.999927 \n", + "GCA_009669805.1_ASM966980v1_genomic 104.999927 \n", + "GCA_009296005.1_ASM929600v1_genomic 97.745306 \n", + "... ... \n", + "NODE188 65.209320 \n", + "NODE189 97.745306 \n", + "NODE190 97.745306 \n", + "NODE191 97.745306 \n", + "NODE192 97.745306 \n", + "\n", + " mugration_country_lon_confidence \\\n", + "sample \n", + "Reference 1.000000 \n", + "GCA_009909635.1_ASM990963v1_genomic 1.000000 \n", + "GCA_009669545.1_ASM966954v1_genomic 1.000000 \n", + "GCA_009669805.1_ASM966980v1_genomic 1.000000 \n", + "GCA_009296005.1_ASM929600v1_genomic 1.000000 \n", + "... ... \n", + "NODE188 0.662908 \n", + "NODE189 0.984523 \n", + "NODE190 0.999691 \n", + "NODE191 0.980724 \n", + "NODE192 0.999722 \n", + "\n", + " mugration_province \\\n", + "sample \n", + "Reference Colorado \n", + "GCA_009909635.1_ASM990963v1_genomic Rostov Oblast \n", + "GCA_009669545.1_ASM966954v1_genomic Xinjiang \n", + "GCA_009669805.1_ASM966980v1_genomic Xinjiang \n", + "GCA_009296005.1_ASM929600v1_genomic Chechnya \n", + "... ... \n", + "NODE188 West Kazakhstan Region \n", + "NODE189 Astrakhan Oblast \n", + "NODE190 Astrakhan Oblast \n", + "NODE191 Republic of Kalmykia \n", + "NODE192 Republic of Kalmykia \n", "\n", " mugration_province_confidence \\\n", "sample \n", "Reference 1.00 \n", "GCA_009909635.1_ASM990963v1_genomic 1.00 \n", "GCA_009669545.1_ASM966954v1_genomic 1.00 \n", - "GCA_009669555.1_ASM966955v1_genomic 1.00 \n", - "GCA_009669565.1_ASM966956v1_genomic 1.00 \n", + "GCA_009669805.1_ASM966980v1_genomic 1.00 \n", + "GCA_009296005.1_ASM929600v1_genomic 1.00 \n", "... ... \n", - "NODE595 1.00 \n", - "NODE596 1.00 \n", - "NODE597 1.00 \n", - "NODE598 1.00 \n", - "NODE599 0.99 \n", + "NODE188 0.36 \n", + "NODE189 0.54 \n", + "NODE190 0.56 \n", + "NODE191 0.43 \n", + "NODE192 0.59 \n", + "\n", + " mugration_province_lat \\\n", + "sample \n", + "Reference 38.725178 \n", + "GCA_009909635.1_ASM990963v1_genomic 47.622245 \n", + "GCA_009669545.1_ASM966954v1_genomic 42.480495 \n", + "GCA_009669805.1_ASM966980v1_genomic 42.480495 \n", + "GCA_009296005.1_ASM929600v1_genomic 43.397615 \n", + "... ... \n", + "NODE188 49.556848 \n", + "NODE189 47.187819 \n", + "NODE190 47.187819 \n", + "NODE191 46.231302 \n", + "NODE192 46.231302 \n", + "\n", + " mugration_province_lat_confidence \\\n", + "sample \n", + "Reference 1.000000 \n", + "GCA_009909635.1_ASM990963v1_genomic 1.000000 \n", + "GCA_009669545.1_ASM966954v1_genomic 1.000000 \n", + "GCA_009669805.1_ASM966980v1_genomic 1.000000 \n", + "GCA_009296005.1_ASM929600v1_genomic 1.000000 \n", + "... ... \n", + "NODE188 0.359162 \n", + "NODE189 0.535277 \n", + "NODE190 0.560352 \n", + "NODE191 0.428943 \n", + "NODE192 0.591156 \n", + "\n", + " mugration_province_lon \\\n", + "sample \n", + "Reference -105.607716 \n", + "GCA_009909635.1_ASM990963v1_genomic 40.795794 \n", + "GCA_009669545.1_ASM966954v1_genomic 85.463346 \n", + "GCA_009669805.1_ASM966980v1_genomic 85.463346 \n", + "GCA_009296005.1_ASM929600v1_genomic 45.698501 \n", + "... ... \n", + "NODE188 50.222741 \n", + "NODE189 47.608851 \n", + "NODE190 47.608851 \n", + "NODE191 45.327574 \n", + "NODE192 45.327574 \n", + "\n", + " mugration_province_lon_confidence \\\n", + "sample \n", + "Reference 1.000000 \n", + "GCA_009909635.1_ASM990963v1_genomic 1.000000 \n", + "GCA_009669545.1_ASM966954v1_genomic 1.000000 \n", + "GCA_009669805.1_ASM966980v1_genomic 1.000000 \n", + "GCA_009296005.1_ASM929600v1_genomic 1.000000 \n", + "... ... \n", + "NODE188 0.359162 \n", + "NODE189 0.535277 \n", + "NODE190 0.560352 \n", + "NODE191 0.428943 \n", + "NODE192 0.591156 \n", "\n", " mugration_branch_major \\\n", "sample \n", "Reference 1.ORI \n", "GCA_009909635.1_ASM990963v1_genomic 2.MED \n", "GCA_009669545.1_ASM966954v1_genomic 0.ANT \n", - "GCA_009669555.1_ASM966955v1_genomic 0.ANT \n", - "GCA_009669565.1_ASM966956v1_genomic 0.ANT \n", + "GCA_009669805.1_ASM966980v1_genomic 0.ANT \n", + "GCA_009296005.1_ASM929600v1_genomic 2.MED \n", "... ... \n", - "NODE595 1.ORI \n", - "NODE596 1.ORI \n", - "NODE597 1.ORI \n", - "NODE598 1.ORI \n", - "NODE599 1.ORI \n", + "NODE188 2.MED \n", + "NODE189 2.MED \n", + "NODE190 2.MED \n", + "NODE191 2.MED \n", + "NODE192 2.MED \n", "\n", " mugration_branch_major_confidence \\\n", "sample \n", "Reference 1.0 \n", "GCA_009909635.1_ASM990963v1_genomic 1.0 \n", "GCA_009669545.1_ASM966954v1_genomic 1.0 \n", - "GCA_009669555.1_ASM966955v1_genomic 1.0 \n", - "GCA_009669565.1_ASM966956v1_genomic 1.0 \n", + "GCA_009669805.1_ASM966980v1_genomic 1.0 \n", + "GCA_009296005.1_ASM929600v1_genomic 1.0 \n", "... ... \n", - "NODE595 1.0 \n", - "NODE596 1.0 \n", - "NODE597 1.0 \n", - "NODE598 1.0 \n", - "NODE599 1.0 \n", + "NODE188 1.0 \n", + "NODE189 1.0 \n", + "NODE190 1.0 \n", + "NODE191 1.0 \n", + "NODE192 1.0 \n", "\n", " mugration_branch_minor \\\n", "sample \n", "Reference 1.ORI1 \n", "GCA_009909635.1_ASM990963v1_genomic 2.MED1 \n", "GCA_009669545.1_ASM966954v1_genomic 0.ANT1 \n", - "GCA_009669555.1_ASM966955v1_genomic 0.ANT1 \n", - "GCA_009669565.1_ASM966956v1_genomic 0.ANT1 \n", + "GCA_009669805.1_ASM966980v1_genomic 0.ANT1 \n", + "GCA_009296005.1_ASM929600v1_genomic 2.MED1 \n", "... ... \n", - "NODE595 1.ORI1 \n", - "NODE596 1.ORI1 \n", - "NODE597 1.ORI1 \n", - "NODE598 1.ORI1 \n", - "NODE599 1.ORI1 \n", + "NODE188 2.MED1 \n", + "NODE189 2.MED1 \n", + "NODE190 2.MED1 \n", + "NODE191 2.MED1 \n", + "NODE192 2.MED1 \n", "\n", " mugration_branch_minor_confidence \\\n", "sample \n", "Reference 1.0 \n", "GCA_009909635.1_ASM990963v1_genomic 1.0 \n", "GCA_009669545.1_ASM966954v1_genomic 1.0 \n", - "GCA_009669555.1_ASM966955v1_genomic 1.0 \n", - "GCA_009669565.1_ASM966956v1_genomic 1.0 \n", + "GCA_009669805.1_ASM966980v1_genomic 1.0 \n", + "GCA_009296005.1_ASM929600v1_genomic 1.0 \n", "... ... \n", - "NODE595 1.0 \n", - "NODE596 1.0 \n", - "NODE597 1.0 \n", - "NODE598 1.0 \n", - "NODE599 1.0 \n", + "NODE188 1.0 \n", + "NODE189 1.0 \n", + "NODE190 1.0 \n", + "NODE191 1.0 \n", + "NODE192 1.0 \n", "\n", " branch_number branch_support \\\n", "sample \n", "Reference 1 100.0 \n", "GCA_009909635.1_ASM990963v1_genomic 2 100.0 \n", "GCA_009669545.1_ASM966954v1_genomic 0 100.0 \n", - "GCA_009669555.1_ASM966955v1_genomic 0 100.0 \n", - "GCA_009669565.1_ASM966956v1_genomic 0 100.0 \n", + "GCA_009669805.1_ASM966980v1_genomic 0 100.0 \n", + "GCA_009296005.1_ASM929600v1_genomic 2 100.0 \n", "... ... ... \n", - "NODE595 NA 13.0 \n", - "NODE596 NA 13.0 \n", - "NODE597 NA 46.0 \n", - "NODE598 NA 34.0 \n", - "NODE599 NA 58.0 \n", + "NODE188 NA 100.0 \n", + "NODE189 NA 100.0 \n", + "NODE190 NA 100.0 \n", + "NODE191 NA 80.0 \n", + "NODE192 NA 100.0 \n", + "\n", + " branch_support_conf_category \\\n", + "sample \n", + "Reference HIGH \n", + "GCA_009909635.1_ASM990963v1_genomic HIGH \n", + "GCA_009669545.1_ASM966954v1_genomic HIGH \n", + "GCA_009669805.1_ASM966980v1_genomic HIGH \n", + "GCA_009296005.1_ASM929600v1_genomic HIGH \n", + "... ... \n", + "NODE188 HIGH \n", + "NODE189 HIGH \n", + "NODE190 HIGH \n", + "NODE191 LOW \n", + "NODE192 HIGH \n", "\n", " continent node_type geometry_size \\\n", "sample \n", "Reference North America terminal 1.0 \n", - "GCA_009909635.1_ASM990963v1_genomic Europe terminal 4.0 \n", - "GCA_009669545.1_ASM966954v1_genomic Asia terminal 105.0 \n", - "GCA_009669555.1_ASM966955v1_genomic Asia terminal 105.0 \n", - "GCA_009669565.1_ASM966956v1_genomic Asia terminal 105.0 \n", + "GCA_009909635.1_ASM990963v1_genomic Europe terminal 3.0 \n", + "GCA_009669545.1_ASM966954v1_genomic Asia terminal 8.0 \n", + "GCA_009669805.1_ASM966980v1_genomic Asia terminal 8.0 \n", + "GCA_009296005.1_ASM929600v1_genomic Europe terminal 2.0 \n", "... ... ... ... \n", - "NODE595 NA internal 18.0 \n", - "NODE596 NA internal 18.0 \n", - "NODE597 NA internal 18.0 \n", - "NODE598 NA internal 18.0 \n", - "NODE599 NA internal 18.0 \n", + "NODE188 NA internal 2.0 \n", + "NODE189 NA internal 3.0 \n", + "NODE190 NA internal 3.0 \n", + "NODE191 NA internal 2.0 \n", + "NODE192 NA internal 2.0 \n", "\n", " biosample_accession strain date_mean \\\n", "sample \n", "Reference SAMEA1705942 CO92 1992 \n", "GCA_009909635.1_ASM990963v1_genomic SAMN13632815 9_10 1923 \n", "GCA_009669545.1_ASM966954v1_genomic SAMN07722925 42126 2006 \n", - "GCA_009669555.1_ASM966955v1_genomic SAMN07722924 42123 2005 \n", - "GCA_009669565.1_ASM966956v1_genomic SAMN07722923 42118 2005 \n", + "GCA_009669805.1_ASM966980v1_genomic SAMN07722912 42092 1980 \n", + "GCA_009296005.1_ASM929600v1_genomic SAMN12991209 C-25 1953 \n", "... ... ... ... \n", - "NODE595 NA NA NA \n", - "NODE596 NA NA NA \n", - "NODE597 NA NA NA \n", - "NODE598 NA NA NA \n", - "NODE599 NA NA NA \n", + "NODE188 NA NA NA \n", + "NODE189 NA NA NA \n", + "NODE190 NA NA NA \n", + "NODE191 NA NA NA \n", + "NODE192 NA NA NA \n", "\n", " date_err date_bp_mean root_rtt_dist \\\n", "sample \n", - "Reference 0 29 7.31686e-05 \n", - "GCA_009909635.1_ASM990963v1_genomic 0 98 7.30501e-05 \n", - "GCA_009669545.1_ASM966954v1_genomic 0 15 5.41847e-05 \n", - "GCA_009669555.1_ASM966955v1_genomic 0 16 5.47035e-05 \n", - "GCA_009669565.1_ASM966956v1_genomic 0 16 5.4939e-05 \n", + "Reference 0 29 6.63842e-05 \n", + "GCA_009909635.1_ASM990963v1_genomic 0 98 6.77518e-05 \n", + "GCA_009669545.1_ASM966954v1_genomic 0 15 5.00169e-05 \n", + "GCA_009669805.1_ASM966980v1_genomic 0 41 5.00169e-05 \n", + "GCA_009296005.1_ASM929600v1_genomic 0 68 6.58604e-05 \n", "... ... ... ... \n", - "NODE595 NA NA 7.21466e-05 \n", - "NODE596 NA NA 7.23822e-05 \n", - "NODE597 NA NA 7.26743e-05 \n", - "NODE598 NA NA 7.21967e-05 \n", - "NODE599 NA NA 7.24215e-05 \n", + "NODE188 NA NA 6.85262e-05 \n", + "NODE189 NA NA 6.7323e-05 \n", + "NODE190 NA NA 6.82667e-05 \n", + "NODE191 NA NA 6.73466e-05 \n", + "NODE192 NA NA 6.75825e-05 \n", "\n", " clade_rtt_dist \\\n", "sample \n", - "Reference 6.3815e-06 \n", - "GCA_009909635.1_ASM990963v1_genomic 9.6582e-06 \n", - "GCA_009669545.1_ASM966954v1_genomic 1.15566e-05 \n", - "GCA_009669555.1_ASM966955v1_genomic 1.20754e-05 \n", - "GCA_009669565.1_ASM966956v1_genomic 1.23109e-05 \n", + "Reference 5.7539e-06 \n", + "GCA_009909635.1_ASM990963v1_genomic 9.4529e-06 \n", + "GCA_009669545.1_ASM966954v1_genomic 1.11117e-05 \n", + "GCA_009669805.1_ASM966980v1_genomic 1.11117e-05 \n", + "GCA_009296005.1_ASM929600v1_genomic 7.5615e-06 \n", "... ... \n", - "NODE595 5.3595e-06 \n", - "NODE596 5.5951e-06 \n", - "NODE597 5.8872e-06 \n", - "NODE598 5.4096e-06 \n", - "NODE599 5.6344e-06 \n", + "NODE188 1.02273e-05 \n", + "NODE189 9.0241e-06 \n", + "NODE190 9.9678e-06 \n", + "NODE191 9.0477e-06 \n", + "NODE192 9.2836e-06 \n", "\n", " timetree_num_date_confidence \\\n", "sample \n", "Reference [1992.0, 1992.0] \n", "GCA_009909635.1_ASM990963v1_genomic [1923.0, 1923.0] \n", "GCA_009669545.1_ASM966954v1_genomic [2006.0, 2006.0] \n", - "GCA_009669555.1_ASM966955v1_genomic [2005.0, 2005.0] \n", - "GCA_009669565.1_ASM966956v1_genomic [2005.0, 2005.0] \n", + "GCA_009669805.1_ASM966980v1_genomic [1980.0, 1980.0] \n", + "GCA_009296005.1_ASM929600v1_genomic [1953.0, 1953.0] \n", "... ... \n", - "NODE595 [1885.0, 1929.0] \n", - "NODE596 [1885.0, 1929.0] \n", - "NODE597 [1906.0, 1976.0] \n", - "NODE598 [1909.0, 1975.0] \n", - "NODE599 [1927.0, 1996.0] \n", + "NODE188 [1961.0, 1990.0] \n", + "NODE189 [1892.0, 1972.0] \n", + "NODE190 [1985.0, 2014.0] \n", + "NODE191 [1932.0, 1984.0] \n", + "NODE192 [1963.0, 1986.0] \n", "\n", " branch_major_conf_category \\\n", "sample \n", "Reference HIGH \n", "GCA_009909635.1_ASM990963v1_genomic HIGH \n", "GCA_009669545.1_ASM966954v1_genomic HIGH \n", - "GCA_009669555.1_ASM966955v1_genomic HIGH \n", - "GCA_009669565.1_ASM966956v1_genomic HIGH \n", + "GCA_009669805.1_ASM966980v1_genomic HIGH \n", + "GCA_009296005.1_ASM929600v1_genomic HIGH \n", "... ... \n", - "NODE595 HIGH \n", - "NODE596 HIGH \n", - "NODE597 HIGH \n", - "NODE598 HIGH \n", - "NODE599 HIGH \n", + "NODE188 HIGH \n", + "NODE189 HIGH \n", + "NODE190 HIGH \n", + "NODE191 HIGH \n", + "NODE192 HIGH \n", "\n", " branch_minor_conf_category \\\n", "sample \n", "Reference HIGH \n", "GCA_009909635.1_ASM990963v1_genomic HIGH \n", "GCA_009669545.1_ASM966954v1_genomic HIGH \n", - "GCA_009669555.1_ASM966955v1_genomic HIGH \n", - "GCA_009669565.1_ASM966956v1_genomic HIGH \n", + "GCA_009669805.1_ASM966980v1_genomic HIGH \n", + "GCA_009296005.1_ASM929600v1_genomic HIGH \n", "... ... \n", - "NODE595 HIGH \n", - "NODE596 HIGH \n", - "NODE597 HIGH \n", - "NODE598 HIGH \n", - "NODE599 HIGH \n", + "NODE188 HIGH \n", + "NODE189 HIGH \n", + "NODE190 HIGH \n", + "NODE191 HIGH \n", + "NODE192 HIGH \n", "\n", " country_conf_category \\\n", "sample \n", "Reference HIGH \n", "GCA_009909635.1_ASM990963v1_genomic HIGH \n", "GCA_009669545.1_ASM966954v1_genomic HIGH \n", - "GCA_009669555.1_ASM966955v1_genomic HIGH \n", - "GCA_009669565.1_ASM966956v1_genomic HIGH \n", + "GCA_009669805.1_ASM966980v1_genomic HIGH \n", + "GCA_009296005.1_ASM929600v1_genomic HIGH \n", "... ... \n", - "NODE595 HIGH \n", - "NODE596 HIGH \n", - "NODE597 HIGH \n", - "NODE598 HIGH \n", - "NODE599 HIGH \n", + "NODE188 LOW \n", + "NODE189 HIGH \n", + "NODE190 HIGH \n", + "NODE191 HIGH \n", + "NODE192 HIGH \n", "\n", " province_conf_category \n", "sample \n", "Reference HIGH \n", "GCA_009909635.1_ASM990963v1_genomic HIGH \n", "GCA_009669545.1_ASM966954v1_genomic HIGH \n", - "GCA_009669555.1_ASM966955v1_genomic HIGH \n", - "GCA_009669565.1_ASM966956v1_genomic HIGH \n", + "GCA_009669805.1_ASM966980v1_genomic HIGH \n", + "GCA_009296005.1_ASM929600v1_genomic HIGH \n", "... ... \n", - "NODE595 HIGH \n", - "NODE596 HIGH \n", - "NODE597 HIGH \n", - "NODE598 HIGH \n", - "NODE599 HIGH \n", + "NODE188 LOW \n", + "NODE189 LOW \n", + "NODE190 LOW \n", + "NODE191 LOW \n", + "NODE192 LOW \n", "\n", - "[1201 rows x 27 columns]" + "[387 rows x 36 columns]" ] }, "metadata": {}, @@ -1748,6 +2021,8 @@ } ], "source": [ + "# Remember, order atters when dealing with confidence!\n", + "\n", "columns = [\n", " # Draw Divergence Tree\n", " \"branch_length\",\n", @@ -1755,10 +2030,18 @@ " \"timetree_date_calendar\",\n", " # Draw Country Map\n", " \"mugration_country\",\n", - " \"mugration_country_confidence\",\n", + " \"mugration_country_confidence\", \n", + " \"mugration_country_lat\", \n", + " \"mugration_country_lat_confidence\",\n", + " \"mugration_country_lon\", \n", + " \"mugration_country_lon_confidence\", \n", " # Draw Province Map\n", " \"mugration_province\",\n", - " \"mugration_province_confidence\",\n", + " \"mugration_province_confidence\", \n", + " \"mugration_province_lat\", \n", + " \"mugration_province_lat_confidence\",\n", + " \"mugration_province_lon\", \n", + " \"mugration_province_lon_confidence\", \n", " # Colors and Filters\n", " \"mugration_branch_major\",\n", " \"mugration_branch_major_confidence\",\n", @@ -1766,6 +2049,7 @@ " \"mugration_branch_minor_confidence\", \n", " \"branch_number\",\n", " \"branch_support\",\n", + " \"branch_support_conf_category\", \n", " \"continent\",\n", " \"node_type\",\n", " \"geometry_size\",\n", @@ -1829,7 +2113,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "{'branch_length': 4.0046e-06, 'num_date': 1992.0, 'country': 'United States of America', 'country_confidence': {'United States of America': 1.0}, 'province': 'Colorado', 'province_confidence': {'Colorado': 1.0}, 'branch_major': '1.ORI', 'branch_major_confidence': {'1.ORI': 1.0}, 'branch_minor': '1.ORI1', 'branch_minor_confidence': {'1.ORI1': 1.0}, 'branch_number': '1.0', 'branch_support': 100.0, 'continent': 'North America', 'node_type': 'terminal', 'biosample_accession': 'SAMEA1705942', 'strain': 'CO92', 'date_mean': 1992.0, 'date_err': 0.0, 'date_bp_mean': 29.0, 'root_rtt_dist': 7.316859999999999e-05, 'clade_rtt_dist': 6.3815e-06, 'num_date_confidence': [1992.0, 1992.0], 'branch_major_conf_category': 'HIGH', 'branch_minor_conf_category': 'HIGH', 'country_conf_category': 'HIGH', 'province_conf_category': 'HIGH'}\n" + "{'branch_length': 3.7746e-06, 'num_date': 1992.0, 'country': 'United States of America', 'country_confidence': {'United States of America': 1.0}, 'country_lat': 39.7837304, 'country_lat_confidence': {39.7837304: 1.0}, 'country_lon': -100.4458825, 'country_lon_confidence': {-100.4458825: 1.0}, 'province': 'Colorado', 'province_confidence': {'Colorado': 1.0}, 'province_lat': 38.7251776, 'province_lat_confidence': {38.7251776: 1.0}, 'province_lon': -105.607716, 'province_lon_confidence': {-105.607716: 1.0}, 'branch_major': '1.ORI', 'branch_major_confidence': {'1.ORI': 1.0}, 'branch_minor': '1.ORI1', 'branch_minor_confidence': {'1.ORI1': 1.0}, 'branch_number': '1.0', 'branch_support': 100.0, 'branch_support_conf_category': 'HIGH', 'continent': 'North America', 'node_type': 'terminal', 'biosample_accession': 'SAMEA1705942', 'strain': 'CO92', 'date_mean': 1992.0, 'date_err': 0.0, 'date_bp_mean': 29.0, 'root_rtt_dist': 6.638419999999997e-05, 'clade_rtt_dist': 5.753899999999999e-06, 'num_date_confidence': [1992.0, 1992.0], 'branch_major_conf_category': 'HIGH', 'branch_minor_conf_category': 'HIGH', 'country_conf_category': 'HIGH', 'province_conf_category': 'HIGH'}\n" ] } ], @@ -1878,19 +2162,35 @@ "output_type": "stream", "text": [ "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/config/auspice_config.json'...\n", - "Validation success.\n", - "{'key': 'node_type', 'title': 'Node Type', 'type': 'categorical', 'scale': [['internal', '#000000'], ['terminal', '#000000']]}\n", - "{'key': 'node_type', 'title': 'Node Type', 'type': 'categorical', 'scale': [['internal', '#000000'], ['terminal', '#000000']]}\n", + "Validation success.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: These values for trait province were not specified in your provided color scale: vietnam, turkmenistan, kyrgyzstan, kazakhstan, uganda, georgia, democratic republic of the congo, madagascar, myanmar. Auspice will create colors for them.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Validating produced JSON\n", - "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/all.json'...\n", + "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/all.json'...\n", "Validating that the JSON is internally consistent...\n", - "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/all.json' succeeded.\n", + "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/all.json' succeeded.\n", "Validation successful for local JSON.\n", "\n" ] } ], "source": [ + "import sys, importlib\n", + "importlib.reload(sys.modules['functions'])\n", + "from functions import auspice_export\n", + "\n", "auspice_dict = auspice_export(\n", " tree=divtree,\n", " augur_json_paths=[out_path_augur_json], \n", @@ -1914,10 +2214,15 @@ "for i in range(0, len(auspice_dict_copy[\"meta\"][\"colorings\"])):\n", " coloring = auspice_dict_copy[\"meta\"][\"colorings\"][i]\n", " for key in coloring:\n", + " # Node type as internal or terminal\n", " if coloring[key] == \"node_type\":\n", - " auspice_dict[\"meta\"][\"colorings\"][i]['scale'] = [['internal', '#000000'], ['terminal', '#000000']]\n", - " print(auspice_dict[\"meta\"][\"colorings\"][i])\n", - "\n", + " auspice_dict[\"meta\"][\"colorings\"][i]['scale'] = [['internal', '#FFFFFF'], ['terminal', '#000000']]\n", + " #print(auspice_dict[\"meta\"][\"colorings\"][i])\n", + " # Confidence category\n", + " if \"conf_category\" in coloring[key]:\n", + " auspice_dict[\"meta\"][\"colorings\"][i]['scale'] = [['LOW', '#FFFFFF'], ['HIGH', '#000000']]\n", + " #print(auspice_dict[\"meta\"][\"colorings\"][i])\n", + " \n", "# Write outputs - For Local Rendering\n", "out_path_auspice_local_json = os.path.join(auspice_dir, \"all.json\" )\n", "utils.write_json(data=auspice_dict, file_name=out_path_auspice_local_json, indent=JSON_INDENT, include_version=False)\n", @@ -1948,7 +2253,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.PRE\n" + "0.PRE #8000ff\n" ] }, { @@ -1970,18 +2275,22 @@ "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/config/auspice_config.json'...\n", "Validation success.\n", "Validating produced JSON\n", - "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/0.PRE.json'...\n", + "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/0.PRE.json'...\n", "Validating that the JSON is internally consistent...\n", - "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/0.PRE.json' succeeded.\n", + "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/0.PRE.json' succeeded, but there were warnings you may want to resolve.\n", "Validation successful for local JSON.\n", "\n", - "0.ANT4\n" + "0.ANT4 #1996f3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_support_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_support_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", @@ -1995,18 +2304,22 @@ "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/config/auspice_config.json'...\n", "Validation success.\n", "Validating produced JSON\n", - "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/0.ANT4.json'...\n", + "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/0.ANT4.json'...\n", "Validating that the JSON is internally consistent...\n", - "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/0.ANT4.json' succeeded.\n", + "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/0.ANT4.json' succeeded, but there were warnings you may want to resolve.\n", "Validation successful for local JSON.\n", "\n", - "0.PE\n" + "0.PE #4c4ffc\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_minor_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_minor_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", @@ -2020,18 +2333,19 @@ "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/config/auspice_config.json'...\n", "Validation success.\n", "Validating produced JSON\n", - "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/0.PE.json'...\n", + "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/0.PE.json'...\n", "Validating that the JSON is internally consistent...\n", - "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/0.PE.json' succeeded.\n", + "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/0.PE.json' succeeded, but there were warnings you may want to resolve.\n", "Validation successful for local JSON.\n", - "\n", - "0.ANT\n" + "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", @@ -2042,21 +2356,26 @@ "name": "stdout", "output_type": "stream", "text": [ + "0.ANT #1996f3\n", "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/config/auspice_config.json'...\n", "Validation success.\n", "Validating produced JSON\n", - "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/0.ANT.json'...\n", + "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/0.ANT.json'...\n", "Validating that the JSON is internally consistent...\n", - "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/0.ANT.json' succeeded.\n", + "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/0.ANT.json' succeeded, but there were warnings you may want to resolve.\n", "Validation successful for local JSON.\n", "\n", - "1.PRE\n" + "1.PRE #e6ce74\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "WARNING: These values for trait province were not specified in your provided color scale: kyrgyzstan. Auspice will create colors for them.\n", + "\n", + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", @@ -2070,43 +2389,53 @@ "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/config/auspice_config.json'...\n", "Validation success.\n", "Validating produced JSON\n", - "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/1.PRE.json'...\n", + "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/1.PRE.json'...\n", "Validating that the JSON is internally consistent...\n", - "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/1.PRE.json' succeeded.\n", + "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/1.PRE.json' succeeded, but there were warnings you may want to resolve.\n", "Validation successful for local JSON.\n", "\n", - "1.ANT\n" + "1.ANT #ff964f\n", + "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/config/auspice_config.json'...\n", + "Validation success.\n", + "Validating produced JSON\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", - "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n" + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "WARNING: These values for trait province were not specified in your provided color scale: democratic republic of the congo, uganda. Auspice will create colors for them.\n", + "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/config/auspice_config.json'...\n", - "Validation success.\n", - "Validating produced JSON\n", - "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/1.ANT.json'...\n", + "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/1.ANT.json'...\n", "Validating that the JSON is internally consistent...\n", - "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/1.ANT.json' succeeded.\n", + "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/1.ANT.json' succeeded, but there were warnings you may want to resolve.\n", "Validation successful for local JSON.\n", "\n", - "1.IN\n" + "1.IN #ff4f28\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_minor_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_support_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_support_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_minor_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", @@ -2120,18 +2449,22 @@ "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/config/auspice_config.json'...\n", "Validation success.\n", "Validating produced JSON\n", - "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/1.IN.json'...\n", + "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/1.IN.json'...\n", "Validating that the JSON is internally consistent...\n", - "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/1.IN.json' succeeded.\n", + "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/1.IN.json' succeeded, but there were warnings you may want to resolve.\n", "Validation successful for local JSON.\n", "\n", - "1.ORI\n" + "1.ORI #ff0000\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"country_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"country_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", @@ -2143,20 +2476,38 @@ "output_type": "stream", "text": [ "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/config/auspice_config.json'...\n", - "Validation success.\n", + "Validation success.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: These values for trait province were not specified in your provided color scale: vietnam, madagascar, myanmar. Auspice will create colors for them.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Validating produced JSON\n", - "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/1.ORI.json'...\n", + "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/1.ORI.json'...\n", "Validating that the JSON is internally consistent...\n", - "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/1.ORI.json' succeeded.\n", + "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/1.ORI.json' succeeded, but there were warnings you may want to resolve.\n", "Validation successful for local JSON.\n", "\n", - "2.ANT\n" + "2.ANT #80ffb4\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_minor_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_minor_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", @@ -2170,18 +2521,19 @@ "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/config/auspice_config.json'...\n", "Validation success.\n", "Validating produced JSON\n", - "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/2.ANT.json'...\n", + "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/2.ANT.json'...\n", "Validating that the JSON is internally consistent...\n", - "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/2.ANT.json' succeeded.\n", + "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/2.ANT.json' succeeded, but there were warnings you may want to resolve.\n", "Validation successful for local JSON.\n", - "\n", - "2.MED\n" + "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", @@ -2192,21 +2544,38 @@ "name": "stdout", "output_type": "stream", "text": [ + "2.MED #b3f396\n", "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/config/auspice_config.json'...\n", - "Validation success.\n", + "Validation success.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING: These values for trait province were not specified in your provided color scale: turkmenistan, kazakhstan. Auspice will create colors for them.\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "Validating produced JSON\n", - "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/2.MED.json'...\n", + "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/2.MED.json'...\n", "Validating that the JSON is internally consistent...\n", - "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/2.MED.json' succeeded.\n", + "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/2.MED.json' succeeded, but there were warnings you may want to resolve.\n", "Validation successful for local JSON.\n", "\n", - "3.ANT\n" + "3.ANT #1acee3\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", @@ -2220,18 +2589,22 @@ "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/config/auspice_config.json'...\n", "Validation success.\n", "Validating produced JSON\n", - "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/3.ANT.json'...\n", + "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/3.ANT.json'...\n", "Validating that the JSON is internally consistent...\n", - "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/3.ANT.json' succeeded.\n", + "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/3.ANT.json' succeeded, but there were warnings you may want to resolve.\n", "Validation successful for local JSON.\n", "\n", - "4.ANT\n" + "4.ANT #4df3ce\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_support_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_support_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame\n", "\n", @@ -2245,23 +2618,38 @@ "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/config/auspice_config.json'...\n", "Validation success.\n", "Validating produced JSON\n", - "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/4.ANT.json'...\n", + "Validating schema of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/4.ANT.json'...\n", "Validating that the JSON is internally consistent...\n", - "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/full/filter5/4.ANT.json' succeeded.\n", + "Validation of '/mnt/c/Users/ktmea/Projects/plague-phylogeography/results/auspice/all/chromosome/prune/filter5/4.ANT.json' succeeded, but there were warnings you may want to resolve.\n", "Validation successful for local JSON.\n", "\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_minor_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_support_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"country_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_support_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_minor_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"branch_major_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n", + "\tWARNING: Color option \"country_conf_category\" specifies a hex code for \"LOW\" but this isn't ever seen on the tree nodes.\n" + ] } ], "source": [ "for branch in BRANCH_LIST:\n", - " print(branch)\n", " timetree_copy = copy.deepcopy(timetree)\n", " \n", " # Create the subtree df\n", " subtree_df = metadata_gdf[metadata_gdf[\"branch_minor\"].isin(BRANCH_LIST[branch])]\n", + " branch_major_color = subtree_df[\"branch_major_color\"][0]\n", " \n", " if len(subtree_df) < 2: continue\n", + " print(branch, branch_major_color)\n", "\n", " subtree_df.sort_values(\"timetree_coord_y\", inplace=True)\n", " subtree_tips = subtree_df[subtree_df[\"node_type\"] == \"terminal\"]\n", @@ -2312,7 +2700,7 @@ " \n", " out_path_augur_json = os.path.join(out_dir, \"{}.json\".format(branch) )\n", " utils.write_json(data=augur_dict, file_name=out_path_augur_json, indent=JSON_INDENT)\n", - " \n", + "\n", " auspice_dict = auspice_export(\n", " tree=subtree,\n", " augur_json_paths=[out_path_augur_json], \n", @@ -2332,13 +2720,18 @@ " ) \n", " \n", " # Last manual changes\n", - " branch_major_color = subtree_df[\"branch_major_color\"][0]\n", " auspice_dict_copy = copy.deepcopy(auspice_dict)\n", " for i in range(0, len(auspice_dict_copy[\"meta\"][\"colorings\"])):\n", " coloring = auspice_dict_copy[\"meta\"][\"colorings\"][i]\n", " for key in coloring:\n", + " # Node type as internal or terminal\n", " if coloring[key] == \"node_type\":\n", - " auspice_dict[\"meta\"][\"colorings\"][i]['scale'] = [['internal', '#000000'], ['terminal', branch_major_color]] \n", + " auspice_dict[\"meta\"][\"colorings\"][i]['scale'] = [['internal', '#FFFFFF'], ['terminal', branch_major_color]]\n", + " #print(auspice_dict[\"meta\"][\"colorings\"][i])\n", + " # Confidence category\n", + " if \"conf_category\" in coloring[key]:\n", + " auspice_dict[\"meta\"][\"colorings\"][i]['scale'] = [['LOW', '#FFFFFF'], ['HIGH', branch_major_color]]\n", + " #print(auspice_dict[\"meta\"][\"colorings\"][i]) \n", " \n", " # Write outputs - For Local Rendering\n", " out_path_auspice_local_json = os.path.join(auspice_dir, \"{}.json\".format(branch) )\n", diff --git a/workflow/notebooks/functions.py b/workflow/notebooks/functions.py index b642747a..c7ca07d3 100644 --- a/workflow/notebooks/functions.py +++ b/workflow/notebooks/functions.py @@ -305,7 +305,7 @@ def auspice_export( metadata_names, ) = export_v2.parse_node_data_and_metadata(tree, augur_json_paths, None) - # print(data_json["tree"] = convert_tree_to_json_structure(T.root, node_attrs) + # data_json["tree"] = convert_tree_to_json_structure(T.root, node_attrs) # Validate and load config file (could put this in try except) export_v2.validate_auspice_config_v2(auspice_config_path) @@ -363,11 +363,27 @@ def branch_attributes(tree_dict, sub_dict, df, label_col): node_type = df["node_type"][node["name"]] if node_type != "internal": continue - branch_labels = {col: df[col][node["name"]] for col in label_col} + branch_labels = {} + for col in label_col: + col_pretty = ( + col.replace("mugration_", "") + .replace("timetree_", "") + .replace("_", " ") + .title() + ) + branch_labels[col_pretty] = df[col][node["name"]] node["branch_attrs"]["labels"] = branch_labels - branch_labels = {col: df[col][root["name"]] for col in label_col} + branch_labels = {} + for col in label_col: + col_pretty = ( + col.replace("mugration_", "") + .replace("timetree_", "") + .replace("_", " ") + .title() + ) + branch_labels[col_pretty] = df[col][node["name"]] root["branch_attrs"]["labels"] = branch_labels return root