move functions into utils

ElcoK · Feb 12, 2019 · a0fbc67 · a0fbc67
1 parent 6ead881
commit a0fbc67
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diff --git a/docs/source/utils.rst b/docs/source/utils.rst
@@ -19,6 +19,8 @@
 
 .. autofunction:: gmtra.utils.sum_tuples
 
+.. autofunction:: gmtra.utils.set_prot_standard
+
 .. autofunction:: gmtra.utils.sensitivity_risk
 
 .. autofunction:: gmtra.utils.monetary_risk

diff --git a/figures/Figure 3, Figure 4 & Figure S5.ipynb b/figures/Figure 3, Figure 4 & Figure S5.ipynb
@@ -38,13 +38,11 @@
     "from tqdm import tqdm\n",
     "from matplotlib.lines import Line2D\n",
     "\n",
-    "from functions import sum_tuples,calc_risk_total,set_prot_standard,pluvial_design,pluvial_design_rail,get_value,get_mean,wbregion\n",
-    "\n",
     "plt.style.use('ggplot')\n",
     "pd.set_option('chained_assignment',None)\n",
     "\n",
     "sys.path.append(os.path.join( '..'))\n",
-    "from gmtra.utils import load_config\n",
+    "from gmtra.utils import load_config,sum_tuples,monetary_risk,set_prot_standard,pluvial_design,pluvial_design_rail,get_value,get_mean,wbregion\n",
     "data_path = load_config()['paths']['data']\n",
     "figure_path = load_config()['paths']['figures']"
    ]
@@ -370,10 +368,10 @@
     "        wb_agg = []\n",
     "    if (iter_ == 0) | (iter_ == 6):\n",
     "        RPS = [1/50,1/100,1/250,1/500,1/1000]\n",
-    "        wb_risk_road = pd.DataFrame(Cyc_wb_stats_road.apply(lambda x: calc_risk_total(x,'Cyc',RPS,events_Cyc),axis=1).tolist(),\n",
+    "        wb_risk_road = pd.DataFrame(Cyc_wb_stats_road.apply(lambda x: monetary_risk(x,RPS,events_Cyc),axis=1).tolist(),\n",
     "                               index=Cyc_wb_stats_road.index,\n",
     "             columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "        wb_risk_rail = pd.DataFrame(Cyc_wb_stats_rail.apply(lambda x: calc_risk_total(x,'Cyc',RPS,events_Cyc),axis=1).tolist(),\n",
+    "        wb_risk_rail = pd.DataFrame(Cyc_wb_stats_rail.apply(lambda x: monetary_risk(x,RPS,events_Cyc),axis=1).tolist(),\n",
     "                               index=Cyc_wb_stats_rail.index,\n",
     "             columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
     "        \n",
@@ -396,10 +394,10 @@
     "        \n",
     "    elif (iter_ == 1) | (iter_ == 7):\n",
     "        RPS = [1/250,1/475,1/975,1/1500,1/2475]\n",
-    "        wb_risk_road = pd.DataFrame(EQ_wb_stats_road.apply(lambda x: calc_risk_total(x,'EQ',RPS,events_EQ),axis=1).tolist(),\n",
+    "        wb_risk_road = pd.DataFrame(EQ_wb_stats_road.apply(lambda x: monetary_risk(x,RPS,events_EQ),axis=1).tolist(),\n",
     "                               index=EQ_wb_stats_road.index,\n",
     "             columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "        wb_risk_rail = pd.DataFrame(EQ_wb_stats_rail.apply(lambda x: calc_risk_total(x,'EQ',RPS,events_EQ),axis=1).tolist(),\n",
+    "        wb_risk_rail = pd.DataFrame(EQ_wb_stats_rail.apply(lambda x: monetary_risk(x,RPS,events_EQ),axis=1).tolist(),\n",
     "                               index=EQ_wb_stats_rail.index,\n",
     "             columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])        \n",
     "        wb_br_ro = pd.DataFrame(wb_road_bridge['EQ_risk'].apply(pd.Series))\n",
@@ -419,10 +417,10 @@
     "        wb_agg.append(wb_risk)\n",
     "    elif (iter_ == 2) | (iter_ == 8):\n",
     "        RPS = [1/5,1/10,1/20,1/50,1/75,1/100,1/200,1/250,1/500,1/1000]\n",
-    "        wb_risk_road = pd.DataFrame(PU_wb_stats_road.apply(lambda x: calc_risk_total(x,'PU',RPS,events_PU),axis=1).tolist(),\n",
+    "        wb_risk_road = pd.DataFrame(PU_wb_stats_road.apply(lambda x: monetary_risk(x,RPS,events_PU),axis=1).tolist(),\n",
     "                               index=PU_wb_stats_road.index,\n",
     "             columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "        wb_risk_rail = pd.DataFrame(PU_wb_stats_rail.apply(lambda x: calc_risk_total(x,'PU',RPS,events_PU),axis=1).tolist(),\n",
+    "        wb_risk_rail = pd.DataFrame(PU_wb_stats_rail.apply(lambda x: monetary_risk(x,RPS,events_PU),axis=1).tolist(),\n",
     "                               index=PU_wb_stats_rail.index,\n",
     "             columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])        \n",
     "        wb_br_ro = pd.DataFrame(wb_road_bridge['PU_risk'].apply(pd.Series))\n",
@@ -443,10 +441,10 @@
     "        \n",
     "    elif (iter_ == 3) | (iter_ == 9):\n",
     "        RPS = [1/5,1/10,1/20,1/50,1/75,1/100,1/200,1/250,1/500,1/1000]\n",
-    "        wb_risk_road = pd.DataFrame(FU_wb_stats_road.apply(lambda x: calc_risk_total(x,'FU',RPS,events_FU),axis=1).tolist(),\n",
+    "        wb_risk_road = pd.DataFrame(FU_wb_stats_road.apply(lambda x: monetary_risk(x,RPS,events_FU),axis=1).tolist(),\n",
     "                               index=FU_wb_stats_road.index,\n",
     "             columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "        wb_risk_rail = pd.DataFrame(FU_wb_stats_rail.apply(lambda x: calc_risk_total(x,'FU',RPS,events_FU),axis=1).tolist(),\n",
+    "        wb_risk_rail = pd.DataFrame(FU_wb_stats_rail.apply(lambda x: monetary_risk(x,RPS,events_FU),axis=1).tolist(),\n",
     "                               index=FU_wb_stats_rail.index,\n",
     "             columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])        \n",
     "        wb_br_ro = pd.DataFrame(wb_road_bridge['FU_risk'].apply(pd.Series))\n",
@@ -467,10 +465,10 @@
     "\n",
     "    elif (iter_ == 4) | (iter_ == 10):\n",
     "        RPS = [1/10,1/20,1/50,1/100,1/200,1/500,1/1000]\n",
-    "        wb_risk_road = pd.DataFrame(CF_wb_stats_road.apply(lambda x: calc_risk_total(x,'CF',RPS,events_CF),axis=1).tolist(),\n",
+    "        wb_risk_road = pd.DataFrame(CF_wb_stats_road.apply(lambda x: monetary_risk(x,RPS,events_CF),axis=1).tolist(),\n",
     "                               index=CF_wb_stats_road.index,\n",
     "             columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "        wb_risk_rail = pd.DataFrame(CF_wb_stats_rail.apply(lambda x: calc_risk_total(x,'CF',RPS,events_CF),axis=1).tolist(),\n",
+    "        wb_risk_rail = pd.DataFrame(CF_wb_stats_rail.apply(lambda x: monetary_risk(x,RPS,events_CF),axis=1).tolist(),\n",
     "                               index=CF_wb_stats_rail.index,\n",
     "             columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])        \n",
     "        wb_br_ro = pd.DataFrame(wb_road_bridge['CF_risk'].apply(pd.Series))\n",

diff --git a/figures/Figure 5.ipynb b/figures/Figure 5.ipynb
@@ -39,10 +39,9 @@
     "plt.style.use('ggplot')\n",
     "pd.set_option('chained_assignment',None)\n",
     "\n",
-    "from functions import sum_tuples,calc_risk_total,set_prot_standard,pluvial_design,pluvial_design_rail,gdp_lookup,get_value,get_mean,wbregion\n",
-    "\n",
     "sys.path.append(os.path.join( '..'))\n",
-    "from gmtra.utils import load_config\n",
+    "from gmtra.utils import load_config,sum_tuples,monetary_risk,set_prot_standard,pluvial_design,pluvial_design_rail,gdp_lookup,get_value,get_mean,wbregion\n",
+    "\n",
     "data_path = load_config()['paths']['data']\n",
     "figure_path = load_config()['paths']['figures']"
    ]
@@ -270,38 +269,38 @@
     "%%time\n",
     "tqdm.pandas()\n",
     "RPS = [1/5,1/10,1/20,1/50,1/75,1/100,1/200,1/250,1/500,1/1000]\n",
-    "country_risk_PU_road = pd.DataFrame(PU_country_stats.progress_apply(lambda x: calc_risk_total(x,'PU',RPS,events_PU),axis=1).tolist(),index=PU_country_stats.index,\n",
+    "country_risk_PU_road = pd.DataFrame(PU_country_stats.progress_apply(lambda x: monetary_risk(x,RPS,events_PU),axis=1).tolist(),index=PU_country_stats.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "country_risk_FU_road = pd.DataFrame(FU_country_stats.progress_apply(lambda x: calc_risk_total(x,'FU',RPS,events_FU),axis=1).tolist(),index=FU_country_stats.index,\n",
+    "country_risk_FU_road = pd.DataFrame(FU_country_stats.progress_apply(lambda x: monetary_risk(x,RPS,events_FU),axis=1).tolist(),index=FU_country_stats.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "country_risk_PU_rail = pd.DataFrame(PU_country_stats_rail.progress_apply(lambda x: calc_risk_total(x,'PU',RPS,events_PU),axis=1).tolist(),\n",
+    "country_risk_PU_rail = pd.DataFrame(PU_country_stats_rail.progress_apply(lambda x: monetary_risk(x,RPS,events_PU),axis=1).tolist(),\n",
     "                                    index=PU_country_stats_rail.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "country_risk_FU_rail = pd.DataFrame(FU_country_stats_rail.progress_apply(lambda x: calc_risk_total(x,'FU',RPS,events_FU),axis=1).tolist(),\n",
+    "country_risk_FU_rail = pd.DataFrame(FU_country_stats_rail.progress_apply(lambda x: monetary_risk(x,RPS,events_FU),axis=1).tolist(),\n",
     "                                    index=FU_country_stats_rail.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
     "\n",
     "RPS = [1/10,1/20,1/50,1/100,1/200,1/500,1/1000]\n",
-    "country_risk_CF_road = pd.DataFrame(CF_country_stats.progress_apply(lambda x: calc_risk_total(x,'CF',RPS,events_CF),axis=1).tolist(),\n",
+    "country_risk_CF_road = pd.DataFrame(CF_country_stats.progress_apply(lambda x: monetary_risk(x,RPS,events_CF),axis=1).tolist(),\n",
     "                       index=CF_country_stats.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "country_risk_CF_rail = pd.DataFrame(CF_country_stats_rail.progress_apply(lambda x: calc_risk_total(x,'CF',RPS,events_CF),axis=1).tolist(),\n",
+    "country_risk_CF_rail = pd.DataFrame(CF_country_stats_rail.progress_apply(lambda x: monetary_risk(x,RPS,events_CF),axis=1).tolist(),\n",
     "                       index=CF_country_stats_rail.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
     "\n",
     "RPS = [1/50,1/100,1/250,1/500,1/1000]\n",
-    "country_risk_EQ_road = pd.DataFrame(EQ_country_stats.progress_apply(lambda x: calc_risk_total(x,'EQ',RPS,events_EQ),axis=1).tolist(),\n",
+    "country_risk_EQ_road = pd.DataFrame(EQ_country_stats.progress_apply(lambda x: monetary_risk(x,RPS,events_EQ),axis=1).tolist(),\n",
     "                       index=EQ_country_stats.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "country_risk_EQ_rail = pd.DataFrame(EQ_country_stats_rail.progress_apply(lambda x: calc_risk_total(x,'EQ',RPS,events_EQ),axis=1).tolist(),\n",
+    "country_risk_EQ_rail = pd.DataFrame(EQ_country_stats_rail.progress_apply(lambda x: monetary_risk(x,RPS,events_EQ),axis=1).tolist(),\n",
     "                       index=EQ_country_stats_rail.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
     "\n",
     "RPS = [1/250,1/475,1/975,1/1500,1/2475]\n",
-    "country_risk_Cyc_road = pd.DataFrame(Cyc_country_stats.progress_apply(lambda x: calc_risk_total(x,'Cyc',RPS,events_Cyc),axis=1).tolist(),\n",
+    "country_risk_Cyc_road = pd.DataFrame(Cyc_country_stats.progress_apply(lambda x: monetary_risk(x,RPS,events_Cyc),axis=1).tolist(),\n",
     "                       index=Cyc_country_stats.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "country_risk_Cyc_rail = pd.DataFrame(Cyc_country_stats_rail.progress_apply(lambda x: calc_risk_total(x,'Cyc',RPS,events_Cyc),axis=1).tolist(),\n",
+    "country_risk_Cyc_rail = pd.DataFrame(Cyc_country_stats_rail.progress_apply(lambda x: monetary_risk(x,RPS,events_Cyc),axis=1).tolist(),\n",
     "                       index=Cyc_country_stats_rail.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])"
    ]

diff --git a/figures/Figure 6.ipynb b/figures/Figure 6.ipynb
@@ -39,13 +39,12 @@
     "import cartopy.crs as ccrs\n",
     "import cartopy\n",
     "\n",
-    "from functions import sum_tuples,calc_risk_total,set_prot_standard,pluvial_design,pluvial_design_1up,gdp_lookup,get_value,get_mean,wbregion\n",
-    "\n",
     "plt.style.use('ggplot')\n",
     "pd.set_option('chained_assignment',None)\n",
     "\n",
     "sys.path.append(os.path.join( '..'))\n",
-    "from gmtra.utils import load_config\n",
+    "from gmtra.utils import load_config,sum_tuples,monetary_risk,set_prot_standard,pluvial_design,pluvial_design_1up,gdp_lookup,get_value,get_mean,wbregion\n",
+    "\n",
     "data_path = load_config()['paths']['data']\n",
     "figure_path = load_config()['paths']['figures']"
    ]
@@ -204,22 +203,22 @@
    "outputs": [],
    "source": [
     "RPS = [1/5,1/10,1/20,1/50,1/75,1/100,1/200,1/250,1/500,1/1000]\n",
-    "wb_risk_PU = pd.DataFrame(PU_wb_stats.apply(lambda x: calc_risk_total(x,'PU',RPS,events_PU),axis=1).tolist(),index=PU_wb_stats.index,\n",
+    "wb_risk_PU = pd.DataFrame(PU_wb_stats.apply(lambda x: monetary_risk(x,RPS,events_PU),axis=1).tolist(),index=PU_wb_stats.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "wb_risk_PU_1up = pd.DataFrame(PU_wb_stats_1up.apply(lambda x: calc_risk_total(x,'PU',RPS,events_PU),axis=1).tolist(),index=PU_wb_stats_1up.index,\n",
+    "wb_risk_PU_1up = pd.DataFrame(PU_wb_stats_1up.apply(lambda x: monetary_risk(x,RPS,events_PU),axis=1).tolist(),index=PU_wb_stats_1up.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
     "\n",
-    "wb_risk_FU = pd.DataFrame(FU_wb_stats.apply(lambda x: calc_risk_total(x,'FU',RPS,events_FU),axis=1).tolist(),index=FU_wb_stats.index,\n",
+    "wb_risk_FU = pd.DataFrame(FU_wb_stats.apply(lambda x: monetary_risk(x,RPS,events_FU),axis=1).tolist(),index=FU_wb_stats.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "wb_risk_FU_1up = pd.DataFrame(FU_wb_stats_1up.apply(lambda x: calc_risk_total(x,'FU',RPS,events_FU),axis=1).tolist(),index=FU_wb_stats_1up.index,\n",
+    "wb_risk_FU_1up = pd.DataFrame(FU_wb_stats_1up.apply(lambda x: monetary_risk(x,RPS,events_FU),axis=1).tolist(),index=FU_wb_stats_1up.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
     "\n",
     "\n",
     "RPS = [1/10,1/20,1/50,1/100,1/200,1/500,1/1000]\n",
-    "wb_risk_CF = pd.DataFrame(CF_wb_stats.apply(lambda x: calc_risk_total(x,'CF',RPS,events_CF),axis=1).tolist(),\n",
+    "wb_risk_CF = pd.DataFrame(CF_wb_stats.apply(lambda x: monetary_risk(x,RPS,events_CF),axis=1).tolist(),\n",
     "                       index=CF_wb_stats.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "wb_risk_CF_1up = pd.DataFrame(CF_wb_stats_1up.apply(lambda x: calc_risk_total(x,'CF',RPS,events_CF),axis=1).tolist(),\n",
+    "wb_risk_CF_1up = pd.DataFrame(CF_wb_stats_1up.apply(lambda x: monetary_risk(x,RPS,events_CF),axis=1).tolist(),\n",
     "                       index=CF_wb_stats.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])"
    ]
@@ -273,22 +272,22 @@
     "%%time\n",
     "tqdm.pandas()\n",
     "RPS = [1/5,1/10,1/20,1/50,1/75,1/100,1/200,1/250,1/500,1/1000]\n",
-    "reg_risk_PU = pd.DataFrame(PU_reg_stats.progress_apply(lambda x: calc_risk_total(x,'PU',RPS,events_PU),axis=1).tolist(),index=PU_reg_stats.index,\n",
+    "reg_risk_PU = pd.DataFrame(PU_reg_stats.progress_apply(lambda x: monetary_risk(x,RPS,events_PU),axis=1).tolist(),index=PU_reg_stats.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "reg_risk_PU_1up = pd.DataFrame(PU_reg_stats_1up.progress_apply(lambda x: calc_risk_total(x,'PU',RPS,events_PU),axis=1).tolist(),index=PU_reg_stats_1up.index,\n",
+    "reg_risk_PU_1up = pd.DataFrame(PU_reg_stats_1up.progress_apply(lambda x: monetary_risk(x,RPS,events_PU),axis=1).tolist(),index=PU_reg_stats_1up.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
     "\n",
-    "reg_risk_FU = pd.DataFrame(FU_reg_stats.progress_apply(lambda x: calc_risk_total(x,'FU',RPS,events_FU),axis=1).tolist(),index=FU_reg_stats.index,\n",
+    "reg_risk_FU = pd.DataFrame(FU_reg_stats.progress_apply(lambda x: monetary_risk(x,RPS,events_FU),axis=1).tolist(),index=FU_reg_stats.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "reg_risk_FU_1up = pd.DataFrame(FU_reg_stats_1up.progress_apply(lambda x: calc_risk_total(x,'FU',RPS,events_FU),axis=1).tolist(),index=FU_reg_stats_1up.index,\n",
+    "reg_risk_FU_1up = pd.DataFrame(FU_reg_stats_1up.progress_apply(lambda x: monetary_risk(x,RPS,events_FU),axis=1).tolist(),index=FU_reg_stats_1up.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
     "\n",
     "\n",
     "RPS = [1/10,1/20,1/50,1/100,1/200,1/500,1/1000]\n",
-    "reg_risk_CF = pd.DataFrame(CF_reg_stats.progress_apply(lambda x: calc_risk_total(x,'CF',RPS,events_CF),axis=1).tolist(),\n",
+    "reg_risk_CF = pd.DataFrame(CF_reg_stats.progress_apply(lambda x: monetary_risk(x,RPS,events_CF),axis=1).tolist(),\n",
     "                       index=CF_reg_stats.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])\n",
-    "reg_risk_CF_1up = pd.DataFrame(CF_reg_stats_1up.progress_apply(lambda x: calc_risk_total(x,'CF',RPS,events_CF),axis=1).tolist(),\n",
+    "reg_risk_CF_1up = pd.DataFrame(CF_reg_stats_1up.progress_apply(lambda x: monetary_risk(x,RPS,events_CF),axis=1).tolist(),\n",
     "                       index=CF_reg_stats.index,\n",
     "     columns=['perc_0','perc_20','perc_40','perc_50','perc_60','perc_80','perc_100'])"
    ]