Repository: janjoch/interplot (language: python)

Issue:
GitHub language detection
only detect .py .ipynb and .md


Discussion / hints:

Fix patch:
diff --git a/README.md b/README.md
index b8e3694..af593d1 100644
--- a/README.md
+++ b/README.md
@@ -69,7 +69,7 @@ Supported
         [matplotlib figure "Preset Title"]
         ```
 
-    - The ```interplot.Plot``` class for full control
+    - The `interplot.Plot` class for full control
         ```python
         >>> fig = interplot.Plot(
         >>>     interactive=True,
diff --git a/demo/3.interplot_preconfiguration.ipynb b/demo/3.interplot_preconfiguration.ipynb
index b6a9e36..f4066ad 100644
--- a/demo/3.interplot_preconfiguration.ipynb
+++ b/demo/3.interplot_preconfiguration.ipynb
@@ -13,7 +13,9 @@
    "id": "210ca050-f455-42ec-8cb8-19e4dcf3c4ad",
    "metadata": {},
    "source": [
-    "Use the `conf` module variables to preconfigure the behavior of `interplot` in your current session."
+    "Use the `conf` module variables to preconfigure the behavior of `interplot` in your current session.\n",
+    "\n",
+    "For the full API reference, see the [Documentation](https://interplot.janjo.ch/api_conf.html)."
    ]
   },
   {
@@ -150,7 +152,7 @@
           "dash": "solid"
          },
          "marker": {
-          "color": "rgba(0,107,164,1.0)"
+          "color": "rgba(0,107,164,1.0000)"
          },
          "mode": "lines",
          "showlegend": false,
@@ -1417,11 +1419,11 @@
         }
        }
       },
-      "image/png": "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",
+      "image/png": "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",
       "text/html": [
-       "<div>                            <div id=\"eb56abdb-2967-4248-8d89-e3e9f588d457\" class=\"plotly-graph-div\" style=\"height:500px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"eb56abdb-2967-4248-8d89-e3e9f588d457\")) {                    Plotly.newPlot(                        \"eb56abdb-2967-4248-8d89-e3e9f588d457\",                        [{\"line\":{\"dash\":\"solid\"},\"marker\":{\"color\":\"rgba(0,107,164,1.0)\"},\"mode\":\"lines\",\"showlegend\":false,\"x\":[-12.566370614359172,-12.440706908215581,-12.315043202071989,-12.189379495928398,-12.063715789784805,-11.938052083641214,-11.812388377497623,-11.68672467135403,-11.561060965210439,-11.435397259066846,-11.309733552923255,-11.184069846779664,-11.058406140636071,-10.93274243449248,-10.807078728348888,-10.681415022205297,-10.555751316061706,-10.430087609918113,-10.304423903774522,-10.178760197630929,-10.053096491487338,-9.927432785343747,-9.801769079200154,-9.676105373056563,-9.55044166691297,-9.42477796076938,-9.299114254625788,-9.173450548482196,-9.047786842338603,-8.922123136195012,-8.79645943005142,-8.67079572390783,-8.545132017764237,-8.419468311620644,-8.293804605477053,-8.168140899333462,-8.042477193189871,-7.916813487046278,-7.791149780902686,-7.6654860747590945,-7.5398223686155035,-7.414158662471912,-7.28849495632832,-7.162831250184728,-7.037167544041136,-6.911503837897544,-6.785840131753953,-6.660176425610361,-6.534512719466769,-6.408849013323177,-6.283185307179585,-6.157521601035994,-6.031857894892402,-5.9061941887488105,-5.780530482605219,-5.654866776461627,-5.529203070318035,-5.403539364174444,-5.277875658030852,-5.15221195188726,-5.026548245743668,-4.900884539600076,-4.775220833456485,-4.649557127312893,-4.523893421169301,-4.39822971502571,-4.272566008882118,-4.146902302738527,-4.021238596594934,-3.8955748904513428,-3.7699111843077517,-3.644247478164159,-3.518583772020568,-3.392920065876975,-3.267256359733384,-3.141592653589793,-3.0159289474462003,-2.8902652413026093,-2.7646015351590165,-2.6389378290154255,-2.5132741228718345,-2.3876104167282417,-2.2619467105846507,-2.136283004441058,-2.010619298297467,-1.884955592153874,-1.759291886010283,-1.633628179866692,-1.5079644737230993,-1.3823007675795083,-1.2566370614359155,-1.1309733552923245,-1.0053096491487334,-0.8796459430051407,-0.7539822368615496,-0.6283185307179568,-0.5026548245743658,-0.3769911184307748,-0.25132741228718203,-0.12566370614359101,1.7763568394002505e-15,0.1256637061435928,0.2513274122871838,0.3769911184307766,0.5026548245743676,0.6283185307179604,0.7539822368615514,0.8796459430051424,1.0053096491487352,1.1309733552923262,1.256637061435919,1.38230076757951,1.5079644737231028,1.6336281798666938,1.7592918860102849,1.8849555921538776,2.0106192982974687,2.1362830044410615,2.2619467105846525,2.3876104167282435,2.5132741228718363,2.6389378290154273,2.76460153515902,2.890265241302611,3.015928947446202,3.141592653589795,3.267256359733386,3.3929200658769787,3.5185837720205697,3.6442474781641607,3.7699111843077517,3.8955748904513463,4.021238596594937,4.146902302738528,4.272566008882119,4.39822971502571,4.523893421169305,4.649557127312896,4.775220833456487,4.900884539600078,5.026548245743669,5.1522119518872636,5.277875658030855,5.403539364174446,5.529203070318037,5.654866776461628,5.780530482605222,5.906194188748813,6.031857894892404,6.157521601035995,6.283185307179586,6.408849013323181,6.534512719466772,6.660176425610363,6.785840131753954,6.911503837897545,7.037167544041139,7.16283125018473,7.2884949563283215,7.4141586624719125,7.5398223686155035,7.665486074759098,7.791149780902689,7.91681348704628,8.042477193189871,8.168140899333466,8.293804605477057,8.419468311620648,8.545132017764239,8.67079572390783,8.796459430051424,8.922123136195015,9.047786842338606,9.173450548482197,9.299114254625788,9.424777960769383,9.550441666912974,9.676105373056565,9.801769079200156,9.927432785343747,10.053096491487342,10.178760197630933,10.304423903774524,10.430087609918115,10.555751316061706,10.6814150222053,10.807078728348891,10.932742434492482,11.058406140636073,11.184069846779664,11.309733552923259,11.43539725906685,11.56106096521044,11.686724671354032,11.812388377497623,11.938052083641217,12.063715789784808,12.1893794959284,12.31504320207199,12.440706908215581,12.566370614359172],\"y\":[4.898587196589413e-16,0.12533323356430404,0.2486898871648556,0.3681245526846781,0.48175367410171627,0.5877852522924735,0.6845471059286885,0.7705132427757897,0.8443279255020152,0.90482705246602,0.9510565162951538,0.9822872507286886,0.9980267284282716,0.9980267284282716,0.9822872507286885,0.9510565162951534,0.9048270524660197,0.8443279255020146,0.7705132427757891,0.684547105928688,0.5877852522924728,0.4817536741017156,0.36812455268467725,0.24868988716485474,0.12533323356430318,-3.6739403974420594e-16,-0.1253332335643039,-0.2486898871648555,-0.36812455268467964,-0.48175367410171616,-0.5877852522924734,-0.6845471059286884,-0.7705132427757897,-0.844327925502016,-0.90482705246602,-0.9510565162951536,-0.9822872507286886,-0.9980267284282716,-0.9980267284282716,-0.9822872507286885,-0.9510565162951534,-0.9048270524660194,-0.8443279255020147,-0.7705132427757887,-0.684547105928688,-0.5877852522924722,-0.48175367410171493,-0.3681245526846774,-0.24868988716485405,-0.1253332335643033,1.133107779529596e-15,0.12533323356430467,0.24868988716485535,0.36812455268467864,0.4817536741017161,0.587785252292474,0.6845471059286896,0.7705132427757896,0.8443279255020155,0.9048270524660199,0.9510565162951539,0.9822872507286889,0.9980267284282716,0.9980267284282716,0.9822872507286885,0.9510565162951535,0.904827052466019,0.8443279255020147,0.7705132427757881,0.6845471059286881,0.587785252292473,0.4817536741017142,0.36812455268467753,0.2486898871648533,0.12533323356430343,-1.2246467991473532e-16,-0.1253332335643054,-0.24868988716485524,-0.36812455268467936,-0.481753674101716,-0.5877852522924732,-0.6845471059286895,-0.7705132427757896,-0.844327925502016,-0.9048270524660199,-0.9510565162951542,-0.9822872507286889,-0.9980267284282716,-0.9980267284282714,-0.9822872507286885,-0.951056516295153,-0.904827052466019,-0.8443279255020149,-0.7705132427757883,-0.6845471059286882,-0.5877852522924717,-0.4817536741017143,-0.3681245526846776,-0.2486898871648534,-0.12533323356430354,1.7763568394002505e-15,0.12533323356430529,0.24868988716485513,0.36812455268467925,0.4817536741017159,0.5877852522924746,0.6845471059286894,0.7705132427757895,0.8443279255020157,0.9048270524660198,0.9510565162951541,0.9822872507286888,0.9980267284282717,0.9980267284282714,0.9822872507286886,0.9510565162951531,0.9048270524660191,0.844327925502014,0.7705132427757884,0.6845471059286883,0.5877852522924718,0.48175367410171444,0.3681245526846761,0.24868988716485352,0.12533323356430365,-1.6538921594855151e-15,-0.1253332335643052,-0.24868988716485674,-0.36812455268467914,-0.48175367410171577,-0.587785252292473,-0.6845471059286906,-0.7705132427757905,-0.8443279255020157,-0.9048270524660198,-0.9510565162951535,-0.9822872507286892,-0.9980267284282717,-0.9980267284282714,-0.9822872507286886,-0.9510565162951536,-0.9048270524660184,-0.8443279255020141,-0.7705132427757885,-0.6845471059286883,-0.5877852522924732,-0.481753674101713,-0.36812455268467614,-0.24868988716485363,-0.1253332335643038,-2.4492935982947064e-16,0.1253332335643068,0.24868988716485663,0.3681245526846791,0.4817536741017157,0.5877852522924729,0.6845471059286905,0.7705132427757904,0.8443279255020156,0.9048270524660197,0.9510565162951534,0.9822872507286892,0.9980267284282717,0.9980267284282714,0.9822872507286886,0.9510565162951525,0.9048270524660185,0.8443279255020141,0.7705132427757886,0.6845471059286884,0.5877852522924705,0.48175367410171305,0.3681245526846763,0.24868988716485377,0.1253332335643039,-3.185319639056295e-15,-0.1253332335643067,-0.24868988716485646,-0.3681245526846789,-0.4817536741017156,-0.5877852522924757,-0.6845471059286905,-0.7705132427757904,-0.8443279255020156,-0.9048270524660197,-0.9510565162951545,-0.9822872507286892,-0.9980267284282717,-0.9980267284282714,-0.9822872507286886,-0.9510565162951526,-0.9048270524660185,-0.8443279255020142,-0.7705132427757886,-0.6845471059286885,-0.5877852522924706,-0.48175367410171316,-0.3681245526846764,-0.24868988716485388,-0.12533323356430404,-4.898587196589413e-16],\"type\":\"scatter\",\"xaxis\":\"x\",\"yaxis\":\"y\"}],                        {\"legend\":{\"traceorder\":\"normal\",\"title\":{}},\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0]},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0]},\"title\":{},\"height\":500,\"barmode\":\"group\"},                        {\"responsive\": true}                    ).then(function(){\n",
+       "<div>                            <div id=\"c5576e01-2613-4019-8313-6b823160e168\" class=\"plotly-graph-div\" style=\"height:500px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"c5576e01-2613-4019-8313-6b823160e168\")) {                    Plotly.newPlot(                        \"c5576e01-2613-4019-8313-6b823160e168\",                        [{\"line\":{\"dash\":\"solid\"},\"marker\":{\"color\":\"rgba(0,107,164,1.0000)\"},\"mode\":\"lines\",\"showlegend\":false,\"x\":[-12.566370614359172,-12.440706908215581,-12.315043202071989,-12.189379495928398,-12.063715789784805,-11.938052083641214,-11.812388377497623,-11.68672467135403,-11.561060965210439,-11.435397259066846,-11.309733552923255,-11.184069846779664,-11.058406140636071,-10.93274243449248,-10.807078728348888,-10.681415022205297,-10.555751316061706,-10.430087609918113,-10.304423903774522,-10.178760197630929,-10.053096491487338,-9.927432785343747,-9.801769079200154,-9.676105373056563,-9.55044166691297,-9.42477796076938,-9.299114254625788,-9.173450548482196,-9.047786842338603,-8.922123136195012,-8.79645943005142,-8.67079572390783,-8.545132017764237,-8.419468311620644,-8.293804605477053,-8.168140899333462,-8.042477193189871,-7.916813487046278,-7.791149780902686,-7.6654860747590945,-7.5398223686155035,-7.414158662471912,-7.28849495632832,-7.162831250184728,-7.037167544041136,-6.911503837897544,-6.785840131753953,-6.660176425610361,-6.534512719466769,-6.408849013323177,-6.283185307179585,-6.157521601035994,-6.031857894892402,-5.9061941887488105,-5.780530482605219,-5.654866776461627,-5.529203070318035,-5.403539364174444,-5.277875658030852,-5.15221195188726,-5.026548245743668,-4.900884539600076,-4.775220833456485,-4.649557127312893,-4.523893421169301,-4.39822971502571,-4.272566008882118,-4.146902302738527,-4.021238596594934,-3.8955748904513428,-3.7699111843077517,-3.644247478164159,-3.518583772020568,-3.392920065876975,-3.267256359733384,-3.141592653589793,-3.0159289474462003,-2.8902652413026093,-2.7646015351590165,-2.6389378290154255,-2.5132741228718345,-2.3876104167282417,-2.2619467105846507,-2.136283004441058,-2.010619298297467,-1.884955592153874,-1.759291886010283,-1.633628179866692,-1.5079644737230993,-1.3823007675795083,-1.2566370614359155,-1.1309733552923245,-1.0053096491487334,-0.8796459430051407,-0.7539822368615496,-0.6283185307179568,-0.5026548245743658,-0.3769911184307748,-0.25132741228718203,-0.12566370614359101,1.7763568394002505e-15,0.1256637061435928,0.2513274122871838,0.3769911184307766,0.5026548245743676,0.6283185307179604,0.7539822368615514,0.8796459430051424,1.0053096491487352,1.1309733552923262,1.256637061435919,1.38230076757951,1.5079644737231028,1.6336281798666938,1.7592918860102849,1.8849555921538776,2.0106192982974687,2.1362830044410615,2.2619467105846525,2.3876104167282435,2.5132741228718363,2.6389378290154273,2.76460153515902,2.890265241302611,3.015928947446202,3.141592653589795,3.267256359733386,3.3929200658769787,3.5185837720205697,3.6442474781641607,3.7699111843077517,3.8955748904513463,4.021238596594937,4.146902302738528,4.272566008882119,4.39822971502571,4.523893421169305,4.649557127312896,4.775220833456487,4.900884539600078,5.026548245743669,5.1522119518872636,5.277875658030855,5.403539364174446,5.529203070318037,5.654866776461628,5.780530482605222,5.906194188748813,6.031857894892404,6.157521601035995,6.283185307179586,6.408849013323181,6.534512719466772,6.660176425610363,6.785840131753954,6.911503837897545,7.037167544041139,7.16283125018473,7.2884949563283215,7.4141586624719125,7.5398223686155035,7.665486074759098,7.791149780902689,7.91681348704628,8.042477193189871,8.168140899333466,8.293804605477057,8.419468311620648,8.545132017764239,8.67079572390783,8.796459430051424,8.922123136195015,9.047786842338606,9.173450548482197,9.299114254625788,9.424777960769383,9.550441666912974,9.676105373056565,9.801769079200156,9.927432785343747,10.053096491487342,10.178760197630933,10.304423903774524,10.430087609918115,10.555751316061706,10.6814150222053,10.807078728348891,10.932742434492482,11.058406140636073,11.184069846779664,11.309733552923259,11.43539725906685,11.56106096521044,11.686724671354032,11.812388377497623,11.938052083641217,12.063715789784808,12.1893794959284,12.31504320207199,12.440706908215581,12.566370614359172],\"y\":[4.898587196589413e-16,0.12533323356430404,0.2486898871648556,0.3681245526846781,0.48175367410171627,0.5877852522924735,0.6845471059286885,0.7705132427757897,0.8443279255020152,0.90482705246602,0.9510565162951538,0.9822872507286886,0.9980267284282716,0.9980267284282716,0.9822872507286885,0.9510565162951534,0.9048270524660197,0.8443279255020146,0.7705132427757891,0.684547105928688,0.5877852522924728,0.4817536741017156,0.36812455268467725,0.24868988716485474,0.12533323356430318,-3.6739403974420594e-16,-0.1253332335643039,-0.2486898871648555,-0.36812455268467964,-0.48175367410171616,-0.5877852522924734,-0.6845471059286884,-0.7705132427757897,-0.844327925502016,-0.90482705246602,-0.9510565162951536,-0.9822872507286886,-0.9980267284282716,-0.9980267284282716,-0.9822872507286885,-0.9510565162951534,-0.9048270524660194,-0.8443279255020147,-0.7705132427757887,-0.684547105928688,-0.5877852522924722,-0.48175367410171493,-0.3681245526846774,-0.24868988716485405,-0.1253332335643033,1.133107779529596e-15,0.12533323356430467,0.24868988716485535,0.36812455268467864,0.4817536741017161,0.587785252292474,0.6845471059286896,0.7705132427757896,0.8443279255020155,0.9048270524660199,0.9510565162951539,0.9822872507286889,0.9980267284282716,0.9980267284282716,0.9822872507286885,0.9510565162951535,0.904827052466019,0.8443279255020147,0.7705132427757881,0.6845471059286881,0.587785252292473,0.4817536741017142,0.36812455268467753,0.2486898871648533,0.12533323356430343,-1.2246467991473532e-16,-0.1253332335643054,-0.24868988716485524,-0.36812455268467936,-0.481753674101716,-0.5877852522924732,-0.6845471059286895,-0.7705132427757896,-0.844327925502016,-0.9048270524660199,-0.9510565162951542,-0.9822872507286889,-0.9980267284282716,-0.9980267284282714,-0.9822872507286885,-0.951056516295153,-0.904827052466019,-0.8443279255020149,-0.7705132427757883,-0.6845471059286882,-0.5877852522924717,-0.4817536741017143,-0.3681245526846776,-0.2486898871648534,-0.12533323356430354,1.7763568394002505e-15,0.12533323356430529,0.24868988716485513,0.36812455268467925,0.4817536741017159,0.5877852522924746,0.6845471059286894,0.7705132427757895,0.8443279255020157,0.9048270524660198,0.9510565162951541,0.9822872507286888,0.9980267284282717,0.9980267284282714,0.9822872507286886,0.9510565162951531,0.9048270524660191,0.844327925502014,0.7705132427757884,0.6845471059286883,0.5877852522924718,0.48175367410171444,0.3681245526846761,0.24868988716485352,0.12533323356430365,-1.6538921594855151e-15,-0.1253332335643052,-0.24868988716485674,-0.36812455268467914,-0.48175367410171577,-0.587785252292473,-0.6845471059286906,-0.7705132427757905,-0.8443279255020157,-0.9048270524660198,-0.9510565162951535,-0.9822872507286892,-0.9980267284282717,-0.9980267284282714,-0.9822872507286886,-0.9510565162951536,-0.9048270524660184,-0.8443279255020141,-0.7705132427757885,-0.6845471059286883,-0.5877852522924732,-0.481753674101713,-0.36812455268467614,-0.24868988716485363,-0.1253332335643038,-2.4492935982947064e-16,0.1253332335643068,0.24868988716485663,0.3681245526846791,0.4817536741017157,0.5877852522924729,0.6845471059286905,0.7705132427757904,0.8443279255020156,0.9048270524660197,0.9510565162951534,0.9822872507286892,0.9980267284282717,0.9980267284282714,0.9822872507286886,0.9510565162951525,0.9048270524660185,0.8443279255020141,0.7705132427757886,0.6845471059286884,0.5877852522924705,0.48175367410171305,0.3681245526846763,0.24868988716485377,0.1253332335643039,-3.185319639056295e-15,-0.1253332335643067,-0.24868988716485646,-0.3681245526846789,-0.4817536741017156,-0.5877852522924757,-0.6845471059286905,-0.7705132427757904,-0.8443279255020156,-0.9048270524660197,-0.9510565162951545,-0.9822872507286892,-0.9980267284282717,-0.9980267284282714,-0.9822872507286886,-0.9510565162951526,-0.9048270524660185,-0.8443279255020142,-0.7705132427757886,-0.6845471059286885,-0.5877852522924706,-0.48175367410171316,-0.3681245526846764,-0.24868988716485388,-0.12533323356430404,-4.898587196589413e-16],\"type\":\"scatter\",\"xaxis\":\"x\",\"yaxis\":\"y\"}],                        {\"legend\":{\"traceorder\":\"normal\",\"title\":{}},\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0]},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0]},\"title\":{},\"height\":500,\"barmode\":\"group\"},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('eb56abdb-2967-4248-8d89-e3e9f588d457');\n",
+       "var gd = document.getElementById('c5576e01-2613-4019-8313-6b823160e168');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -1446,7 +1448,7 @@
        "                        })                };                });            </script>        </div>"
       ],
       "text/plain": [
-       "<interplot.plot.Plot at 0x7fbf0104f490>"
+       "<interplot.plot.Plot at 0x7f793195f430>"
       ]
      },
      "execution_count": 4,
@@ -1486,7 +1488,19 @@
     "ip.conf.EXPORT_FORMAT = \"pdf\"\n",
     "\n",
     "ip.conf.EXPORT_REPLACE[\" \"] = \"_\"  # rewrite spaces as underscores\n",
-    "ip.conf.EXPORT_REPLACE[\"^.*$\"] = lambda s: s.group().lower()  # rewrite auto export name to lowercase"
+    "ip.conf.EXPORT_REPLACE[\"^.*$\"] = lambda s: s.group().lower()  # rewrite auto export name to lowercase\n",
+    "\n",
+    "def global_custom_func(fig):\n",
+    "    fig.add_text(\n",
+    "        0.9, 0.9,\n",
+    "        \"watermark\",\n",
+    "        color=\"blue\",\n",
+    "        data_coords=False,\n",
+    "        horizontal_alignment=\"right\",\n",
+    "        vertical_alignment=\"top\",\n",
+    "    )\n",
+    "\n",
+    "ip.conf.GLOBAL_CUSTOM_FUNC = global_custom_func"
    ]
   },
   {
@@ -1497,9 +1511,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<interplot.plot.Plot at 0x7fbf09358f40>"
+       "<interplot.plot.Plot at 0x7f7939c5ec10>"
       ]
      },
      "execution_count": 6,
@@ -1975,6 +1989,22 @@
         }
        ],
        "layout": {
+        "annotations": [
+         {
+          "align": "right",
+          "font": {
+           "color": "rgba(0,0,255,1.0000)"
+          },
+          "showarrow": false,
+          "text": "watermark",
+          "x": 0.9,
+          "xanchor": "right",
+          "xref": "x domain",
+          "y": 0.9,
+          "yanchor": "top",
+          "yref": "y domain"
+         }
+        ],
         "barmode": "group",
         "height": 500,
         "legend": {
@@ -2827,11 +2857,11 @@
         }
        }
       },
-      "image/png": "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",
+      "image/png": "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",
       "text/html": [
-       "<div>                            <div id=\"4922e155-e55b-405e-9d74-93ccec05fa7b\" class=\"plotly-graph-div\" style=\"height:500px; width:500px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"4922e155-e55b-405e-9d74-93ccec05fa7b\")) {                    Plotly.newPlot(                        \"4922e155-e55b-405e-9d74-93ccec05fa7b\",                        [{\"line\":{\"dash\":\"solid\"},\"marker\":{\"color\":\"rgba(0,0,0,1.0)\"},\"mode\":\"lines\",\"showlegend\":false,\"x\":[-12.566370614359172,-12.440706908215581,-12.315043202071989,-12.189379495928398,-12.063715789784805,-11.938052083641214,-11.812388377497623,-11.68672467135403,-11.561060965210439,-11.435397259066846,-11.309733552923255,-11.184069846779664,-11.058406140636071,-10.93274243449248,-10.807078728348888,-10.681415022205297,-10.555751316061706,-10.430087609918113,-10.304423903774522,-10.178760197630929,-10.053096491487338,-9.927432785343747,-9.801769079200154,-9.676105373056563,-9.55044166691297,-9.42477796076938,-9.299114254625788,-9.173450548482196,-9.047786842338603,-8.922123136195012,-8.79645943005142,-8.67079572390783,-8.545132017764237,-8.419468311620644,-8.293804605477053,-8.168140899333462,-8.042477193189871,-7.916813487046278,-7.791149780902686,-7.6654860747590945,-7.5398223686155035,-7.414158662471912,-7.28849495632832,-7.162831250184728,-7.037167544041136,-6.911503837897544,-6.785840131753953,-6.660176425610361,-6.534512719466769,-6.408849013323177,-6.283185307179585,-6.157521601035994,-6.031857894892402,-5.9061941887488105,-5.780530482605219,-5.654866776461627,-5.529203070318035,-5.403539364174444,-5.277875658030852,-5.15221195188726,-5.026548245743668,-4.900884539600076,-4.775220833456485,-4.649557127312893,-4.523893421169301,-4.39822971502571,-4.272566008882118,-4.146902302738527,-4.021238596594934,-3.8955748904513428,-3.7699111843077517,-3.644247478164159,-3.518583772020568,-3.392920065876975,-3.267256359733384,-3.141592653589793,-3.0159289474462003,-2.8902652413026093,-2.7646015351590165,-2.6389378290154255,-2.5132741228718345,-2.3876104167282417,-2.2619467105846507,-2.136283004441058,-2.010619298297467,-1.884955592153874,-1.759291886010283,-1.633628179866692,-1.5079644737230993,-1.3823007675795083,-1.2566370614359155,-1.1309733552923245,-1.0053096491487334,-0.8796459430051407,-0.7539822368615496,-0.6283185307179568,-0.5026548245743658,-0.3769911184307748,-0.25132741228718203,-0.12566370614359101,1.7763568394002505e-15,0.1256637061435928,0.2513274122871838,0.3769911184307766,0.5026548245743676,0.6283185307179604,0.7539822368615514,0.8796459430051424,1.0053096491487352,1.1309733552923262,1.256637061435919,1.38230076757951,1.5079644737231028,1.6336281798666938,1.7592918860102849,1.8849555921538776,2.0106192982974687,2.1362830044410615,2.2619467105846525,2.3876104167282435,2.5132741228718363,2.6389378290154273,2.76460153515902,2.890265241302611,3.015928947446202,3.141592653589795,3.267256359733386,3.3929200658769787,3.5185837720205697,3.6442474781641607,3.7699111843077517,3.8955748904513463,4.021238596594937,4.146902302738528,4.272566008882119,4.39822971502571,4.523893421169305,4.649557127312896,4.775220833456487,4.900884539600078,5.026548245743669,5.1522119518872636,5.277875658030855,5.403539364174446,5.529203070318037,5.654866776461628,5.780530482605222,5.906194188748813,6.031857894892404,6.157521601035995,6.283185307179586,6.408849013323181,6.534512719466772,6.660176425610363,6.785840131753954,6.911503837897545,7.037167544041139,7.16283125018473,7.2884949563283215,7.4141586624719125,7.5398223686155035,7.665486074759098,7.791149780902689,7.91681348704628,8.042477193189871,8.168140899333466,8.293804605477057,8.419468311620648,8.545132017764239,8.67079572390783,8.796459430051424,8.922123136195015,9.047786842338606,9.173450548482197,9.299114254625788,9.424777960769383,9.550441666912974,9.676105373056565,9.801769079200156,9.927432785343747,10.053096491487342,10.178760197630933,10.304423903774524,10.430087609918115,10.555751316061706,10.6814150222053,10.807078728348891,10.932742434492482,11.058406140636073,11.184069846779664,11.309733552923259,11.43539725906685,11.56106096521044,11.686724671354032,11.812388377497623,11.938052083641217,12.063715789784808,12.1893794959284,12.31504320207199,12.440706908215581,12.566370614359172],\"y\":[4.898587196589413e-16,0.12533323356430404,0.2486898871648556,0.3681245526846781,0.48175367410171627,0.5877852522924735,0.6845471059286885,0.7705132427757897,0.8443279255020152,0.90482705246602,0.9510565162951538,0.9822872507286886,0.9980267284282716,0.9980267284282716,0.9822872507286885,0.9510565162951534,0.9048270524660197,0.8443279255020146,0.7705132427757891,0.684547105928688,0.5877852522924728,0.4817536741017156,0.36812455268467725,0.24868988716485474,0.12533323356430318,-3.6739403974420594e-16,-0.1253332335643039,-0.2486898871648555,-0.36812455268467964,-0.48175367410171616,-0.5877852522924734,-0.6845471059286884,-0.7705132427757897,-0.844327925502016,-0.90482705246602,-0.9510565162951536,-0.9822872507286886,-0.9980267284282716,-0.9980267284282716,-0.9822872507286885,-0.9510565162951534,-0.9048270524660194,-0.8443279255020147,-0.7705132427757887,-0.684547105928688,-0.5877852522924722,-0.48175367410171493,-0.3681245526846774,-0.24868988716485405,-0.1253332335643033,1.133107779529596e-15,0.12533323356430467,0.24868988716485535,0.36812455268467864,0.4817536741017161,0.587785252292474,0.6845471059286896,0.7705132427757896,0.8443279255020155,0.9048270524660199,0.9510565162951539,0.9822872507286889,0.9980267284282716,0.9980267284282716,0.9822872507286885,0.9510565162951535,0.904827052466019,0.8443279255020147,0.7705132427757881,0.6845471059286881,0.587785252292473,0.4817536741017142,0.36812455268467753,0.2486898871648533,0.12533323356430343,-1.2246467991473532e-16,-0.1253332335643054,-0.24868988716485524,-0.36812455268467936,-0.481753674101716,-0.5877852522924732,-0.6845471059286895,-0.7705132427757896,-0.844327925502016,-0.9048270524660199,-0.9510565162951542,-0.9822872507286889,-0.9980267284282716,-0.9980267284282714,-0.9822872507286885,-0.951056516295153,-0.904827052466019,-0.8443279255020149,-0.7705132427757883,-0.6845471059286882,-0.5877852522924717,-0.4817536741017143,-0.3681245526846776,-0.2486898871648534,-0.12533323356430354,1.7763568394002505e-15,0.12533323356430529,0.24868988716485513,0.36812455268467925,0.4817536741017159,0.5877852522924746,0.6845471059286894,0.7705132427757895,0.8443279255020157,0.9048270524660198,0.9510565162951541,0.9822872507286888,0.9980267284282717,0.9980267284282714,0.9822872507286886,0.9510565162951531,0.9048270524660191,0.844327925502014,0.7705132427757884,0.6845471059286883,0.5877852522924718,0.48175367410171444,0.3681245526846761,0.24868988716485352,0.12533323356430365,-1.6538921594855151e-15,-0.1253332335643052,-0.24868988716485674,-0.36812455268467914,-0.48175367410171577,-0.587785252292473,-0.6845471059286906,-0.7705132427757905,-0.8443279255020157,-0.9048270524660198,-0.9510565162951535,-0.9822872507286892,-0.9980267284282717,-0.9980267284282714,-0.9822872507286886,-0.9510565162951536,-0.9048270524660184,-0.8443279255020141,-0.7705132427757885,-0.6845471059286883,-0.5877852522924732,-0.481753674101713,-0.36812455268467614,-0.24868988716485363,-0.1253332335643038,-2.4492935982947064e-16,0.1253332335643068,0.24868988716485663,0.3681245526846791,0.4817536741017157,0.5877852522924729,0.6845471059286905,0.7705132427757904,0.8443279255020156,0.9048270524660197,0.9510565162951534,0.9822872507286892,0.9980267284282717,0.9980267284282714,0.9822872507286886,0.9510565162951525,0.9048270524660185,0.8443279255020141,0.7705132427757886,0.6845471059286884,0.5877852522924705,0.48175367410171305,0.3681245526846763,0.24868988716485377,0.1253332335643039,-3.185319639056295e-15,-0.1253332335643067,-0.24868988716485646,-0.3681245526846789,-0.4817536741017156,-0.5877852522924757,-0.6845471059286905,-0.7705132427757904,-0.8443279255020156,-0.9048270524660197,-0.9510565162951545,-0.9822872507286892,-0.9980267284282717,-0.9980267284282714,-0.9822872507286886,-0.9510565162951526,-0.9048270524660185,-0.8443279255020142,-0.7705132427757886,-0.6845471059286885,-0.5877852522924706,-0.48175367410171316,-0.3681245526846764,-0.24868988716485388,-0.12533323356430404,-4.898587196589413e-16],\"type\":\"scatter\",\"xaxis\":\"x\",\"yaxis\":\"y\"}],                        {\"legend\":{\"traceorder\":\"normal\",\"title\":{}},\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0]},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0]},\"title\":{},\"height\":500,\"width\":500,\"barmode\":\"group\"},                        {\"responsive\": true}                    ).then(function(){\n",
+       "<div>                            <div id=\"ab5bebdf-2201-44a1-a181-740211c6af02\" class=\"plotly-graph-div\" style=\"height:500px; width:500px;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"ab5bebdf-2201-44a1-a181-740211c6af02\")) {                    Plotly.newPlot(                        \"ab5bebdf-2201-44a1-a181-740211c6af02\",                        [{\"line\":{\"dash\":\"solid\"},\"marker\":{\"color\":\"rgba(0,0,0,1.0000)\"},\"mode\":\"lines\",\"showlegend\":false,\"x\":[-12.566370614359172,-12.440706908215581,-12.315043202071989,-12.189379495928398,-12.063715789784805,-11.938052083641214,-11.812388377497623,-11.68672467135403,-11.561060965210439,-11.435397259066846,-11.309733552923255,-11.184069846779664,-11.058406140636071,-10.93274243449248,-10.807078728348888,-10.681415022205297,-10.555751316061706,-10.430087609918113,-10.304423903774522,-10.178760197630929,-10.053096491487338,-9.927432785343747,-9.801769079200154,-9.676105373056563,-9.55044166691297,-9.42477796076938,-9.299114254625788,-9.173450548482196,-9.047786842338603,-8.922123136195012,-8.79645943005142,-8.67079572390783,-8.545132017764237,-8.419468311620644,-8.293804605477053,-8.168140899333462,-8.042477193189871,-7.916813487046278,-7.791149780902686,-7.6654860747590945,-7.5398223686155035,-7.414158662471912,-7.28849495632832,-7.162831250184728,-7.037167544041136,-6.911503837897544,-6.785840131753953,-6.660176425610361,-6.534512719466769,-6.408849013323177,-6.283185307179585,-6.157521601035994,-6.031857894892402,-5.9061941887488105,-5.780530482605219,-5.654866776461627,-5.529203070318035,-5.403539364174444,-5.277875658030852,-5.15221195188726,-5.026548245743668,-4.900884539600076,-4.775220833456485,-4.649557127312893,-4.523893421169301,-4.39822971502571,-4.272566008882118,-4.146902302738527,-4.021238596594934,-3.8955748904513428,-3.7699111843077517,-3.644247478164159,-3.518583772020568,-3.392920065876975,-3.267256359733384,-3.141592653589793,-3.0159289474462003,-2.8902652413026093,-2.7646015351590165,-2.6389378290154255,-2.5132741228718345,-2.3876104167282417,-2.2619467105846507,-2.136283004441058,-2.010619298297467,-1.884955592153874,-1.759291886010283,-1.633628179866692,-1.5079644737230993,-1.3823007675795083,-1.2566370614359155,-1.1309733552923245,-1.0053096491487334,-0.8796459430051407,-0.7539822368615496,-0.6283185307179568,-0.5026548245743658,-0.3769911184307748,-0.25132741228718203,-0.12566370614359101,1.7763568394002505e-15,0.1256637061435928,0.2513274122871838,0.3769911184307766,0.5026548245743676,0.6283185307179604,0.7539822368615514,0.8796459430051424,1.0053096491487352,1.1309733552923262,1.256637061435919,1.38230076757951,1.5079644737231028,1.6336281798666938,1.7592918860102849,1.8849555921538776,2.0106192982974687,2.1362830044410615,2.2619467105846525,2.3876104167282435,2.5132741228718363,2.6389378290154273,2.76460153515902,2.890265241302611,3.015928947446202,3.141592653589795,3.267256359733386,3.3929200658769787,3.5185837720205697,3.6442474781641607,3.7699111843077517,3.8955748904513463,4.021238596594937,4.146902302738528,4.272566008882119,4.39822971502571,4.523893421169305,4.649557127312896,4.775220833456487,4.900884539600078,5.026548245743669,5.1522119518872636,5.277875658030855,5.403539364174446,5.529203070318037,5.654866776461628,5.780530482605222,5.906194188748813,6.031857894892404,6.157521601035995,6.283185307179586,6.408849013323181,6.534512719466772,6.660176425610363,6.785840131753954,6.911503837897545,7.037167544041139,7.16283125018473,7.2884949563283215,7.4141586624719125,7.5398223686155035,7.665486074759098,7.791149780902689,7.91681348704628,8.042477193189871,8.168140899333466,8.293804605477057,8.419468311620648,8.545132017764239,8.67079572390783,8.796459430051424,8.922123136195015,9.047786842338606,9.173450548482197,9.299114254625788,9.424777960769383,9.550441666912974,9.676105373056565,9.801769079200156,9.927432785343747,10.053096491487342,10.178760197630933,10.304423903774524,10.430087609918115,10.555751316061706,10.6814150222053,10.807078728348891,10.932742434492482,11.058406140636073,11.184069846779664,11.309733552923259,11.43539725906685,11.56106096521044,11.686724671354032,11.812388377497623,11.938052083641217,12.063715789784808,12.1893794959284,12.31504320207199,12.440706908215581,12.566370614359172],\"y\":[4.898587196589413e-16,0.12533323356430404,0.2486898871648556,0.3681245526846781,0.48175367410171627,0.5877852522924735,0.6845471059286885,0.7705132427757897,0.8443279255020152,0.90482705246602,0.9510565162951538,0.9822872507286886,0.9980267284282716,0.9980267284282716,0.9822872507286885,0.9510565162951534,0.9048270524660197,0.8443279255020146,0.7705132427757891,0.684547105928688,0.5877852522924728,0.4817536741017156,0.36812455268467725,0.24868988716485474,0.12533323356430318,-3.6739403974420594e-16,-0.1253332335643039,-0.2486898871648555,-0.36812455268467964,-0.48175367410171616,-0.5877852522924734,-0.6845471059286884,-0.7705132427757897,-0.844327925502016,-0.90482705246602,-0.9510565162951536,-0.9822872507286886,-0.9980267284282716,-0.9980267284282716,-0.9822872507286885,-0.9510565162951534,-0.9048270524660194,-0.8443279255020147,-0.7705132427757887,-0.684547105928688,-0.5877852522924722,-0.48175367410171493,-0.3681245526846774,-0.24868988716485405,-0.1253332335643033,1.133107779529596e-15,0.12533323356430467,0.24868988716485535,0.36812455268467864,0.4817536741017161,0.587785252292474,0.6845471059286896,0.7705132427757896,0.8443279255020155,0.9048270524660199,0.9510565162951539,0.9822872507286889,0.9980267284282716,0.9980267284282716,0.9822872507286885,0.9510565162951535,0.904827052466019,0.8443279255020147,0.7705132427757881,0.6845471059286881,0.587785252292473,0.4817536741017142,0.36812455268467753,0.2486898871648533,0.12533323356430343,-1.2246467991473532e-16,-0.1253332335643054,-0.24868988716485524,-0.36812455268467936,-0.481753674101716,-0.5877852522924732,-0.6845471059286895,-0.7705132427757896,-0.844327925502016,-0.9048270524660199,-0.9510565162951542,-0.9822872507286889,-0.9980267284282716,-0.9980267284282714,-0.9822872507286885,-0.951056516295153,-0.904827052466019,-0.8443279255020149,-0.7705132427757883,-0.6845471059286882,-0.5877852522924717,-0.4817536741017143,-0.3681245526846776,-0.2486898871648534,-0.12533323356430354,1.7763568394002505e-15,0.12533323356430529,0.24868988716485513,0.36812455268467925,0.4817536741017159,0.5877852522924746,0.6845471059286894,0.7705132427757895,0.8443279255020157,0.9048270524660198,0.9510565162951541,0.9822872507286888,0.9980267284282717,0.9980267284282714,0.9822872507286886,0.9510565162951531,0.9048270524660191,0.844327925502014,0.7705132427757884,0.6845471059286883,0.5877852522924718,0.48175367410171444,0.3681245526846761,0.24868988716485352,0.12533323356430365,-1.6538921594855151e-15,-0.1253332335643052,-0.24868988716485674,-0.36812455268467914,-0.48175367410171577,-0.587785252292473,-0.6845471059286906,-0.7705132427757905,-0.8443279255020157,-0.9048270524660198,-0.9510565162951535,-0.9822872507286892,-0.9980267284282717,-0.9980267284282714,-0.9822872507286886,-0.9510565162951536,-0.9048270524660184,-0.8443279255020141,-0.7705132427757885,-0.6845471059286883,-0.5877852522924732,-0.481753674101713,-0.36812455268467614,-0.24868988716485363,-0.1253332335643038,-2.4492935982947064e-16,0.1253332335643068,0.24868988716485663,0.3681245526846791,0.4817536741017157,0.5877852522924729,0.6845471059286905,0.7705132427757904,0.8443279255020156,0.9048270524660197,0.9510565162951534,0.9822872507286892,0.9980267284282717,0.9980267284282714,0.9822872507286886,0.9510565162951525,0.9048270524660185,0.8443279255020141,0.7705132427757886,0.6845471059286884,0.5877852522924705,0.48175367410171305,0.3681245526846763,0.24868988716485377,0.1253332335643039,-3.185319639056295e-15,-0.1253332335643067,-0.24868988716485646,-0.3681245526846789,-0.4817536741017156,-0.5877852522924757,-0.6845471059286905,-0.7705132427757904,-0.8443279255020156,-0.9048270524660197,-0.9510565162951545,-0.9822872507286892,-0.9980267284282717,-0.9980267284282714,-0.9822872507286886,-0.9510565162951526,-0.9048270524660185,-0.8443279255020142,-0.7705132427757886,-0.6845471059286885,-0.5877852522924706,-0.48175367410171316,-0.3681245526846764,-0.24868988716485388,-0.12533323356430404,-4.898587196589413e-16],\"type\":\"scatter\",\"xaxis\":\"x\",\"yaxis\":\"y\"}],                        {\"legend\":{\"traceorder\":\"normal\",\"title\":{}},\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0]},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0]},\"title\":{},\"height\":500,\"width\":500,\"barmode\":\"group\",\"annotations\":[{\"align\":\"right\",\"font\":{\"color\":\"rgba(0,0,255,1.0000)\"},\"showarrow\":false,\"text\":\"watermark\",\"x\":0.9,\"xanchor\":\"right\",\"xref\":\"x domain\",\"y\":0.9,\"yanchor\":\"top\",\"yref\":\"y domain\"}]},                        {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
-       "var gd = document.getElementById('4922e155-e55b-405e-9d74-93ccec05fa7b');\n",
+       "var gd = document.getElementById('ab5bebdf-2201-44a1-a181-740211c6af02');\n",
        "var x = new MutationObserver(function (mutations, observer) {{\n",
        "        var display = window.getComputedStyle(gd).display;\n",
        "        if (!display || display === 'none') {{\n",
@@ -2856,7 +2886,7 @@
        "                        })                };                });            </script>        </div>"
       ],
       "text/plain": [
-       "<interplot.plot.Plot at 0x7fbf0104f2b0>"
+       "<interplot.plot.Plot at 0x7f7939ec15b0>"
       ]
      },
      "execution_count": 7,
@@ -2883,9 +2913,9 @@
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<interplot.plot.Plot at 0x7fbf098f9490>"
+       "<interplot.plot.Plot at 0x7f792fd41340>"
       ]
      },
      "execution_count": 8,
@@ -2937,14 +2967,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "saved figure at exports/exported_20240403T000356Z.pdf\n"
+      "saved figure at exports/exported_20240704T101523Z.pdf\n"
      ]
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
-       "<interplot.plot.Plot at 0x7fbf098f9190>"
+       "<interplot.plot.Plot at 0x7f793a92dd00>"
       ]
      },
      "execution_count": 11,
@@ -2971,14 +3001,14 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "saved figure at exports/manual-name-not-affected.png\n"
+      "saved figure at exports/manual-arguments-not-affected.png\n"
      ]
     },
     {
      "data": {
       "image/png": "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",
       "text/plain": [
-       "<interplot.plot.Plot at 0x7fbf0960aca0>"
+       "<interplot.plot.Plot at 0x7f793a9b0d30>"
       ]
      },
      "execution_count": 12,
@@ -2987,11 +3017,12 @@
     }
    ],
    "source": [
-    "# file names and formats provided manually are not affected\n",
+    "# arguments provided manually are not affected\n",
     "ip.line(\n",
     "    x,\n",
     "    y,\n",
-    "    save_fig=\"exports/manual-name-not-affected.png\",\n",
+    "    save_fig=\"exports/manual-arguments-not-affected.png\",\n",
+    "    global_custom_func=lambda _: None,\n",
     ")"
    ]
   }
diff --git a/demo/exports/exported_20240704T100518Z.pdf b/demo/exports/exported_20240704T100518Z.pdf
new file mode 100644
index 0000000..d1308c2
Binary files /dev/null and b/demo/exports/exported_20240704T100518Z.pdf differ
diff --git a/demo/exports/exported_20240704T100736Z.pdf b/demo/exports/exported_20240704T100736Z.pdf
new file mode 100644
index 0000000..069afc6
Binary files /dev/null and b/demo/exports/exported_20240704T100736Z.pdf differ
diff --git a/demo/exports/exported_20240704T100822Z.pdf b/demo/exports/exported_20240704T100822Z.pdf
new file mode 100644
index 0000000..af6f1d5
Binary files /dev/null and b/demo/exports/exported_20240704T100822Z.pdf differ
diff --git a/demo/exports/exported_20240704T101122Z.pdf b/demo/exports/exported_20240704T101122Z.pdf
new file mode 100644
index 0000000..fdeeaef
Binary files /dev/null and b/demo/exports/exported_20240704T101122Z.pdf differ
diff --git a/demo/exports/exported_20240704T101523Z.pdf b/demo/exports/exported_20240704T101523Z.pdf
new file mode 100644
index 0000000..fea34cb
Binary files /dev/null and b/demo/exports/exported_20240704T101523Z.pdf differ
diff --git a/demo/exports/lowercase_export.pdf b/demo/exports/lowercase_export.pdf
index 7506c4f..054012f 100644
Binary files a/demo/exports/lowercase_export.pdf and b/demo/exports/lowercase_export.pdf differ
diff --git a/demo/exports/manual-name-not-affected.png b/demo/exports/manual-arguments-not-affected.png
similarity index 100%
rename from demo/exports/manual-name-not-affected.png
rename to demo/exports/manual-arguments-not-affected.png
diff --git a/docs/_sources/api_conf.rst.txt b/docs/_sources/api_conf.rst.txt
index fa8df78..231d09f 100644
--- a/docs/_sources/api_conf.rst.txt
+++ b/docs/_sources/api_conf.rst.txt
@@ -27,6 +27,12 @@
 
 .. autovariable:: interplot.conf.EXPORT_REPLACE
 
+.. autofunction:: interplot.conf.GLOBAL_CUSTOM_FUNC
+
+.. autofunction:: interplot.conf.MPL_CUSTOM_FUNC
+
+.. autofunction:: interplot.conf.PTY_CUSTOM_FUNC
+
 .. autovariable:: interplot.conf.PTY_CONFIG
 
 .. autovariable:: interplot.conf.PTY_LINE_STYLES
diff --git a/docs/_sources/api_plot.rst.txt b/docs/_sources/api_plot.rst.txt
index 98d255f..739dbc8 100644
--- a/docs/_sources/api_plot.rst.txt
+++ b/docs/_sources/api_plot.rst.txt
@@ -90,3 +90,9 @@ One-line Plotting
     :private-members: _repr_html_, _repr_mimebundle_
     :member-order: bysource
 
+
+----------------
+Helper functions
+----------------
+
+.. autofunction:: interplot.pick_non_none
diff --git a/docs/api_conf.html b/docs/api_conf.html
index dd4f38d..679c4ab 100644
--- a/docs/api_conf.html
+++ b/docs/api_conf.html
@@ -141,6 +141,127 @@ <h2><a class="toc-backref" href="#id2" role="doc-backlink"><cite>VARIABLES</cite
 and the (regex) replacement as values.</p>
 </dd></dl>
 
+<dl class="py function">
+<dt class="sig sig-object py" id="interplot.conf.GLOBAL_CUSTOM_FUNC">
+<span class="sig-prename descclassname"><span class="pre">interplot.conf.</span></span><span class="sig-name descname"><span class="pre">GLOBAL_CUSTOM_FUNC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fig</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.conf.GLOBAL_CUSTOM_FUNC" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply a custom function on figures on finishing the plot.</p>
+<p>Will be called in <cite>interplot.Plot.post_process()</cite>.</p>
+<p>When overriding this function, make sure it accepts <cite>fig</cite>.
+There is no need to return <cite>fig</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>fig: interplot.Plot instance</strong></dt><dd></dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">global_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;watermark&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">conf</span><span class="o">.</span><span class="n">GLOBAL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">global_custom_func</span><span class="o">=</span><span class="n">global_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># global_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="interplot.conf.MPL_CUSTOM_FUNC">
+<span class="sig-prename descclassname"><span class="pre">interplot.conf.</span></span><span class="sig-name descname"><span class="pre">MPL_CUSTOM_FUNC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fig</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ax</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.conf.MPL_CUSTOM_FUNC" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply a custom function on matplotlib figures on finishing the plot.</p>
+<p>Will be called in <cite>interplot.Plot.post_process()</cite>.</p>
+<p>When overriding this function, make sure it accepts <cite>fig</cite> and <cite>ax</cite>
+and returns <cite>fig</cite> and <cite>ax</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>fig</strong><span class="classifier">matplotlib.figure.Figure</span></dt><dd><p>The figure object.</p>
+</dd>
+<dt><strong>ax</strong><span class="classifier">matplotlib.axes.Axes</span></dt><dd><p>The axes object.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>fig</strong><span class="classifier">matplotlib.figure.Figure</span></dt><dd><p>The modified figure object.</p>
+</dd>
+<dt><strong>ax</strong><span class="classifier">matplotlib.axes.Axes</span></dt><dd><p>The modified axes object.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mpl_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
+<span class="gp">... </span>    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">do_more</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">MPL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">mpl_custom_func</span><span class="o">=</span><span class="n">mpl_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># mpl_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="interplot.conf.PTY_CUSTOM_FUNC">
+<span class="sig-prename descclassname"><span class="pre">interplot.conf.</span></span><span class="sig-name descname"><span class="pre">PTY_CUSTOM_FUNC</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fig</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.conf.PTY_CUSTOM_FUNC" title="Permalink to this definition">¶</a></dt>
+<dd><p>Apply a custom function on plotly figures on finishing the plot.</p>
+<p>Will be called in <cite>interplot.Plot.post_process()</cite>.</p>
+<p>When overriding this function, make sure it accepts <cite>fig</cite>
+and returns <cite>fig</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>fig</strong><span class="classifier">plotly.graph_objects.Figure</span></dt><dd><p>The figure object.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt><strong>fig</strong><span class="classifier">plotly.graph_objects.Figure</span></dt><dd><p>The modified figure object.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<p class="rubric">Examples</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">PTY_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">pty_custom_func</span><span class="o">=</span><span class="n">pty_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># pty_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</dd></dl>
+
 <dl class="py data">
 <dt class="sig sig-object py" id="interplot.conf.PTY_CONFIG">
 <span class="sig-prename descclassname"><span class="pre">interplot.conf.</span></span><span class="sig-name descname"><span class="pre">PTY_CONFIG</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">{'displayModeBar':</span> <span class="pre">True,</span> <span class="pre">'displaylogo':</span> <span class="pre">False}</span></em><a class="headerlink" href="#interplot.conf.PTY_CONFIG" title="Permalink to this definition">¶</a></dt>
@@ -217,6 +338,9 @@ <h3><a href="index.html">Table of Contents</a></h3>
 <li><a class="reference internal" href="#interplot.conf.MPL_FIG_SIZE"><code class="docutils literal notranslate"><span class="pre">MPL_FIG_SIZE</span></code></a></li>
 <li><a class="reference internal" href="#interplot.conf.EXPORT_FORMAT"><code class="docutils literal notranslate"><span class="pre">EXPORT_FORMAT</span></code></a></li>
 <li><a class="reference internal" href="#interplot.conf.EXPORT_REPLACE"><code class="docutils literal notranslate"><span class="pre">EXPORT_REPLACE</span></code></a></li>
+<li><a class="reference internal" href="#interplot.conf.GLOBAL_CUSTOM_FUNC"><code class="docutils literal notranslate"><span class="pre">GLOBAL_CUSTOM_FUNC()</span></code></a></li>
+<li><a class="reference internal" href="#interplot.conf.MPL_CUSTOM_FUNC"><code class="docutils literal notranslate"><span class="pre">MPL_CUSTOM_FUNC()</span></code></a></li>
+<li><a class="reference internal" href="#interplot.conf.PTY_CUSTOM_FUNC"><code class="docutils literal notranslate"><span class="pre">PTY_CUSTOM_FUNC()</span></code></a></li>
 <li><a class="reference internal" href="#interplot.conf.PTY_CONFIG"><code class="docutils literal notranslate"><span class="pre">PTY_CONFIG</span></code></a></li>
 <li><a class="reference internal" href="#interplot.conf.PTY_LINE_STYLES"><code class="docutils literal notranslate"><span class="pre">PTY_LINE_STYLES</span></code></a></li>
 <li><a class="reference internal" href="#interplot.conf.MPL_LINE_STYLES"><code class="docutils literal notranslate"><span class="pre">MPL_LINE_STYLES</span></code></a></li>
diff --git a/docs/api_plot.html b/docs/api_plot.html
index 2aca212..449ac9b 100644
--- a/docs/api_plot.html
+++ b/docs/api_plot.html
@@ -44,17 +44,18 @@ <h3>Navigation</h3>
           <div class="body" role="main">
             
   <section id="interplot-documentation-and-api-reference">
-<h1><a class="toc-backref" href="#id3" role="doc-backlink"><cite>interplot</cite> Documentation and API reference</a><a class="headerlink" href="#interplot-documentation-and-api-reference" title="Permalink to this heading">¶</a></h1>
+<h1><a class="toc-backref" href="#id1" role="doc-backlink"><cite>interplot</cite> Documentation and API reference</a><a class="headerlink" href="#interplot-documentation-and-api-reference" title="Permalink to this heading">¶</a></h1>
 <nav class="contents" id="on-this-page">
 <p class="topic-title">On this page</p>
 <ul class="simple">
-<li><p><a class="reference internal" href="#interplot-documentation-and-api-reference" id="id3"><cite>interplot</cite> Documentation and API reference</a></p>
+<li><p><a class="reference internal" href="#interplot-documentation-and-api-reference" id="id1"><cite>interplot</cite> Documentation and API reference</a></p>
 <ul>
-<li><p><a class="reference internal" href="#the-core-plot-class" id="id4">The core <cite>Plot</cite> class</a></p></li>
-<li><p><a class="reference internal" href="#one-line-plotting" id="id5">One-line Plotting</a></p></li>
-<li><p><a class="reference internal" href="#magic-plot-decorators" id="id6"><cite>magic_plot</cite> decorators</a></p></li>
-<li><p><a class="reference internal" href="#notebookinteraction-parent-class" id="id7"><cite>NotebookInteraction</cite> Parent Class</a></p></li>
-<li><p><a class="reference internal" href="#showdataarray-showdataset" id="id8"><cite>ShowDataArray</cite>, <cite>ShowDataset</cite></a></p></li>
+<li><p><a class="reference internal" href="#the-core-plot-class" id="id2">The core <cite>Plot</cite> class</a></p></li>
+<li><p><a class="reference internal" href="#one-line-plotting" id="id3">One-line Plotting</a></p></li>
+<li><p><a class="reference internal" href="#magic-plot-decorators" id="id4"><cite>magic_plot</cite> decorators</a></p></li>
+<li><p><a class="reference internal" href="#notebookinteraction-parent-class" id="id5"><cite>NotebookInteraction</cite> Parent Class</a></p></li>
+<li><p><a class="reference internal" href="#showdataarray-showdataset" id="id6"><cite>ShowDataArray</cite>, <cite>ShowDataset</cite></a></p></li>
+<li><p><a class="reference internal" href="#helper-functions" id="id7">Helper functions</a></p></li>
 </ul>
 </li>
 </ul>
@@ -76,10 +77,10 @@ <h1><a class="toc-backref" href="#id3" role="doc-backlink"><cite>interplot</cite
 <li><p>color cycling</p></li>
 </ul>
 <section id="the-core-plot-class">
-<h2><a class="toc-backref" href="#id4" role="doc-backlink">The core <cite>Plot</cite> class</a><a class="headerlink" href="#the-core-plot-class" title="Permalink to this heading">¶</a></h2>
+<h2><a class="toc-backref" href="#id2" role="doc-backlink">The core <cite>Plot</cite> class</a><a class="headerlink" href="#the-core-plot-class" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="interplot.Plot">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">Plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.Plot" title="Permalink to this definition">¶</a></dt>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">Plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_cycle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.Plot" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="#interplot.NotebookInteraction" title="interplot.plot.NotebookInteraction"><code class="xref py py-class docutils literal notranslate"><span class="pre">NotebookInteraction</span></code></a></p>
 <p>Create <cite>matplotlib/plotly</cite> hybrid plots with a few lines of code.</p>
 <p>It combines the best of the <cite>matplotlib</cite> and the <cite>plotly</cite> worlds through
@@ -103,7 +104,7 @@ <h2><a class="toc-backref" href="#id4" role="doc-backlink">The core <cite>Plot</
 <dt><strong>interactive: bool, default: True</strong></dt><dd><p>Display an interactive plotly line plot
 instead of the default matplotlib figure.</p>
 </dd>
-<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with x rows and y columns.</p>
+<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with <cite>n</cite> rows and <cite>m</cite> columns.</p>
 </dd>
 <dt><strong>title: str, default: None</strong></dt><dd><p>Plot title.</p>
 </dd>
@@ -115,7 +116,7 @@ <h2><a class="toc-backref" href="#id4" role="doc-backlink">The core <cite>Plot</
 <dt>In case of multiple rows/cols provide either:</dt><dd><ul class="simple">
 <li><p>a tuple</p></li>
 <li><p>a tuple for each row</p></li>
-<li><p>a tuple for each row containing tuple for each column.</p></li>
+<li><p>a tuple for each row containing a tuple for each column.</p></li>
 </ul>
 </dd>
 </dl>
@@ -134,14 +135,10 @@ <h2><a class="toc-backref" href="#id4" role="doc-backlink">The core <cite>Plot</
 <dt><strong>column_widths, row_heights: tuple/list, default: None</strong></dt><dd><p>Ratios of the width/height dimensions in each column/row.
 Will be normalised to a sum of 1.</p>
 </dd>
-<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Figure size in pixels.</p>
-<dl class="simple">
-<dt>Default behavior:</dt><dd><ul class="simple">
-<li><p>MPL: Default figure size.</p></li>
-<li><p>PLT: Responsive sizing.</p></li>
-</ul>
-</dd>
-</dl>
+<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Width, height in pixels.</p>
+<p>Default behavior defined by <cite>conf.MPL_FIG_SIZE</cite>
+and <cite>conf.PTY_FIG_SIZE</cite>.
+Plotly allows None for responsive size adapting to viewport.</p>
 </dd>
 <dt><strong>dpi: int, default: 100</strong></dt><dd><p>Plot resolution.</p>
 </dd>
@@ -158,6 +155,10 @@ <h2><a class="toc-backref" href="#id4" role="doc-backlink">The core <cite>Plot</
 the subplots may be provided.</p>
 <p>PTY: Just provide a <cite>str</cite>.</p>
 </dd>
+<dt><strong>color_cycle: list, optional</strong></dt><dd><p>Cycle through colors in the provided list.</p>
+<p>If left <cite>None</cite>, the default color cycle
+<cite>conf.COLOR_CYCLE</cite> will be used.</p>
+</dd>
 <dt><strong>save_fig: str or pathlib.Path, default: None</strong></dt><dd><p>Provide a path to export the plot.</p>
 <p>Possible formats: png, jpg, svg, html, …</p>
 <p>The figure will only be saved on calling the instance’s
@@ -171,32 +172,79 @@ <h2><a class="toc-backref" href="#id4" role="doc-backlink">The core <cite>Plot</
 <p>An iterable of multiple formats may be provided. In this case
 the save command will be repeated for each element.</p>
 </dd>
-<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass keyword arguments to plotly’s
-<cite>fig.update_layout(**pty_update_layout)</cite>
-Thus, take full control over</p>
+<dt><strong>save_config: dict, default: None</strong></dt><dd><p>Plotly only.</p>
+<p>Pass config options to
+<cite>plotly.graph_objects.Figure.write_html(config=save_config)</cite>.</p>
 </dd>
-<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass a function reference to further style the plotly graphs.
-Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
-<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
-<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<dt><strong>global_custom_func: function, default: None</strong></dt><dd><p>Pass a function reference to further style the figures.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">global_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;watermark&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">conf</span><span class="o">.</span><span class="n">GLOBAL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">global_custom_func</span><span class="o">=</span><span class="n">global_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># global_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
-<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.
-Pass a function reference to further style the matplotlib graphs.
+<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.</p>
+<p>Pass a function reference to further style the matplotlib graphs.
 Function must accept <cite>fig, ax</cite> and return <cite>fig, ax</cite>.</p>
-<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the plot is
-just 1x1.</p>
+<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the
+plot is just 1x1.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mpl_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
 <span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
 <span class="gp">... </span>    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">do_more</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">MPL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+<span class="gp">...</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">mpl_custom_func</span><span class="o">=</span><span class="n">mpl_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># mpl_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
+<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass a function reference to further style the plotly graphs.
+Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">PTY_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">pty_custom_func</span><span class="o">=</span><span class="n">pty_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># pty_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</dd>
+<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass keyword arguments to plotly’s
+<cite>plotly.graph_objects.Figure.update_layout(**pty_update_layout)</cite></p>
+<p>Default: None</p>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -288,25 +336,28 @@ <h2><a class="toc-backref" href="#id4" role="doc-backlink">The core <cite>Plot</
 <tr class="row-odd"><td><p><a class="reference internal" href="#interplot.Plot.add_text" title="interplot.Plot.add_text"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_text</span></code></a>(x, y, text[, horizontal_alignment, ...])</p></td>
 <td><p>Draw text.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#interplot.Plot.digest_color" title="interplot.Plot.digest_color"><code class="xref py py-obj docutils literal notranslate"><span class="pre">digest_color</span></code></a>([color, alpha, increment])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#interplot.Plot.close" title="interplot.Plot.close"><code class="xref py py-obj docutils literal notranslate"><span class="pre">close</span></code></a>()</p></td>
+<td><p>Close the plot.</p></td>
+</tr>
+<tr class="row-odd"><td><p><a class="reference internal" href="#interplot.Plot.digest_color" title="interplot.Plot.digest_color"><code class="xref py py-obj docutils literal notranslate"><span class="pre">digest_color</span></code></a>([color, alpha, increment])</p></td>
 <td><p>Parse color with matplotlib.colors to a rgba array.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#interplot.Plot.digest_marker" title="interplot.Plot.digest_marker"><code class="xref py py-obj docutils literal notranslate"><span class="pre">digest_marker</span></code></a>(marker, mode, interactive[, ...])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#interplot.Plot.digest_marker" title="interplot.Plot.digest_marker"><code class="xref py py-obj docutils literal notranslate"><span class="pre">digest_marker</span></code></a>(marker, mode, interactive[, ...])</p></td>
 <td><p>Digests the marker parameter based on the given mode.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#interplot.Plot.get_cycle_color" title="interplot.Plot.get_cycle_color"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_cycle_color</span></code></a>([increment, i])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#interplot.Plot.get_cycle_color" title="interplot.Plot.get_cycle_color"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_cycle_color</span></code></a>([increment, i])</p></td>
 <td><p>Retrieves the next color in the color cycle.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#interplot.Plot.init" title="interplot.Plot.init"><code class="xref py py-obj docutils literal notranslate"><span class="pre">init</span></code></a>(fig, *args, **kwargs)</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#interplot.Plot.init" title="interplot.Plot.init"><code class="xref py py-obj docutils literal notranslate"><span class="pre">init</span></code></a>(fig, *args, **kwargs)</p></td>
 <td><p>Initialize a Plot instance, if not already initialized.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#interplot.Plot.post_process" title="interplot.Plot.post_process"><code class="xref py py-obj docutils literal notranslate"><span class="pre">post_process</span></code></a>([pty_update_layout, ...])</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#interplot.Plot.post_process" title="interplot.Plot.post_process"><code class="xref py py-obj docutils literal notranslate"><span class="pre">post_process</span></code></a>([global_custom_func, ...])</p></td>
 <td><p>Finish the plot.</p></td>
 </tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#interplot.Plot.save" title="interplot.Plot.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(path[, export_format, print_confirm])</p></td>
+<tr class="row-even"><td><p><a class="reference internal" href="#interplot.Plot.save" title="interplot.Plot.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a>(path[, export_format, print_confirm])</p></td>
 <td><p>Save the plot.</p></td>
 </tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#interplot.Plot.show" title="interplot.Plot.show"><code class="xref py py-obj docutils literal notranslate"><span class="pre">show</span></code></a>()</p></td>
+<tr class="row-odd"><td><p><a class="reference internal" href="#interplot.Plot.show" title="interplot.Plot.show"><code class="xref py py-obj docutils literal notranslate"><span class="pre">show</span></code></a>()</p></td>
 <td><p>Show the plot.</p></td>
 </tr>
 </tbody>
@@ -1072,41 +1123,68 @@ <h2><a class="toc-backref" href="#id4" role="doc-backlink">The core <cite>Plot</
 
 <dl class="py method">
 <dt class="sig sig-object py" id="interplot.Plot.post_process">
-<span class="sig-name descname"><span class="pre">post_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.Plot.post_process" title="Permalink to this definition">¶</a></dt>
+<span class="sig-name descname"><span class="pre">post_process</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.Plot.post_process" title="Permalink to this definition">¶</a></dt>
 <dd><p>Finish the plot.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><dl>
-<dt><strong>Note: If not provided, the parameters given on init will be used.</strong></dt><dd></dd>
-<dt><strong>pty_update_layout: dict, optional</strong></dt><dd><p>PLOTLY ONLY.
-Pass keyword arguments to plotly’s
-fig.update_layout(<a href="#id1"><span class="problematic" id="id2">**</span></a>pty_update_layout)
-Thus, take full control over
-Default: None</p>
-</dd>
-<dt><strong>pty_custom_func: function, optional</strong></dt><dd><p>PLOTLY ONLY.
-Pass a function reference to further style the plotly graphs.
-Function must accept fig and return fig.</p>
-<p>Default: None</p>
-<p>Example:</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<dt><strong>Note: If not provided, the parameters given on init or</strong></dt><dd></dd>
+<dt><strong>the `interplot.conf` default values will be used.</strong></dt><dd></dd>
+<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.</p>
+<p>Pass a function reference to further style the matplotlib graphs.
+Function must accept <cite>fig, ax</cite> and return <cite>fig, ax</cite>.</p>
+<p>If providing your own function reference, <cite>conf.MPL_CUSTOM_FUNC</cite>
+will not be executed.</p>
+<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the
+plot is just 1x1.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mpl_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
 <span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
-<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<span class="gp">... </span>    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">do_more</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">MPL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">mpl_custom_func</span><span class="o">=</span><span class="n">mpl_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># mpl_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
-<dt><strong>mpl_custom_func: function, optional</strong></dt><dd><p>MATPLOTLIB ONLY.
-Pass a function reference to further style the matplotlib graphs.
-Function must accept fig, ax and return fig, ax.</p>
-<p>Default: None</p>
-<p>Example:</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mpl_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass a function reference to further style the plotly graphs.
+Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
+<p>If providing your own function reference, <cite>conf.PTY_CUSTOM_FUNC</cite>
+will not be executed.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
 <span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
-<span class="gp">... </span>    <span class="n">ax</span><span class="o">.</span><span class="n">do_more</span><span class="p">()</span>
-<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">PTY_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">pty_custom_func</span><span class="o">=</span><span class="n">pty_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># pty_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
+<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass keyword arguments to plotly’s
+<cite>plotly.graph_objects.Figure.update_layout(**pty_update_layout)</cite></p>
+</dd>
 </dl>
 </dd>
 </dl>
@@ -1149,6 +1227,12 @@ <h2><a class="toc-backref" href="#id4" role="doc-backlink">The core <cite>Plot</
 <dd><p>Show the plot.</p>
 </dd></dl>
 
+<dl class="py method">
+<dt class="sig sig-object py" id="interplot.Plot.close">
+<span class="sig-name descname"><span class="pre">close</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#interplot.Plot.close" title="Permalink to this definition">¶</a></dt>
+<dd><p>Close the plot.</p>
+</dd></dl>
+
 <dl class="py method">
 <dt class="sig sig-object py" id="interplot.Plot._repr_mimebundle_">
 <span class="sig-name descname"><span class="pre">_repr_mimebundle_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.Plot._repr_mimebundle_" title="Permalink to this definition">¶</a></dt>
@@ -1171,10 +1255,10 @@ <h2><a class="toc-backref" href="#id4" role="doc-backlink">The core <cite>Plot</
 
 </section>
 <section id="one-line-plotting">
-<h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a><a class="headerlink" href="#one-line-plotting" title="Permalink to this heading">¶</a></h2>
+<h2><a class="toc-backref" href="#id3" role="doc-backlink">One-line Plotting</a><a class="headerlink" href="#one-line-plotting" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="interplot.line">
-<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">line</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.line" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">line</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_cycle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.line" title="Permalink to this definition">¶</a></dt>
 <dd><p>Draw a line or scatter plot.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1256,7 +1340,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>interactive: bool, default: True</strong></dt><dd><p>Display an interactive plotly line plot
 instead of the default matplotlib figure.</p>
 </dd>
-<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with x rows and y columns.</p>
+<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with <cite>n</cite> rows and <cite>m</cite> columns.</p>
 </dd>
 <dt><strong>title: str, default: None</strong></dt><dd><p>Plot title.</p>
 </dd>
@@ -1268,7 +1352,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt>In case of multiple rows/cols provide either:</dt><dd><ul class="simple">
 <li><p>a tuple</p></li>
 <li><p>a tuple for each row</p></li>
-<li><p>a tuple for each row containing tuple for each column.</p></li>
+<li><p>a tuple for each row containing a tuple for each column.</p></li>
 </ul>
 </dd>
 </dl>
@@ -1287,14 +1371,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>column_widths, row_heights: tuple/list, default: None</strong></dt><dd><p>Ratios of the width/height dimensions in each column/row.
 Will be normalised to a sum of 1.</p>
 </dd>
-<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Figure size in pixels.</p>
-<dl class="simple">
-<dt>Default behavior:</dt><dd><ul class="simple">
-<li><p>MPL: Default figure size.</p></li>
-<li><p>PLT: Responsive sizing.</p></li>
-</ul>
-</dd>
-</dl>
+<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Width, height in pixels.</p>
+<p>Default behavior defined by <cite>conf.MPL_FIG_SIZE</cite>
+and <cite>conf.PTY_FIG_SIZE</cite>.
+Plotly allows None for responsive size adapting to viewport.</p>
 </dd>
 <dt><strong>dpi: int, default: 100</strong></dt><dd><p>Plot resolution.</p>
 </dd>
@@ -1311,6 +1391,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 the subplots may be provided.</p>
 <p>PTY: Just provide a <cite>str</cite>.</p>
 </dd>
+<dt><strong>color_cycle: list, optional</strong></dt><dd><p>Cycle through colors in the provided list.</p>
+<p>If left <cite>None</cite>, the default color cycle
+<cite>conf.COLOR_CYCLE</cite> will be used.</p>
+</dd>
 <dt><strong>save_fig: str or pathlib.Path, default: None</strong></dt><dd><p>Provide a path to export the plot.</p>
 <p>Possible formats: png, jpg, svg, html, …</p>
 <p>The figure will only be saved on calling the instance’s
@@ -1324,32 +1408,79 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <p>An iterable of multiple formats may be provided. In this case
 the save command will be repeated for each element.</p>
 </dd>
-<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass keyword arguments to plotly’s
-<cite>fig.update_layout(**pty_update_layout)</cite>
-Thus, take full control over</p>
+<dt><strong>save_config: dict, default: None</strong></dt><dd><p>Plotly only.</p>
+<p>Pass config options to
+<cite>plotly.graph_objects.Figure.write_html(config=save_config)</cite>.</p>
 </dd>
-<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass a function reference to further style the plotly graphs.
-Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
-<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
-<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<dt><strong>global_custom_func: function, default: None</strong></dt><dd><p>Pass a function reference to further style the figures.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">global_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;watermark&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">conf</span><span class="o">.</span><span class="n">GLOBAL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">global_custom_func</span><span class="o">=</span><span class="n">global_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># global_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
-<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.
-Pass a function reference to further style the matplotlib graphs.
+<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.</p>
+<p>Pass a function reference to further style the matplotlib graphs.
 Function must accept <cite>fig, ax</cite> and return <cite>fig, ax</cite>.</p>
-<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the plot is
-just 1x1.</p>
+<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the
+plot is just 1x1.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mpl_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
 <span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
 <span class="gp">... </span>    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">do_more</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">MPL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+<span class="gp">...</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">mpl_custom_func</span><span class="o">=</span><span class="n">mpl_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># mpl_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</dd>
+<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass a function reference to further style the plotly graphs.
+Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">PTY_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">pty_custom_func</span><span class="o">=</span><span class="n">pty_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># pty_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
+<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass keyword arguments to plotly’s
+<cite>plotly.graph_objects.Figure.update_layout(**pty_update_layout)</cite></p>
+<p>Default: None</p>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1403,7 +1534,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="interplot.scatter">
-<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">scatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.scatter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">scatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_cycle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.scatter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Draw a line or scatter plot.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1485,7 +1616,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>interactive: bool, default: True</strong></dt><dd><p>Display an interactive plotly line plot
 instead of the default matplotlib figure.</p>
 </dd>
-<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with x rows and y columns.</p>
+<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with <cite>n</cite> rows and <cite>m</cite> columns.</p>
 </dd>
 <dt><strong>title: str, default: None</strong></dt><dd><p>Plot title.</p>
 </dd>
@@ -1497,7 +1628,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt>In case of multiple rows/cols provide either:</dt><dd><ul class="simple">
 <li><p>a tuple</p></li>
 <li><p>a tuple for each row</p></li>
-<li><p>a tuple for each row containing tuple for each column.</p></li>
+<li><p>a tuple for each row containing a tuple for each column.</p></li>
 </ul>
 </dd>
 </dl>
@@ -1516,14 +1647,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>column_widths, row_heights: tuple/list, default: None</strong></dt><dd><p>Ratios of the width/height dimensions in each column/row.
 Will be normalised to a sum of 1.</p>
 </dd>
-<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Figure size in pixels.</p>
-<dl class="simple">
-<dt>Default behavior:</dt><dd><ul class="simple">
-<li><p>MPL: Default figure size.</p></li>
-<li><p>PLT: Responsive sizing.</p></li>
-</ul>
-</dd>
-</dl>
+<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Width, height in pixels.</p>
+<p>Default behavior defined by <cite>conf.MPL_FIG_SIZE</cite>
+and <cite>conf.PTY_FIG_SIZE</cite>.
+Plotly allows None for responsive size adapting to viewport.</p>
 </dd>
 <dt><strong>dpi: int, default: 100</strong></dt><dd><p>Plot resolution.</p>
 </dd>
@@ -1540,6 +1667,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 the subplots may be provided.</p>
 <p>PTY: Just provide a <cite>str</cite>.</p>
 </dd>
+<dt><strong>color_cycle: list, optional</strong></dt><dd><p>Cycle through colors in the provided list.</p>
+<p>If left <cite>None</cite>, the default color cycle
+<cite>conf.COLOR_CYCLE</cite> will be used.</p>
+</dd>
 <dt><strong>save_fig: str or pathlib.Path, default: None</strong></dt><dd><p>Provide a path to export the plot.</p>
 <p>Possible formats: png, jpg, svg, html, …</p>
 <p>The figure will only be saved on calling the instance’s
@@ -1553,32 +1684,79 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <p>An iterable of multiple formats may be provided. In this case
 the save command will be repeated for each element.</p>
 </dd>
-<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass keyword arguments to plotly’s
-<cite>fig.update_layout(**pty_update_layout)</cite>
-Thus, take full control over</p>
+<dt><strong>save_config: dict, default: None</strong></dt><dd><p>Plotly only.</p>
+<p>Pass config options to
+<cite>plotly.graph_objects.Figure.write_html(config=save_config)</cite>.</p>
 </dd>
-<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass a function reference to further style the plotly graphs.
-Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
-<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
-<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<dt><strong>global_custom_func: function, default: None</strong></dt><dd><p>Pass a function reference to further style the figures.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">global_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;watermark&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">conf</span><span class="o">.</span><span class="n">GLOBAL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">global_custom_func</span><span class="o">=</span><span class="n">global_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># global_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
-<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.
-Pass a function reference to further style the matplotlib graphs.
+<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.</p>
+<p>Pass a function reference to further style the matplotlib graphs.
 Function must accept <cite>fig, ax</cite> and return <cite>fig, ax</cite>.</p>
-<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the plot is
-just 1x1.</p>
+<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the
+plot is just 1x1.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mpl_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
 <span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
 <span class="gp">... </span>    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">do_more</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">MPL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+<span class="gp">...</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">mpl_custom_func</span><span class="o">=</span><span class="n">mpl_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># mpl_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
+<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass a function reference to further style the plotly graphs.
+Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">PTY_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">pty_custom_func</span><span class="o">=</span><span class="n">pty_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># pty_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</dd>
+<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass keyword arguments to plotly’s
+<cite>plotly.graph_objects.Figure.update_layout(**pty_update_layout)</cite></p>
+<p>Default: None</p>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1632,7 +1810,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="interplot.linescatter">
-<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">linescatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.linescatter" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">linescatter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_cycle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.linescatter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Draw a line or scatter plot.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1714,7 +1892,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>interactive: bool, default: True</strong></dt><dd><p>Display an interactive plotly line plot
 instead of the default matplotlib figure.</p>
 </dd>
-<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with x rows and y columns.</p>
+<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with <cite>n</cite> rows and <cite>m</cite> columns.</p>
 </dd>
 <dt><strong>title: str, default: None</strong></dt><dd><p>Plot title.</p>
 </dd>
@@ -1726,7 +1904,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt>In case of multiple rows/cols provide either:</dt><dd><ul class="simple">
 <li><p>a tuple</p></li>
 <li><p>a tuple for each row</p></li>
-<li><p>a tuple for each row containing tuple for each column.</p></li>
+<li><p>a tuple for each row containing a tuple for each column.</p></li>
 </ul>
 </dd>
 </dl>
@@ -1745,14 +1923,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>column_widths, row_heights: tuple/list, default: None</strong></dt><dd><p>Ratios of the width/height dimensions in each column/row.
 Will be normalised to a sum of 1.</p>
 </dd>
-<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Figure size in pixels.</p>
-<dl class="simple">
-<dt>Default behavior:</dt><dd><ul class="simple">
-<li><p>MPL: Default figure size.</p></li>
-<li><p>PLT: Responsive sizing.</p></li>
-</ul>
-</dd>
-</dl>
+<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Width, height in pixels.</p>
+<p>Default behavior defined by <cite>conf.MPL_FIG_SIZE</cite>
+and <cite>conf.PTY_FIG_SIZE</cite>.
+Plotly allows None for responsive size adapting to viewport.</p>
 </dd>
 <dt><strong>dpi: int, default: 100</strong></dt><dd><p>Plot resolution.</p>
 </dd>
@@ -1769,6 +1943,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 the subplots may be provided.</p>
 <p>PTY: Just provide a <cite>str</cite>.</p>
 </dd>
+<dt><strong>color_cycle: list, optional</strong></dt><dd><p>Cycle through colors in the provided list.</p>
+<p>If left <cite>None</cite>, the default color cycle
+<cite>conf.COLOR_CYCLE</cite> will be used.</p>
+</dd>
 <dt><strong>save_fig: str or pathlib.Path, default: None</strong></dt><dd><p>Provide a path to export the plot.</p>
 <p>Possible formats: png, jpg, svg, html, …</p>
 <p>The figure will only be saved on calling the instance’s
@@ -1782,32 +1960,79 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <p>An iterable of multiple formats may be provided. In this case
 the save command will be repeated for each element.</p>
 </dd>
-<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass keyword arguments to plotly’s
-<cite>fig.update_layout(**pty_update_layout)</cite>
-Thus, take full control over</p>
+<dt><strong>save_config: dict, default: None</strong></dt><dd><p>Plotly only.</p>
+<p>Pass config options to
+<cite>plotly.graph_objects.Figure.write_html(config=save_config)</cite>.</p>
 </dd>
-<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass a function reference to further style the plotly graphs.
-Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
-<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
-<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<dt><strong>global_custom_func: function, default: None</strong></dt><dd><p>Pass a function reference to further style the figures.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">global_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;watermark&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">conf</span><span class="o">.</span><span class="n">GLOBAL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">global_custom_func</span><span class="o">=</span><span class="n">global_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># global_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
-<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.
-Pass a function reference to further style the matplotlib graphs.
+<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.</p>
+<p>Pass a function reference to further style the matplotlib graphs.
 Function must accept <cite>fig, ax</cite> and return <cite>fig, ax</cite>.</p>
-<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the plot is
-just 1x1.</p>
+<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the
+plot is just 1x1.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mpl_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
 <span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
 <span class="gp">... </span>    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">do_more</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">MPL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+<span class="gp">...</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">mpl_custom_func</span><span class="o">=</span><span class="n">mpl_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># mpl_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</dd>
+<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass a function reference to further style the plotly graphs.
+Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">PTY_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">pty_custom_func</span><span class="o">=</span><span class="n">pty_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># pty_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
+<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass keyword arguments to plotly’s
+<cite>plotly.graph_objects.Figure.update_layout(**pty_update_layout)</cite></p>
+<p>Default: None</p>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -1861,7 +2086,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="interplot.bar">
-<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">bar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.bar" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">bar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_cycle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.bar" title="Permalink to this definition">¶</a></dt>
 <dd><p>Draw a bar plot.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -1914,7 +2139,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>interactive: bool, default: True</strong></dt><dd><p>Display an interactive plotly line plot
 instead of the default matplotlib figure.</p>
 </dd>
-<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with x rows and y columns.</p>
+<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with <cite>n</cite> rows and <cite>m</cite> columns.</p>
 </dd>
 <dt><strong>title: str, default: None</strong></dt><dd><p>Plot title.</p>
 </dd>
@@ -1926,7 +2151,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt>In case of multiple rows/cols provide either:</dt><dd><ul class="simple">
 <li><p>a tuple</p></li>
 <li><p>a tuple for each row</p></li>
-<li><p>a tuple for each row containing tuple for each column.</p></li>
+<li><p>a tuple for each row containing a tuple for each column.</p></li>
 </ul>
 </dd>
 </dl>
@@ -1945,14 +2170,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>column_widths, row_heights: tuple/list, default: None</strong></dt><dd><p>Ratios of the width/height dimensions in each column/row.
 Will be normalised to a sum of 1.</p>
 </dd>
-<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Figure size in pixels.</p>
-<dl class="simple">
-<dt>Default behavior:</dt><dd><ul class="simple">
-<li><p>MPL: Default figure size.</p></li>
-<li><p>PLT: Responsive sizing.</p></li>
-</ul>
-</dd>
-</dl>
+<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Width, height in pixels.</p>
+<p>Default behavior defined by <cite>conf.MPL_FIG_SIZE</cite>
+and <cite>conf.PTY_FIG_SIZE</cite>.
+Plotly allows None for responsive size adapting to viewport.</p>
 </dd>
 <dt><strong>dpi: int, default: 100</strong></dt><dd><p>Plot resolution.</p>
 </dd>
@@ -1969,6 +2190,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 the subplots may be provided.</p>
 <p>PTY: Just provide a <cite>str</cite>.</p>
 </dd>
+<dt><strong>color_cycle: list, optional</strong></dt><dd><p>Cycle through colors in the provided list.</p>
+<p>If left <cite>None</cite>, the default color cycle
+<cite>conf.COLOR_CYCLE</cite> will be used.</p>
+</dd>
 <dt><strong>save_fig: str or pathlib.Path, default: None</strong></dt><dd><p>Provide a path to export the plot.</p>
 <p>Possible formats: png, jpg, svg, html, …</p>
 <p>The figure will only be saved on calling the instance’s
@@ -1982,32 +2207,79 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <p>An iterable of multiple formats may be provided. In this case
 the save command will be repeated for each element.</p>
 </dd>
-<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass keyword arguments to plotly’s
-<cite>fig.update_layout(**pty_update_layout)</cite>
-Thus, take full control over</p>
+<dt><strong>save_config: dict, default: None</strong></dt><dd><p>Plotly only.</p>
+<p>Pass config options to
+<cite>plotly.graph_objects.Figure.write_html(config=save_config)</cite>.</p>
 </dd>
-<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass a function reference to further style the plotly graphs.
-Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
-<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
-<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<dt><strong>global_custom_func: function, default: None</strong></dt><dd><p>Pass a function reference to further style the figures.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">global_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;watermark&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">conf</span><span class="o">.</span><span class="n">GLOBAL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">global_custom_func</span><span class="o">=</span><span class="n">global_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># global_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
-<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.
-Pass a function reference to further style the matplotlib graphs.
+<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.</p>
+<p>Pass a function reference to further style the matplotlib graphs.
 Function must accept <cite>fig, ax</cite> and return <cite>fig, ax</cite>.</p>
-<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the plot is
-just 1x1.</p>
+<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the
+plot is just 1x1.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mpl_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
 <span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
 <span class="gp">... </span>    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">do_more</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">MPL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+<span class="gp">...</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">mpl_custom_func</span><span class="o">=</span><span class="n">mpl_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># mpl_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</dd>
+<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass a function reference to further style the plotly graphs.
+Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">PTY_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">pty_custom_func</span><span class="o">=</span><span class="n">pty_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># pty_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
+<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass keyword arguments to plotly’s
+<cite>plotly.graph_objects.Figure.update_layout(**pty_update_layout)</cite></p>
+<p>Default: None</p>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2020,7 +2292,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="interplot.hist">
-<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">hist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.hist" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">hist</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_cycle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.hist" title="Permalink to this definition">¶</a></dt>
 <dd><p>Draw a histogram.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -2052,7 +2324,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>interactive: bool, default: True</strong></dt><dd><p>Display an interactive plotly line plot
 instead of the default matplotlib figure.</p>
 </dd>
-<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with x rows and y columns.</p>
+<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with <cite>n</cite> rows and <cite>m</cite> columns.</p>
 </dd>
 <dt><strong>title: str, default: None</strong></dt><dd><p>Plot title.</p>
 </dd>
@@ -2064,7 +2336,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt>In case of multiple rows/cols provide either:</dt><dd><ul class="simple">
 <li><p>a tuple</p></li>
 <li><p>a tuple for each row</p></li>
-<li><p>a tuple for each row containing tuple for each column.</p></li>
+<li><p>a tuple for each row containing a tuple for each column.</p></li>
 </ul>
 </dd>
 </dl>
@@ -2083,14 +2355,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>column_widths, row_heights: tuple/list, default: None</strong></dt><dd><p>Ratios of the width/height dimensions in each column/row.
 Will be normalised to a sum of 1.</p>
 </dd>
-<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Figure size in pixels.</p>
-<dl class="simple">
-<dt>Default behavior:</dt><dd><ul class="simple">
-<li><p>MPL: Default figure size.</p></li>
-<li><p>PLT: Responsive sizing.</p></li>
-</ul>
-</dd>
-</dl>
+<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Width, height in pixels.</p>
+<p>Default behavior defined by <cite>conf.MPL_FIG_SIZE</cite>
+and <cite>conf.PTY_FIG_SIZE</cite>.
+Plotly allows None for responsive size adapting to viewport.</p>
 </dd>
 <dt><strong>dpi: int, default: 100</strong></dt><dd><p>Plot resolution.</p>
 </dd>
@@ -2107,6 +2375,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 the subplots may be provided.</p>
 <p>PTY: Just provide a <cite>str</cite>.</p>
 </dd>
+<dt><strong>color_cycle: list, optional</strong></dt><dd><p>Cycle through colors in the provided list.</p>
+<p>If left <cite>None</cite>, the default color cycle
+<cite>conf.COLOR_CYCLE</cite> will be used.</p>
+</dd>
 <dt><strong>save_fig: str or pathlib.Path, default: None</strong></dt><dd><p>Provide a path to export the plot.</p>
 <p>Possible formats: png, jpg, svg, html, …</p>
 <p>The figure will only be saved on calling the instance’s
@@ -2120,32 +2392,79 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <p>An iterable of multiple formats may be provided. In this case
 the save command will be repeated for each element.</p>
 </dd>
-<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass keyword arguments to plotly’s
-<cite>fig.update_layout(**pty_update_layout)</cite>
-Thus, take full control over</p>
+<dt><strong>save_config: dict, default: None</strong></dt><dd><p>Plotly only.</p>
+<p>Pass config options to
+<cite>plotly.graph_objects.Figure.write_html(config=save_config)</cite>.</p>
 </dd>
-<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass a function reference to further style the plotly graphs.
-Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
-<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
-<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<dt><strong>global_custom_func: function, default: None</strong></dt><dd><p>Pass a function reference to further style the figures.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">global_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;watermark&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">conf</span><span class="o">.</span><span class="n">GLOBAL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">global_custom_func</span><span class="o">=</span><span class="n">global_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># global_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
-<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.
-Pass a function reference to further style the matplotlib graphs.
+<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.</p>
+<p>Pass a function reference to further style the matplotlib graphs.
 Function must accept <cite>fig, ax</cite> and return <cite>fig, ax</cite>.</p>
-<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the plot is
-just 1x1.</p>
+<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the
+plot is just 1x1.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mpl_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
 <span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
 <span class="gp">... </span>    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">do_more</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">MPL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+<span class="gp">...</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">mpl_custom_func</span><span class="o">=</span><span class="n">mpl_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># mpl_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
+<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass a function reference to further style the plotly graphs.
+Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">PTY_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">pty_custom_func</span><span class="o">=</span><span class="n">pty_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># pty_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</dd>
+<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass keyword arguments to plotly’s
+<cite>plotly.graph_objects.Figure.update_layout(**pty_update_layout)</cite></p>
+<p>Default: None</p>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2158,7 +2477,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="interplot.boxplot">
-<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">boxplot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.boxplot" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">boxplot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_cycle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.boxplot" title="Permalink to this definition">¶</a></dt>
 <dd><p>Draw a boxplot.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -2191,7 +2510,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>interactive: bool, default: True</strong></dt><dd><p>Display an interactive plotly line plot
 instead of the default matplotlib figure.</p>
 </dd>
-<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with x rows and y columns.</p>
+<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with <cite>n</cite> rows and <cite>m</cite> columns.</p>
 </dd>
 <dt><strong>title: str, default: None</strong></dt><dd><p>Plot title.</p>
 </dd>
@@ -2203,7 +2522,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt>In case of multiple rows/cols provide either:</dt><dd><ul class="simple">
 <li><p>a tuple</p></li>
 <li><p>a tuple for each row</p></li>
-<li><p>a tuple for each row containing tuple for each column.</p></li>
+<li><p>a tuple for each row containing a tuple for each column.</p></li>
 </ul>
 </dd>
 </dl>
@@ -2222,14 +2541,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>column_widths, row_heights: tuple/list, default: None</strong></dt><dd><p>Ratios of the width/height dimensions in each column/row.
 Will be normalised to a sum of 1.</p>
 </dd>
-<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Figure size in pixels.</p>
-<dl class="simple">
-<dt>Default behavior:</dt><dd><ul class="simple">
-<li><p>MPL: Default figure size.</p></li>
-<li><p>PLT: Responsive sizing.</p></li>
-</ul>
-</dd>
-</dl>
+<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Width, height in pixels.</p>
+<p>Default behavior defined by <cite>conf.MPL_FIG_SIZE</cite>
+and <cite>conf.PTY_FIG_SIZE</cite>.
+Plotly allows None for responsive size adapting to viewport.</p>
 </dd>
 <dt><strong>dpi: int, default: 100</strong></dt><dd><p>Plot resolution.</p>
 </dd>
@@ -2246,6 +2561,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 the subplots may be provided.</p>
 <p>PTY: Just provide a <cite>str</cite>.</p>
 </dd>
+<dt><strong>color_cycle: list, optional</strong></dt><dd><p>Cycle through colors in the provided list.</p>
+<p>If left <cite>None</cite>, the default color cycle
+<cite>conf.COLOR_CYCLE</cite> will be used.</p>
+</dd>
 <dt><strong>save_fig: str or pathlib.Path, default: None</strong></dt><dd><p>Provide a path to export the plot.</p>
 <p>Possible formats: png, jpg, svg, html, …</p>
 <p>The figure will only be saved on calling the instance’s
@@ -2259,32 +2578,79 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <p>An iterable of multiple formats may be provided. In this case
 the save command will be repeated for each element.</p>
 </dd>
-<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass keyword arguments to plotly’s
-<cite>fig.update_layout(**pty_update_layout)</cite>
-Thus, take full control over</p>
+<dt><strong>save_config: dict, default: None</strong></dt><dd><p>Plotly only.</p>
+<p>Pass config options to
+<cite>plotly.graph_objects.Figure.write_html(config=save_config)</cite>.</p>
 </dd>
-<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass a function reference to further style the plotly graphs.
-Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
-<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
-<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<dt><strong>global_custom_func: function, default: None</strong></dt><dd><p>Pass a function reference to further style the figures.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">global_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;watermark&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">conf</span><span class="o">.</span><span class="n">GLOBAL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">global_custom_func</span><span class="o">=</span><span class="n">global_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># global_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
-<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.
-Pass a function reference to further style the matplotlib graphs.
+<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.</p>
+<p>Pass a function reference to further style the matplotlib graphs.
 Function must accept <cite>fig, ax</cite> and return <cite>fig, ax</cite>.</p>
-<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the plot is
-just 1x1.</p>
+<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the
+plot is just 1x1.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mpl_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
 <span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
 <span class="gp">... </span>    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">do_more</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">MPL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+<span class="gp">...</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">mpl_custom_func</span><span class="o">=</span><span class="n">mpl_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># mpl_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</dd>
+<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass a function reference to further style the plotly graphs.
+Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">PTY_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">pty_custom_func</span><span class="o">=</span><span class="n">pty_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># pty_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
+<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass keyword arguments to plotly’s
+<cite>plotly.graph_objects.Figure.update_layout(**pty_update_layout)</cite></p>
+<p>Default: None</p>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2297,7 +2663,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="interplot.heatmap">
-<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">heatmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.heatmap" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">heatmap</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_cycle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.heatmap" title="Permalink to this definition">¶</a></dt>
 <dd><p>Draw a heatmap.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -2328,7 +2694,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>interactive: bool, default: True</strong></dt><dd><p>Display an interactive plotly line plot
 instead of the default matplotlib figure.</p>
 </dd>
-<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with x rows and y columns.</p>
+<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with <cite>n</cite> rows and <cite>m</cite> columns.</p>
 </dd>
 <dt><strong>title: str, default: None</strong></dt><dd><p>Plot title.</p>
 </dd>
@@ -2340,7 +2706,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt>In case of multiple rows/cols provide either:</dt><dd><ul class="simple">
 <li><p>a tuple</p></li>
 <li><p>a tuple for each row</p></li>
-<li><p>a tuple for each row containing tuple for each column.</p></li>
+<li><p>a tuple for each row containing a tuple for each column.</p></li>
 </ul>
 </dd>
 </dl>
@@ -2359,14 +2725,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>column_widths, row_heights: tuple/list, default: None</strong></dt><dd><p>Ratios of the width/height dimensions in each column/row.
 Will be normalised to a sum of 1.</p>
 </dd>
-<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Figure size in pixels.</p>
-<dl class="simple">
-<dt>Default behavior:</dt><dd><ul class="simple">
-<li><p>MPL: Default figure size.</p></li>
-<li><p>PLT: Responsive sizing.</p></li>
-</ul>
-</dd>
-</dl>
+<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Width, height in pixels.</p>
+<p>Default behavior defined by <cite>conf.MPL_FIG_SIZE</cite>
+and <cite>conf.PTY_FIG_SIZE</cite>.
+Plotly allows None for responsive size adapting to viewport.</p>
 </dd>
 <dt><strong>dpi: int, default: 100</strong></dt><dd><p>Plot resolution.</p>
 </dd>
@@ -2383,6 +2745,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 the subplots may be provided.</p>
 <p>PTY: Just provide a <cite>str</cite>.</p>
 </dd>
+<dt><strong>color_cycle: list, optional</strong></dt><dd><p>Cycle through colors in the provided list.</p>
+<p>If left <cite>None</cite>, the default color cycle
+<cite>conf.COLOR_CYCLE</cite> will be used.</p>
+</dd>
 <dt><strong>save_fig: str or pathlib.Path, default: None</strong></dt><dd><p>Provide a path to export the plot.</p>
 <p>Possible formats: png, jpg, svg, html, …</p>
 <p>The figure will only be saved on calling the instance’s
@@ -2396,32 +2762,79 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <p>An iterable of multiple formats may be provided. In this case
 the save command will be repeated for each element.</p>
 </dd>
-<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass keyword arguments to plotly’s
-<cite>fig.update_layout(**pty_update_layout)</cite>
-Thus, take full control over</p>
+<dt><strong>save_config: dict, default: None</strong></dt><dd><p>Plotly only.</p>
+<p>Pass config options to
+<cite>plotly.graph_objects.Figure.write_html(config=save_config)</cite>.</p>
 </dd>
-<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass a function reference to further style the plotly graphs.
-Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
-<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
-<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<dt><strong>global_custom_func: function, default: None</strong></dt><dd><p>Pass a function reference to further style the figures.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">global_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;watermark&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">conf</span><span class="o">.</span><span class="n">GLOBAL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">global_custom_func</span><span class="o">=</span><span class="n">global_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># global_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
-<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.
-Pass a function reference to further style the matplotlib graphs.
+<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.</p>
+<p>Pass a function reference to further style the matplotlib graphs.
 Function must accept <cite>fig, ax</cite> and return <cite>fig, ax</cite>.</p>
-<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the plot is
-just 1x1.</p>
+<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the
+plot is just 1x1.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mpl_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
 <span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
 <span class="gp">... </span>    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">do_more</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">MPL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+<span class="gp">...</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">mpl_custom_func</span><span class="o">=</span><span class="n">mpl_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># mpl_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
+<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass a function reference to further style the plotly graphs.
+Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">PTY_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">pty_custom_func</span><span class="o">=</span><span class="n">pty_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># pty_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</dd>
+<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass keyword arguments to plotly’s
+<cite>plotly.graph_objects.Figure.update_layout(**pty_update_layout)</cite></p>
+<p>Default: None</p>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2434,7 +2847,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="interplot.regression">
-<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.regression" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_cycle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.regression" title="Permalink to this definition">¶</a></dt>
 <dd><p>Draw a linear regression plot.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -2455,7 +2868,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>interactive: bool, default: True</strong></dt><dd><p>Display an interactive plotly line plot
 instead of the default matplotlib figure.</p>
 </dd>
-<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with x rows and y columns.</p>
+<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with <cite>n</cite> rows and <cite>m</cite> columns.</p>
 </dd>
 <dt><strong>title: str, default: None</strong></dt><dd><p>Plot title.</p>
 </dd>
@@ -2467,7 +2880,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt>In case of multiple rows/cols provide either:</dt><dd><ul class="simple">
 <li><p>a tuple</p></li>
 <li><p>a tuple for each row</p></li>
-<li><p>a tuple for each row containing tuple for each column.</p></li>
+<li><p>a tuple for each row containing a tuple for each column.</p></li>
 </ul>
 </dd>
 </dl>
@@ -2486,14 +2899,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>column_widths, row_heights: tuple/list, default: None</strong></dt><dd><p>Ratios of the width/height dimensions in each column/row.
 Will be normalised to a sum of 1.</p>
 </dd>
-<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Figure size in pixels.</p>
-<dl class="simple">
-<dt>Default behavior:</dt><dd><ul class="simple">
-<li><p>MPL: Default figure size.</p></li>
-<li><p>PLT: Responsive sizing.</p></li>
-</ul>
-</dd>
-</dl>
+<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Width, height in pixels.</p>
+<p>Default behavior defined by <cite>conf.MPL_FIG_SIZE</cite>
+and <cite>conf.PTY_FIG_SIZE</cite>.
+Plotly allows None for responsive size adapting to viewport.</p>
 </dd>
 <dt><strong>dpi: int, default: 100</strong></dt><dd><p>Plot resolution.</p>
 </dd>
@@ -2510,6 +2919,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 the subplots may be provided.</p>
 <p>PTY: Just provide a <cite>str</cite>.</p>
 </dd>
+<dt><strong>color_cycle: list, optional</strong></dt><dd><p>Cycle through colors in the provided list.</p>
+<p>If left <cite>None</cite>, the default color cycle
+<cite>conf.COLOR_CYCLE</cite> will be used.</p>
+</dd>
 <dt><strong>save_fig: str or pathlib.Path, default: None</strong></dt><dd><p>Provide a path to export the plot.</p>
 <p>Possible formats: png, jpg, svg, html, …</p>
 <p>The figure will only be saved on calling the instance’s
@@ -2523,32 +2936,79 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <p>An iterable of multiple formats may be provided. In this case
 the save command will be repeated for each element.</p>
 </dd>
-<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass keyword arguments to plotly’s
-<cite>fig.update_layout(**pty_update_layout)</cite>
-Thus, take full control over</p>
+<dt><strong>save_config: dict, default: None</strong></dt><dd><p>Plotly only.</p>
+<p>Pass config options to
+<cite>plotly.graph_objects.Figure.write_html(config=save_config)</cite>.</p>
 </dd>
-<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass a function reference to further style the plotly graphs.
-Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
-<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
-<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<dt><strong>global_custom_func: function, default: None</strong></dt><dd><p>Pass a function reference to further style the figures.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">global_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;watermark&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">conf</span><span class="o">.</span><span class="n">GLOBAL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">global_custom_func</span><span class="o">=</span><span class="n">global_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># global_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
-<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.
-Pass a function reference to further style the matplotlib graphs.
+<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.</p>
+<p>Pass a function reference to further style the matplotlib graphs.
 Function must accept <cite>fig, ax</cite> and return <cite>fig, ax</cite>.</p>
-<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the plot is
-just 1x1.</p>
+<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the
+plot is just 1x1.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mpl_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
 <span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
 <span class="gp">... </span>    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">do_more</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">MPL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+<span class="gp">...</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">mpl_custom_func</span><span class="o">=</span><span class="n">mpl_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># mpl_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
+<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass a function reference to further style the plotly graphs.
+Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">PTY_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">pty_custom_func</span><span class="o">=</span><span class="n">pty_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># pty_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</dd>
+<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass keyword arguments to plotly’s
+<cite>plotly.graph_objects.Figure.update_layout(**pty_update_layout)</cite></p>
+<p>Default: None</p>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2561,7 +3021,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="interplot.fill">
-<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">fill</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.fill" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">fill</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_cycle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.fill" title="Permalink to this definition">¶</a></dt>
 <dd><p>Draw a fill between two y lines.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -2594,7 +3054,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>interactive: bool, default: True</strong></dt><dd><p>Display an interactive plotly line plot
 instead of the default matplotlib figure.</p>
 </dd>
-<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with x rows and y columns.</p>
+<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with <cite>n</cite> rows and <cite>m</cite> columns.</p>
 </dd>
 <dt><strong>title: str, default: None</strong></dt><dd><p>Plot title.</p>
 </dd>
@@ -2606,7 +3066,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt>In case of multiple rows/cols provide either:</dt><dd><ul class="simple">
 <li><p>a tuple</p></li>
 <li><p>a tuple for each row</p></li>
-<li><p>a tuple for each row containing tuple for each column.</p></li>
+<li><p>a tuple for each row containing a tuple for each column.</p></li>
 </ul>
 </dd>
 </dl>
@@ -2625,14 +3085,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>column_widths, row_heights: tuple/list, default: None</strong></dt><dd><p>Ratios of the width/height dimensions in each column/row.
 Will be normalised to a sum of 1.</p>
 </dd>
-<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Figure size in pixels.</p>
-<dl class="simple">
-<dt>Default behavior:</dt><dd><ul class="simple">
-<li><p>MPL: Default figure size.</p></li>
-<li><p>PLT: Responsive sizing.</p></li>
-</ul>
-</dd>
-</dl>
+<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Width, height in pixels.</p>
+<p>Default behavior defined by <cite>conf.MPL_FIG_SIZE</cite>
+and <cite>conf.PTY_FIG_SIZE</cite>.
+Plotly allows None for responsive size adapting to viewport.</p>
 </dd>
 <dt><strong>dpi: int, default: 100</strong></dt><dd><p>Plot resolution.</p>
 </dd>
@@ -2649,6 +3105,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 the subplots may be provided.</p>
 <p>PTY: Just provide a <cite>str</cite>.</p>
 </dd>
+<dt><strong>color_cycle: list, optional</strong></dt><dd><p>Cycle through colors in the provided list.</p>
+<p>If left <cite>None</cite>, the default color cycle
+<cite>conf.COLOR_CYCLE</cite> will be used.</p>
+</dd>
 <dt><strong>save_fig: str or pathlib.Path, default: None</strong></dt><dd><p>Provide a path to export the plot.</p>
 <p>Possible formats: png, jpg, svg, html, …</p>
 <p>The figure will only be saved on calling the instance’s
@@ -2662,32 +3122,79 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <p>An iterable of multiple formats may be provided. In this case
 the save command will be repeated for each element.</p>
 </dd>
-<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass keyword arguments to plotly’s
-<cite>fig.update_layout(**pty_update_layout)</cite>
-Thus, take full control over</p>
+<dt><strong>save_config: dict, default: None</strong></dt><dd><p>Plotly only.</p>
+<p>Pass config options to
+<cite>plotly.graph_objects.Figure.write_html(config=save_config)</cite>.</p>
 </dd>
-<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass a function reference to further style the plotly graphs.
-Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
-<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
-<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<dt><strong>global_custom_func: function, default: None</strong></dt><dd><p>Pass a function reference to further style the figures.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">global_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;watermark&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">conf</span><span class="o">.</span><span class="n">GLOBAL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">global_custom_func</span><span class="o">=</span><span class="n">global_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># global_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
-<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.
-Pass a function reference to further style the matplotlib graphs.
+<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.</p>
+<p>Pass a function reference to further style the matplotlib graphs.
 Function must accept <cite>fig, ax</cite> and return <cite>fig, ax</cite>.</p>
-<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the plot is
-just 1x1.</p>
+<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the
+plot is just 1x1.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mpl_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
 <span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
 <span class="gp">... </span>    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">do_more</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">MPL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+<span class="gp">...</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">mpl_custom_func</span><span class="o">=</span><span class="n">mpl_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># mpl_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</dd>
+<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass a function reference to further style the plotly graphs.
+Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">PTY_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">pty_custom_func</span><span class="o">=</span><span class="n">pty_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># pty_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
+<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass keyword arguments to plotly’s
+<cite>plotly.graph_objects.Figure.update_layout(**pty_update_layout)</cite></p>
+<p>Default: None</p>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2700,7 +3207,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 
 <dl class="py function">
 <dt class="sig sig-object py" id="interplot.text">
-<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.text" title="Permalink to this definition">¶</a></dt>
+<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">text</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">skip_post_process</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">interactive</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rows</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylabel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xlim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ylim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_xaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shared_yaxes</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">column_widths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">row_heights</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dpi</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_loc</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">legend_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_cycle</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_fig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_config</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mpl_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_custom_func</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pty_update_layout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.text" title="Permalink to this definition">¶</a></dt>
 <dd><p>Draw text.</p>
 <dl class="field-list">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
@@ -2756,7 +3263,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>interactive: bool, default: True</strong></dt><dd><p>Display an interactive plotly line plot
 instead of the default matplotlib figure.</p>
 </dd>
-<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with x rows and y columns.</p>
+<dt><strong>rows, cols: int, default: 1</strong></dt><dd><p>Create a grid with <cite>n</cite> rows and <cite>m</cite> columns.</p>
 </dd>
 <dt><strong>title: str, default: None</strong></dt><dd><p>Plot title.</p>
 </dd>
@@ -2768,7 +3275,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt>In case of multiple rows/cols provide either:</dt><dd><ul class="simple">
 <li><p>a tuple</p></li>
 <li><p>a tuple for each row</p></li>
-<li><p>a tuple for each row containing tuple for each column.</p></li>
+<li><p>a tuple for each row containing a tuple for each column.</p></li>
 </ul>
 </dd>
 </dl>
@@ -2787,14 +3294,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <dt><strong>column_widths, row_heights: tuple/list, default: None</strong></dt><dd><p>Ratios of the width/height dimensions in each column/row.
 Will be normalised to a sum of 1.</p>
 </dd>
-<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Figure size in pixels.</p>
-<dl class="simple">
-<dt>Default behavior:</dt><dd><ul class="simple">
-<li><p>MPL: Default figure size.</p></li>
-<li><p>PLT: Responsive sizing.</p></li>
-</ul>
-</dd>
-</dl>
+<dt><strong>fig_size: tuple of 2x float, optional</strong></dt><dd><p>Width, height in pixels.</p>
+<p>Default behavior defined by <cite>conf.MPL_FIG_SIZE</cite>
+and <cite>conf.PTY_FIG_SIZE</cite>.
+Plotly allows None for responsive size adapting to viewport.</p>
 </dd>
 <dt><strong>dpi: int, default: 100</strong></dt><dd><p>Plot resolution.</p>
 </dd>
@@ -2811,6 +3314,10 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 the subplots may be provided.</p>
 <p>PTY: Just provide a <cite>str</cite>.</p>
 </dd>
+<dt><strong>color_cycle: list, optional</strong></dt><dd><p>Cycle through colors in the provided list.</p>
+<p>If left <cite>None</cite>, the default color cycle
+<cite>conf.COLOR_CYCLE</cite> will be used.</p>
+</dd>
 <dt><strong>save_fig: str or pathlib.Path, default: None</strong></dt><dd><p>Provide a path to export the plot.</p>
 <p>Possible formats: png, jpg, svg, html, …</p>
 <p>The figure will only be saved on calling the instance’s
@@ -2824,32 +3331,79 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 <p>An iterable of multiple formats may be provided. In this case
 the save command will be repeated for each element.</p>
 </dd>
-<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass keyword arguments to plotly’s
-<cite>fig.update_layout(**pty_update_layout)</cite>
-Thus, take full control over</p>
+<dt><strong>save_config: dict, default: None</strong></dt><dd><p>Plotly only.</p>
+<p>Pass config options to
+<cite>plotly.graph_objects.Figure.write_html(config=save_config)</cite>.</p>
 </dd>
-<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.
-Pass a function reference to further style the plotly graphs.
-Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
-<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
-<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
-<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<dt><strong>global_custom_func: function, default: None</strong></dt><dd><p>Pass a function reference to further style the figures.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">global_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="s2">&quot;watermark&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">conf</span><span class="o">.</span><span class="n">GLOBAL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">global_custom_func</span><span class="o">=</span><span class="n">global_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># global_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
-<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.
-Pass a function reference to further style the matplotlib graphs.
+<dt><strong>mpl_custom_func: function, default: None</strong></dt><dd><p>MATPLOTLIB ONLY.</p>
+<p>Pass a function reference to further style the matplotlib graphs.
 Function must accept <cite>fig, ax</cite> and return <cite>fig, ax</cite>.</p>
-<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the plot is
-just 1x1.</p>
+<p>Note: <cite>ax</cite> always has <cite>row</cite> and <cite>col</cite> coordinates, even if the
+plot is just 1x1.</p>
 <div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">mpl_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
 <span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
 <span class="gp">... </span>    <span class="n">ax</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">do_more</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">MPL_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">)</span>
+<span class="gp">...</span>
 <span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">mpl_custom_func</span><span class="o">=</span><span class="n">mpl_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># mpl_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
 </pre></div>
 </div>
 </dd>
+<dt><strong>pty_custom_func: function, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass a function reference to further style the plotly graphs.
+Function must accept <cite>fig</cite> and return <cite>fig</cite>.</p>
+<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">pty_custom_func</span><span class="p">(</span><span class="n">fig</span><span class="p">):</span>
+<span class="gp">... </span>    <span class="c1"># do your customisations</span>
+<span class="gp">... </span>    <span class="n">fig</span><span class="o">.</span><span class="n">do_stuff</span><span class="p">()</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="c1"># also include default function</span>
+<span class="gp">... </span>    <span class="n">fig</span> <span class="o">=</span> <span class="n">conf</span><span class="o">.</span><span class="n">PTY_CUSTOM_FUNC</span><span class="p">(</span><span class="n">fig</span><span class="p">)</span>
+<span class="gp">...</span>
+<span class="gp">... </span>    <span class="k">return</span> <span class="n">fig</span>
+<span class="gp">...</span>
+<span class="gp">... </span><span class="n">fig</span> <span class="o">=</span> <span class="n">interplot</span><span class="o">.</span><span class="n">Plot</span><span class="p">(</span>
+<span class="gp">... </span>    <span class="n">interactive</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="gp">... </span>    <span class="n">pty_custom_func</span><span class="o">=</span><span class="n">pty_custom_func</span>
+<span class="gp">... </span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">add_line</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">post_process</span><span class="p">()</span>  <span class="c1"># pty_custom_func will be executed here</span>
+<span class="gp">... </span><span class="n">fig</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</dd>
+<dt><strong>pty_update_layout: dict, default: None</strong></dt><dd><p>PLOTLY ONLY.</p>
+<p>Pass keyword arguments to plotly’s
+<cite>plotly.graph_objects.Figure.update_layout(**pty_update_layout)</cite></p>
+<p>Default: None</p>
+</dd>
 </dl>
 </dd>
 <dt class="field-even">Returns<span class="colon">:</span></dt>
@@ -2862,7 +3416,7 @@ <h2><a class="toc-backref" href="#id5" role="doc-backlink">One-line Plotting</a>
 
 </section>
 <section id="magic-plot-decorators">
-<h2><a class="toc-backref" href="#id6" role="doc-backlink"><cite>magic_plot</cite> decorators</a><a class="headerlink" href="#magic-plot-decorators" title="Permalink to this heading">¶</a></h2>
+<h2><a class="toc-backref" href="#id4" role="doc-backlink"><cite>magic_plot</cite> decorators</a><a class="headerlink" href="#magic-plot-decorators" title="Permalink to this heading">¶</a></h2>
 <dl class="py function">
 <dt class="sig sig-object py" id="interplot.magic_plot">
 <span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">magic_plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">core</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">doc_decorator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.magic_plot" title="Permalink to this definition">¶</a></dt>
@@ -2957,7 +3511,7 @@ <h2><a class="toc-backref" href="#id6" role="doc-backlink"><cite>magic_plot</cit
 
 </section>
 <section id="notebookinteraction-parent-class">
-<h2><a class="toc-backref" href="#id7" role="doc-backlink"><cite>NotebookInteraction</cite> Parent Class</a><a class="headerlink" href="#notebookinteraction-parent-class" title="Permalink to this heading">¶</a></h2>
+<h2><a class="toc-backref" href="#id5" role="doc-backlink"><cite>NotebookInteraction</cite> Parent Class</a><a class="headerlink" href="#notebookinteraction-parent-class" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="interplot.NotebookInteraction">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">NotebookInteraction</span></span><a class="headerlink" href="#interplot.NotebookInteraction" title="Permalink to this definition">¶</a></dt>
@@ -2999,7 +3553,7 @@ <h2><a class="toc-backref" href="#id7" role="doc-backlink"><cite>NotebookInterac
 
 </section>
 <section id="showdataarray-showdataset">
-<h2><a class="toc-backref" href="#id8" role="doc-backlink"><cite>ShowDataArray</cite>, <cite>ShowDataset</cite></a><a class="headerlink" href="#showdataarray-showdataset" title="Permalink to this heading">¶</a></h2>
+<h2><a class="toc-backref" href="#id6" role="doc-backlink"><cite>ShowDataArray</cite>, <cite>ShowDataset</cite></a><a class="headerlink" href="#showdataarray-showdataset" title="Permalink to this heading">¶</a></h2>
 <dl class="py class">
 <dt class="sig sig-object py" id="interplot.ShowDataArray">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">ShowDataArray</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_sel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">default_isel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.ShowDataArray" title="Permalink to this definition">¶</a></dt>
@@ -3167,6 +3721,29 @@ <h2><a class="toc-backref" href="#id8" role="doc-backlink"><cite>ShowDataArray</
 
 </dd></dl>
 
+</section>
+<section id="helper-functions">
+<h2><a class="toc-backref" href="#id7" role="doc-backlink">Helper functions</a><a class="headerlink" href="#helper-functions" title="Permalink to this heading">¶</a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="interplot.pick_non_none">
+<span class="sig-prename descclassname"><span class="pre">interplot.</span></span><span class="sig-name descname"><span class="pre">pick_non_none</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#interplot.pick_non_none" title="Permalink to this definition">¶</a></dt>
+<dd><p>Return the first non-None argument.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><dl class="simple">
+<dt><strong>*args: any</strong></dt><dd><p>Any number of arguments.</p>
+</dd>
+</dl>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><dl class="simple">
+<dt>any</dt><dd><p>The first non-None argument.</p>
+</dd>
+</dl>
+</dd>
+</dl>
+</dd></dl>
+
 </section>
 </section>
 
@@ -3185,6 +3762,7 @@ <h3><a href="index.html">Table of Contents</a></h3>
 <li class="toctree-l2"><a class="reference internal" href="#magic-plot-decorators"><cite>magic_plot</cite> decorators</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#notebookinteraction-parent-class"><cite>NotebookInteraction</cite> Parent Class</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#showdataarray-showdataset"><cite>ShowDataArray</cite>, <cite>ShowDataset</cite></a></li>
+<li class="toctree-l2"><a class="reference internal" href="#helper-functions">Helper functions</a></li>
 </ul>
 </li>
 </ul>
@@ -3218,6 +3796,7 @@ <h3><a href="index.html">Table of Contents</a></h3>
 <li><a class="reference internal" href="#interplot.Plot.post_process"><code class="docutils literal notranslate"><span class="pre">Plot.post_process()</span></code></a></li>
 <li><a class="reference internal" href="#interplot.Plot.save"><code class="docutils literal notranslate"><span class="pre">Plot.save()</span></code></a></li>
 <li><a class="reference internal" href="#interplot.Plot.show"><code class="docutils literal notranslate"><span class="pre">Plot.show()</span></code></a></li>
+<li><a class="reference internal" href="#interplot.Plot.close"><code class="docutils literal notranslate"><span class="pre">Plot.close()</span></code></a></li>
 <li><a class="reference internal" href="#interplot.Plot._repr_mimebundle_"><code class="docutils literal notranslate"><span class="pre">Plot._repr_mimebundle_()</span></code></a></li>
 <li><a class="reference internal" href="#interplot.Plot._repr_html_"><code class="docutils literal notranslate"><span class="pre">Plot._repr_html_()</span></code></a></li>
 <li><a class="reference internal" href="#interplot.Plot.JS_RENDER_WARNING"><code class="docutils literal notranslate"><span class="pre">Plot.JS_RENDER_WARNING</span></code></a></li>
@@ -3269,6 +3848,10 @@ <h3><a href="index.html">Table of Contents</a></h3>
 </li>
 </ul>
 </li>
+<li><a class="reference internal" href="#helper-functions">Helper functions</a><ul>
+<li><a class="reference internal" href="#interplot.pick_non_none"><code class="docutils literal notranslate"><span class="pre">pick_non_none()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 </ul>
diff --git a/docs/genindex.html b/docs/genindex.html
index 1976043..cf5f4b0 100644
--- a/docs/genindex.html
+++ b/docs/genindex.html
@@ -129,6 +129,10 @@ <h2 id="B">B</h2>
 
 <h2 id="C">C</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_plot.html#interplot.Plot.close">close() (interplot.Plot method)</a>
+</li>
+  </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="api_conf.html#interplot.conf.COLOR_CYCLE">COLOR_CYCLE (in module interplot.conf)</a>
 </li>
@@ -177,6 +181,10 @@ <h2 id="G">G</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="api_plot.html#interplot.Plot.get_cycle_color">get_cycle_color() (interplot.Plot method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_conf.html#interplot.conf.GLOBAL_CUSTOM_FUNC">GLOBAL_CUSTOM_FUNC() (in module interplot.conf)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -274,6 +282,8 @@ <h2 id="M">M</h2>
       </ul></li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_conf.html#interplot.conf.MPL_CUSTOM_FUNC">MPL_CUSTOM_FUNC() (in module interplot.conf)</a>
+</li>
       <li><a href="api_conf.html#interplot.conf.MPL_FIG_SIZE">MPL_FIG_SIZE (in module interplot.conf)</a>
 </li>
       <li><a href="api_conf.html#interplot.conf.MPL_LINE_STYLES">MPL_LINE_STYLES (in module interplot.conf)</a>
@@ -298,6 +308,8 @@ <h2 id="N">N</h2>
 <h2 id="P">P</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="api_plot.html#interplot.pick_non_none">pick_non_none() (in module interplot)</a>
+</li>
       <li><a href="api_plot.html#interplot.Plot">Plot (class in interplot)</a>
 </li>
       <li><a href="api_plot.html#interplot.ShowDataArray.plot">plot() (interplot.ShowDataArray method)</a>
@@ -311,6 +323,8 @@ <h2 id="P">P</h2>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="api_conf.html#interplot.conf.PTY_CONFIG">PTY_CONFIG (in module interplot.conf)</a>
+</li>
+      <li><a href="api_conf.html#interplot.conf.PTY_CUSTOM_FUNC">PTY_CUSTOM_FUNC() (in module interplot.conf)</a>
 </li>
       <li><a href="api_conf.html#interplot.conf.PTY_FIG_SIZE">PTY_FIG_SIZE (in module interplot.conf)</a>
 </li>
diff --git a/docs/index.html b/docs/index.html
index 89d479a..127cc51 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -53,6 +53,7 @@ <h2><a class="toc-backref" href="#id3" role="doc-backlink">Quick Start</a><a cla
 <li class="toctree-l2"><a class="reference internal" href="api_plot.html#magic-plot-decorators"><cite>magic_plot</cite> decorators</a></li>
 <li class="toctree-l2"><a class="reference internal" href="api_plot.html#notebookinteraction-parent-class"><cite>NotebookInteraction</cite> Parent Class</a></li>
 <li class="toctree-l2"><a class="reference internal" href="api_plot.html#showdataarray-showdataset"><cite>ShowDataArray</cite>, <cite>ShowDataset</cite></a></li>
+<li class="toctree-l2"><a class="reference internal" href="api_plot.html#helper-functions">Helper functions</a></li>
 </ul>
 </li>
 </ul>
diff --git a/docs/objects.inv b/docs/objects.inv
index f50ffb4..0ab8306 100644
Binary files a/docs/objects.inv and b/docs/objects.inv differ
diff --git a/docs/searchindex.js b/docs/searchindex.js
index 05bc83a..502d83e 100644
--- a/docs/searchindex.js
+++ b/docs/searchindex.js
@@ -1,1 +1,1 @@
-Search.setIndex({"docnames": ["api_conf", "api_iter", "api_plot", "index"], "filenames": ["api_conf.rst", "api_iter.rst", "api_plot.rst", "index.rst"], "titles": ["<cite>interplot.conf</cite>: Module Defaults Configuration", "<cite>interplot.iter</cite>: Iteration Tools Documentation and API reference", "<cite>interplot</cite> Documentation and API reference", "interplot"], "terms": {"modifi": [0, 2], "behavior": [0, 1, 2], "packag": [0, 3], "exampl": [0, 1, 2], "line": [0, 3], "x": [0, 2, 3], "y": [0, 2, 3], "interact": [0, 2, 3], "fals": [0, 1, 2, 3], "color_cycl": [0, 2], "0": [0, 2, 3], "000000": 0, "dpi": [0, 2], "150": 0, "mpl_fig_siz": 0, "400": 0, "user": [0, 2], "true": [0, 1, 2, 3], "type": [0, 1, 2], "bool": [0, 1, 2], "gener": [0, 1, 2], "plotli": [0, 2, 3], "figur": [0, 2, 3], "006ba4": 0, "ff800e": 0, "ababab": 0, "595959": 0, "5f9ed1": 0, "c85200": 0, "898989": 0, "a2c8ec": 0, "ffbc79": 0, "cfcfcf": 0, "list": [0, 1, 2], "color": [0, 2, 3], "cycl": [0, 2, 3], "through": [0, 2, 3], "i": [0, 1, 2, 3], "optimis": 0, "vision": 0, "defici": 0, "100": [0, 2, 3], "int": [0, 1, 2], "resolut": [0, 2], "dot": [0, 2], "per": 0, "inch": 0, "pty_fig_s": 0, "none": [0, 1, 2, 3], "500": [0, 2, 3], "tupl": [0, 1, 2], "size": [0, 2], "backend": 0, "px": 0, "us": [0, 1, 2, 3], "adapt": 0, "700": 0, "450": 0, "matplotlib": [0, 2, 3], "export_format": [0, 2], "png": [0, 2, 3], "str": [0, 2], "export": [0, 2, 3], "format": [0, 2, 3], "export_replac": 0, "n": [0, 2, 3], "_": 0, "": [0, 1, 2], "br": 0, "dict": [0, 1, 2], "replac": 0, "charact": 0, "titl": [0, 2, 3], "filenam": [0, 2], "dictionari": [0, 1], "regex": 0, "pattern": [0, 1], "kei": 0, "valu": [0, 1, 2], "pty_config": 0, "displaymodebar": 0, "displaylogo": 0, "layout": 0, "see": 0, "http": [0, 2, 3], "com": [0, 3], "python": [0, 1], "refer": [0, 3], "pty_line_styl": 0, "solid": [0, 2], "dash": [0, 2], "dashdot": [0, 2], "map": [0, 2], "style": [0, 2], "mpl_line_styl": 0, "pty_mark": 0, "circl": [0, 2], "squar": 0, "d": [0, 1], "diamond": 0, "p": [0, 2], "cross": 0, "triangl": 0, "up": [0, 1], "v": 0, "down": 0, "left": [0, 2], "right": [0, 2], "ne": 0, "se": 0, "sw": 0, "nw": 0, "pentagon": 0, "h": 0, "hexagon": 0, "hexagon2": 0, "8": [0, 2, 3], "octagon": 0, "star": 0, "hexagram": 0, "tall": 0, "wide": 0, "hourglass": 0, "bowti": 0, "thin": 0, "asterisk": 0, "hash": 0, "2": [0, 1, 2, 3], "1": [0, 1, 2, 3], "3": [0, 1, 3], "4": [0, 1, 2, 3], "ew": 0, "6": [0, 2, 3], "arrow": 0, "7": [0, 2, 3], "5": [0, 1, 2, 3], "bar": [0, 2, 3], "marker": [0, 2], "pty_markers_list": 0, "possibl": [0, 2], "mpl_marker": 0, "object": [1, 2], "unpack_nozip": 1, "strict": 1, "over": [1, 2], "sever": 1, "parallel": 1, "produc": 1, "an": [1, 2], "item": 1, "from": [1, 2, 3], "each": [1, 2], "one": [1, 2], "like": [1, 2], "builtin": 1, "zip": 1, "function": [1, 2, 3], "argument": [1, 2], "except": 1, "string": [1, 2], "default": [1, 2, 3], "overrid": [1, 2], "conf": [1, 2, 3], "non_iterable_typ": 1, "chang": 1, "To": [1, 2], "need": 1, "have": 1, "__iter__": 1, "attribut": 1, "otherwis": [1, 2], "pai": 1, "attent": [1, 2], "paramet": [1, 2, 3], "onli": [1, 2], "work": 1, "10": [1, 2, 3], "ignor": [1, 2], "rais": 1, "warn": 1, "unless": 1, "mute_strict_zip_warn": 1, "set": [1, 2], "alwai": [1, 2], "error": [1, 2], "sinc": 1, "endless": 1, "misc": 1, "element": [1, 2], "unpack": 1, "nozip": 1, "wrap": 1, "fail": 1, "ar": [1, 2, 3], "same": [1, 2, 3], "length": 1, "Not": [1, 2], "support": [1, 2, 3], "return": [1, 2], "you": 1, "would": 1, "b": 1, "c": 1, "e": [1, 2, 3], "A": 1, "notic": 1, "extra": 1, "won": 1, "t": 1, "print": [1, 2], "arg": [1, 2], "ani": [1, 2], "arbitrati": 1, "instead": [1, 2], "protect": 1, "which": [1, 2], "no_iter_typ": 1, "recurs": [1, 2], "prevent": [1, 2], "certain": 1, "being": 1, "fix": [1, 2, 3], "so": [1, 2], "thei": 1, "By": [1, 2], "contain": [1, 2, 3], "float": [1, 2], "datetim": 1, "option": [1, 2], "found": [1, 3], "indic": [1, 2], "should": [1, 2], "either": [1, 2], "inp": 1, "iterable_typ": 1, "depth": 1, "custom_digest": 1, "add": [1, 2], "all": [1, 2, 3], "nest": [1, 2], "structur": 1, "ad": 1, "In": [1, 2], "If": [1, 2], "itself": 1, "hand": 1, "directli": [1, 3], "without": 1, "np": [1, 2, 3], "ndarrai": 1, "panda": [1, 2], "seri": [1, 2], "maximum": 1, "infinit": 1, "must": [1, 2, 3], "follow": 1, "lambda": 1, "digest": [1, 2], "The": [1, 3], "result": 1, "treat": 1, "new": [1, 2], "sum": [1, 2], "class": [1, 3], "avoid": 1, "method": [1, 2, 3], "releas": 1, "origin": 1, "variabl": [1, 2], "creat": [2, 3], "hybrid": 2, "few": [2, 3], "code": [2, 3], "It": [2, 3], "combin": [2, 3], "best": [2, 3], "world": [2, 3], "unifi": [2, 3], "flat": [2, 3], "necessari": [2, 3], "boilerpl": [2, 3], "modul": [2, 3], "current": [2, 3], "scatter": [2, 3], "fill": [2, 3], "histogram": [2, 3], "heatmap": [2, 3], "boxplot": [2, 3], "linear": [2, 3], "regress": [2, 3], "text": [2, 3], "annot": [2, 3], "2d": [2, 3], "subplot": [2, 3], "row": [2, 3], "col": [2, 3], "xlabel": [2, 3], "ylabel": [2, 3], "xlim": 2, "ylim": 2, "shared_xax": 2, "shared_yax": [2, 3], "column_width": 2, "row_height": 2, "fig_siz": [2, 3], "legend_loc": 2, "legend_titl": 2, "save_fig": [2, 3], "save_format": [2, 3], "save_config": 2, "pty_update_layout": 2, "pty_custom_func": 2, "mpl_custom_func": 2, "base": 2, "displai": 2, "grid": 2, "column": 2, "axi": 2, "label": [2, 3], "entir": 2, "number": 2, "rang": [2, 3], "limit": 2, "case": 2, "multipl": 2, "provid": 2, "defin": 2, "how": 2, "share": 2, "ax": 2, "ratio": 2, "width": 2, "height": 2, "dimens": 2, "Will": 2, "normalis": 2, "2x": 2, "pixel": 2, "mpl": 2, "plt": 2, "respons": 2, "auto": [2, 3], "detect": 2, "ha": 2, "its": 2, "own": 2, "legend": 2, "shape": 2, "mai": 2, "pty": 2, "just": 2, "pathlib": 2, "path": [2, 3], "jpg": 2, "svg": 2, "html": [2, 3], "save": [2, 3], "call": 2, "instanc": [2, 3], "post_process": [2, 3], "directori": 2, "local": 2, "automat": [2, 3], "iter": [2, 3], "command": 2, "repeat": [2, 3], "declar": 2, "pass": 2, "keyword": [2, 3], "fig": [2, 3], "update_layout": 2, "thu": 2, "take": 2, "full": [2, 3], "control": [2, 3], "further": 2, "graph": 2, "accept": [2, 3], "def": [2, 3], "do_stuff": 2, "note": 2, "coordin": 2, "even": 2, "1x1": 2, "do_mor": 2, "everyth": [2, 3], "under": [2, 3], "800": [2, 3], "add_hist": [2, 3], "random": [2, 3], "normal": [2, 3], "1000": [2, 3], "add_boxplot": [2, 3], "20": [2, 3], "40": [2, 3], "60": [2, 3], "show": [2, 3], "static": [2, 3], "init": 2, "kwarg": 2, "initi": [2, 3], "alreadi": 2, "__init__": 2, "get_cycle_color": 2, "increment": 2, "retriev": 2, "next": 2, "time": 2, "jump": 2, "get": 2, "index": [2, 3], "regular": 2, "hex": 2, "lead": 2, "hashtag": 2, "digest_color": 2, "alpha": 2, "pars": 2, "rgba": 2, "arrai": 2, "g": 2, "blue": 2, "0000ff": 2, "c3": 2, "pick": 2, "opac": 2, "input": 2, "cycler": 2, "increas": 2, "digest_mark": 2, "mode": [2, 3], "pty_marker_kwarg": 2, "given": 2, "integ": 2, "convert": 2, "correspond": 2, "determin": 2, "drawn": 2, "request": [2, 3], "add_lin": [2, 3], "x_error": 2, "y_error": 2, "line_styl": 2, "marker_s": 2, "marker_line_width": 2, "marker_line_color": 2, "show_legend": 2, "linewidth": 2, "_serial_i": 2, "_serial_n": 2, "kwargs_pti": 2, "kwargs_mpl": 2, "draw": [2, 3], "assum": 2, "taken": 2, "els": 2, "datafram": 2, "loop": 2, "start": 2, "numpi": 2, "errorbar": 2, "scalar": 2, "symmetr": 2, "data": [2, 3], "point": 2, "separ": 2, "first": 2, "lower": 2, "second": 2, "upper": 2, "No": 2, "depend": 2, "add_scatt": 2, "add_linescatt": 2, "alias": 2, "can": [2, 3], "rgb": 2, "name": 2, "understood": 2, "trace": 2, "whether": 2, "shown": 2, "access": 2, "c0": 2, "c1": 2, "fallback": 2, "addit": 2, "specif": 2, "linescatt": [2, 3], "red": [2, 3], "abscissa": [2, 3], "ordin": [2, 3], "add_bar": 2, "horizont": 2, "line_width": 2, "line_color": 2, "regardless": 2, "vertic": 2, "both": 2, "posit": 2, "orient": 2, "rel": 2, "outlin": 2, "specifi": 2, "bin": 2, "densiti": 2, "undefin": 2, "hist": [2, 3], "color_median": 2, "black": 2, "notch": 2, "sequenc": 2, "vector": 2, "build": [2, 3], "median": 2, "add_heatmap": 2, "lim": 2, "aspect": 2, "invert_x": 2, "invert_i": 2, "cmap": 2, "rainbow": 2, "cmap_und": 2, "cmap_ov": 2, "cmap_bad": 2, "invert": 2, "direct": 2, "org": [2, 3], "stabl": 2, "galleri": 2, "colormap_refer": 2, "avail": 2, "librari": 2, "differ": [2, 3], "slightli": 2, "invalid": 2, "nan": 2, "add_regress": 2, "05": 2, "linspac": 2, "101": 2, "arraytool": 2, "linearregress": 2, "pre": 2, "exist": 2, "omit": 2, "model": 2, "confic": 2, "predict": 2, "interv": 2, "add_fil": [2, 3], "y1": 2, "y2": 2, "line_opac": 2, "between": [2, 3], "two": 2, "boundari": 2, "add_text": 2, "horizontal_align": 2, "center": 2, "vertical_align": 2, "text_align": 2, "data_coord": 2, "x_data_coord": 2, "y_data_coord": 2, "where": 2, "box": 2, "anchor": 2, "top": 2, "bottom": 2, "align": 2, "insid": 2, "relat": 2, "finish": 2, "print_confirm": 2, "file": [2, 3], "confirm": 2, "messag": 2, "been": 2, "_repr_mimebundle_": 2, "forward": 2, "self": 2, "_repr_html_": 2, "js_render_warn": 2, "skip_post_process": 2, "doc_decor": 2, "wrapper": 2, "your": [2, 3], "feed": 2, "give": 2, "append": 2, "docstr": 2, "global": 2, "_docstring_decor": 2, "plot_lin": [2, 3], "sampl": [2, 3], "sigma": [2, 3], "mu": [2, 3], "gaussian": [2, 3], "nois": [2, 3], "200": [2, 3], "distribut": [2, 3], "magic_plot_preset": [2, 3], "kwargs_preset": 2, "preconfigur": 2, "preset": [2, 3], "strict_preset": 2, "view": [2, 3], "jupyt": 2, "notebook": [2, 3], "child": 2, "default_sel": 2, "default_isel": 2, "xarrai": 2, "dataarrai": 2, "more": [2, 3], "than": 2, "sel": 2, "isel": 2, "selector": 2, "reduc": 2, "select": 2, "subset": 2, "slice": 2, "count": 2, "default_var": 2, "dataset": 2, "var": 2, "document": 3, "api": 3, "core": 3, "plot": 3, "One": 3, "magic_plot": 3, "decor": 3, "notebookinteract": 3, "parent": 3, "showdataarrai": 3, "showdataset": 3, "configur": 3, "tool": 3, "zip_smart": 3, "filter_nozip": 3, "sum_nest": 3, "deprec": 3, "chart": 3, "switch": 3, "singl": 3, "translat": 3, "block": 3, "plot_my_data": 3, "import": 3, "process": 3, "obtain": 3, "my": 3, "999": 3, "record": 3, "plot_my_data_preconfigur": 3, "janjo": 3, "ch": 3, "nbviewer": 3, "github": 3, "janjoch": 3, "tree": 3, "main": 3, "sourc": 3, "pypi": 3, "project": 3, "try": 3, "binder": 3, "pip": 3, "git": 3, "clone": 3, "cd": 3, "idea": 3, "bug": 3, "report": 3, "featur": 3, "submiss": 3, "veri": 3, "welcom": 3, "pleas": 3, "write": 3, "duck": 3, "pull": 3, "search": 3}, "objects": {"interplot": [[1, 0, 1, "", "NoZip"], [2, 0, 1, "", "NotebookInteraction"], [2, 0, 1, "", "Plot"], [2, 0, 1, "", "ShowDataArray"], [2, 0, 1, "", "ShowDataset"], [2, 3, 1, "", "bar"], [2, 3, 1, "", "boxplot"], [0, 4, 0, "-", "conf"], [2, 3, 1, "", "fill"], [1, 3, 1, "", "filter_nozip"], [2, 3, 1, "", "heatmap"], [2, 3, 1, "", "hist"], [1, 4, 0, "-", "iter"], [2, 3, 1, "", "line"], [2, 3, 1, "", "linescatter"], [2, 3, 1, "", "magic_plot"], [2, 3, 1, "", "magic_plot_preset"], [2, 4, 0, "-", "plot"], [2, 3, 1, "", "regression"], [1, 3, 1, "", "repeat"], [2, 3, 1, "", "scatter"], [1, 3, 1, "", "sum_nested"], [2, 3, 1, "", "text"], [1, 3, 1, "", "zip_smart"]], "interplot.NoZip": [[1, 1, 1, "", "release"]], "interplot.NotebookInteraction": [[2, 2, 1, "", "JS_RENDER_WARNING"], [2, 1, 1, "", "_repr_html_"], [2, 1, 1, "", "_repr_mimebundle_"]], "interplot.Plot": [[2, 2, 1, "", "JS_RENDER_WARNING"], [2, 1, 1, "", "_repr_html_"], [2, 1, 1, "", "_repr_mimebundle_"], [2, 1, 1, "", "add_bar"], [2, 1, 1, "", "add_boxplot"], [2, 1, 1, "", "add_fill"], [2, 1, 1, "", "add_heatmap"], [2, 1, 1, "", "add_hist"], [2, 1, 1, "", "add_line"], [2, 1, 1, "", "add_linescatter"], [2, 1, 1, "", "add_regression"], [2, 1, 1, "", "add_scatter"], [2, 1, 1, "", "add_text"], [2, 1, 1, "", "digest_color"], [2, 1, 1, "", "digest_marker"], [2, 1, 1, "", "get_cycle_color"], [2, 1, 1, "", "init"], [2, 1, 1, "", "post_process"], [2, 1, 1, "", "save"], [2, 1, 1, "", "show"]], "interplot.ShowDataArray": [[2, 2, 1, "", "JS_RENDER_WARNING"], [2, 1, 1, "", "_repr_html_"], [2, 1, 1, "", "_repr_mimebundle_"], [2, 1, 1, "", "plot"]], "interplot.ShowDataset": [[2, 2, 1, "", "JS_RENDER_WARNING"], [2, 1, 1, "", "_repr_html_"], [2, 1, 1, "", "_repr_mimebundle_"], [2, 1, 1, "", "plot"]], "interplot.conf": [[0, 5, 1, "", "COLOR_CYCLE"], [0, 5, 1, "", "DPI"], [0, 5, 1, "", "EXPORT_FORMAT"], [0, 5, 1, "", "EXPORT_REPLACE"], [0, 5, 1, "", "INTERACTIVE"], [0, 5, 1, "", "MPL_FIG_SIZE"], [0, 5, 1, "", "MPL_LINE_STYLES"], [0, 5, 1, "", "MPL_MARKERS"], [0, 5, 1, "", "PTY_CONFIG"], [0, 5, 1, "", "PTY_FIG_SIZE"], [0, 5, 1, "", "PTY_LINE_STYLES"], [0, 5, 1, "", "PTY_MARKERS"], [0, 5, 1, "", "PTY_MARKERS_LIST"]]}, "objtypes": {"0": "py:class", "1": "py:method", "2": "py:attribute", "3": "py:function", "4": "py:module", "5": "py:data"}, "objnames": {"0": ["py", "class", "Python class"], "1": ["py", "method", "Python method"], "2": ["py", "attribute", "Python attribute"], "3": ["py", "function", "Python function"], "4": ["py", "module", "Python module"], "5": ["py", "data", "Python data"]}, "titleterms": {"interplot": [0, 1, 2, 3], "conf": 0, "modul": 0, "default": 0, "configur": 0, "On": [0, 1, 2, 3], "thi": [0, 1, 2, 3], "page": [0, 1, 2, 3], "variabl": 0, "iter": 1, "tool": 1, "document": [1, 2], "api": [1, 2], "refer": [1, 2], "zip_smart": 1, "repeat": 1, "filter_nozip": 1, "sum_nest": 1, "deprec": 1, "The": 2, "core": 2, "plot": 2, "class": 2, "One": 2, "line": 2, "magic_plot": 2, "decor": 2, "notebookinteract": 2, "parent": 2, "showdataarrai": 2, "showdataset": 2, "quick": 3, "start": 3, "resourc": 3, "licenc": 3, "demo": 3, "instal": 3, "dev": 3, "contribut": 3, "exampl": 3, "indic": 3, "tabl": 3}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx": 58}, "alltitles": {"interplot.conf: Module Defaults Configuration": [[0, "interplot-conf-module-defaults-configuration"]], "On this page": [[0, "on-this-page"], [1, "on-this-page"], [2, "on-this-page"], [3, "on-this-page"]], "VARIABLES": [[0, "variables"]], "interplot.iter: Iteration Tools Documentation and API reference": [[1, "interplot-iter-iteration-tools-documentation-and-api-reference"]], "zip_smart": [[1, "zip-smart"]], "repeat": [[1, "repeat"]], "filter_nozip": [[1, "filter-nozip"]], "sum_nested": [[1, "sum-nested"]], "Deprecated": [[1, "deprecated"]], "interplot Documentation and API reference": [[2, "interplot-documentation-and-api-reference"]], "The core Plot class": [[2, "the-core-plot-class"]], "One-line Plotting": [[2, "one-line-plotting"]], "magic_plot decorators": [[2, "magic-plot-decorators"]], "NotebookInteraction Parent Class": [[2, "notebookinteraction-parent-class"]], "ShowDataArray, ShowDataset": [[2, "showdataarray-showdataset"]], "interplot": [[3, "interplot"], [3, "id1"]], "Quick Start": [[3, "quick-start"]], "Resources": [[3, "resources"]], "Licence": [[3, "licence"]], "Demo": [[3, "demo"]], "Install": [[3, "install"]], "dev installation": [[3, "dev-installation"]], "Contribute": [[3, "contribute"]], "Examples": [[3, "examples"]], "Indices and tables": [[3, "indices-and-tables"]]}, "indexentries": {"color_cycle (in module interplot.conf)": [[0, "interplot.conf.COLOR_CYCLE"]], "dpi (in module interplot.conf)": [[0, "interplot.conf.DPI"]], "export_format (in module interplot.conf)": [[0, "interplot.conf.EXPORT_FORMAT"]], "export_replace (in module interplot.conf)": [[0, "interplot.conf.EXPORT_REPLACE"]], "interactive (in module interplot.conf)": [[0, "interplot.conf.INTERACTIVE"]], "mpl_fig_size (in module interplot.conf)": [[0, "interplot.conf.MPL_FIG_SIZE"]], "mpl_line_styles (in module interplot.conf)": [[0, "interplot.conf.MPL_LINE_STYLES"]], "mpl_markers (in module interplot.conf)": [[0, "interplot.conf.MPL_MARKERS"]], "pty_config (in module interplot.conf)": [[0, "interplot.conf.PTY_CONFIG"]], "pty_fig_size (in module interplot.conf)": [[0, "interplot.conf.PTY_FIG_SIZE"]], "pty_line_styles (in module interplot.conf)": [[0, "interplot.conf.PTY_LINE_STYLES"]], "pty_markers (in module interplot.conf)": [[0, "interplot.conf.PTY_MARKERS"]], "pty_markers_list (in module interplot.conf)": [[0, "interplot.conf.PTY_MARKERS_LIST"]], "interplot.conf": [[0, "module-interplot.conf"]], "module": [[0, "module-interplot.conf"], [1, "module-interplot.iter"], [2, "module-interplot.plot"]], "nozip (class in interplot)": [[1, "interplot.NoZip"]], "filter_nozip() (in module interplot)": [[1, "interplot.filter_nozip"]], "interplot.iter": [[1, "module-interplot.iter"]], "release() (interplot.nozip method)": [[1, "interplot.NoZip.release"]], "repeat() (in module interplot)": [[1, "interplot.repeat"]], "sum_nested() (in module interplot)": [[1, "interplot.sum_nested"]], "zip_smart() (in module interplot)": [[1, "interplot.zip_smart"]], "js_render_warning (interplot.notebookinteraction attribute)": [[2, "interplot.NotebookInteraction.JS_RENDER_WARNING"]], "js_render_warning (interplot.plot attribute)": [[2, "interplot.Plot.JS_RENDER_WARNING"]], "js_render_warning (interplot.showdataarray attribute)": [[2, "interplot.ShowDataArray.JS_RENDER_WARNING"]], "js_render_warning (interplot.showdataset attribute)": [[2, "interplot.ShowDataset.JS_RENDER_WARNING"]], "notebookinteraction (class in interplot)": [[2, "interplot.NotebookInteraction"]], "plot (class in interplot)": [[2, "interplot.Plot"]], "showdataarray (class in interplot)": [[2, "interplot.ShowDataArray"]], "showdataset (class in interplot)": [[2, "interplot.ShowDataset"]], "_repr_html_() (interplot.notebookinteraction method)": [[2, "interplot.NotebookInteraction._repr_html_"]], "_repr_html_() (interplot.plot method)": [[2, "interplot.Plot._repr_html_"]], "_repr_html_() (interplot.showdataarray method)": [[2, "interplot.ShowDataArray._repr_html_"]], "_repr_html_() (interplot.showdataset method)": [[2, "interplot.ShowDataset._repr_html_"]], "_repr_mimebundle_() (interplot.notebookinteraction method)": [[2, "interplot.NotebookInteraction._repr_mimebundle_"]], "_repr_mimebundle_() (interplot.plot method)": [[2, "interplot.Plot._repr_mimebundle_"]], "_repr_mimebundle_() (interplot.showdataarray method)": [[2, "interplot.ShowDataArray._repr_mimebundle_"]], "_repr_mimebundle_() (interplot.showdataset method)": [[2, "interplot.ShowDataset._repr_mimebundle_"]], "add_bar() (interplot.plot method)": [[2, "interplot.Plot.add_bar"]], "add_boxplot() (interplot.plot method)": [[2, "interplot.Plot.add_boxplot"]], "add_fill() (interplot.plot method)": [[2, "interplot.Plot.add_fill"]], "add_heatmap() (interplot.plot method)": [[2, "interplot.Plot.add_heatmap"]], "add_hist() (interplot.plot method)": [[2, "interplot.Plot.add_hist"]], "add_line() (interplot.plot method)": [[2, "interplot.Plot.add_line"]], "add_linescatter() (interplot.plot method)": [[2, "interplot.Plot.add_linescatter"]], "add_regression() (interplot.plot method)": [[2, "interplot.Plot.add_regression"]], "add_scatter() (interplot.plot method)": [[2, "interplot.Plot.add_scatter"]], "add_text() (interplot.plot method)": [[2, "interplot.Plot.add_text"]], "bar() (in module interplot)": [[2, "interplot.bar"]], "boxplot() (in module interplot)": [[2, "interplot.boxplot"]], "digest_color() (interplot.plot method)": [[2, "interplot.Plot.digest_color"]], "digest_marker() (interplot.plot static method)": [[2, "interplot.Plot.digest_marker"]], "fill() (in module interplot)": [[2, "interplot.fill"]], "get_cycle_color() (interplot.plot method)": [[2, "interplot.Plot.get_cycle_color"]], "heatmap() (in module interplot)": [[2, "interplot.heatmap"]], "hist() (in module interplot)": [[2, "interplot.hist"]], "init() (interplot.plot static method)": [[2, "interplot.Plot.init"]], "interplot.plot": [[2, "module-interplot.plot"]], "line() (in module interplot)": [[2, "interplot.line"]], "linescatter() (in module interplot)": [[2, "interplot.linescatter"]], "magic_plot() (in module interplot)": [[2, "interplot.magic_plot"]], "magic_plot_preset() (in module interplot)": [[2, "interplot.magic_plot_preset"]], "plot() (interplot.showdataarray method)": [[2, "interplot.ShowDataArray.plot"]], "plot() (interplot.showdataset method)": [[2, "interplot.ShowDataset.plot"]], "post_process() (interplot.plot method)": [[2, "interplot.Plot.post_process"]], "regression() (in module interplot)": [[2, "interplot.regression"]], "save() (interplot.plot method)": [[2, "interplot.Plot.save"]], "scatter() (in module interplot)": [[2, "interplot.scatter"]], "show() (interplot.plot method)": [[2, "interplot.Plot.show"]], "text() (in module interplot)": [[2, "interplot.text"]]}})
\ No newline at end of file
+Search.setIndex({"docnames": ["api_conf", "api_iter", "api_plot", "index"], "filenames": ["api_conf.rst", "api_iter.rst", "api_plot.rst", "index.rst"], "titles": ["<cite>interplot.conf</cite>: Module Defaults Configuration", "<cite>interplot.iter</cite>: Iteration Tools Documentation and API reference", "<cite>interplot</cite> Documentation and API reference", "interplot"], "terms": {"modifi": [0, 2], "behavior": [0, 1, 2], "packag": [0, 3], "exampl": [0, 1, 2], "line": [0, 3], "x": [0, 2, 3], "y": [0, 2, 3], "interact": [0, 2, 3], "fals": [0, 1, 2, 3], "color_cycl": [0, 2], "0": [0, 2, 3], "000000": 0, "dpi": [0, 2], "150": 0, "mpl_fig_siz": [0, 2], "400": 0, "user": [0, 2], "true": [0, 1, 2, 3], "type": [0, 1, 2], "bool": [0, 1, 2], "gener": [0, 1, 2], "plotli": [0, 2, 3], "figur": [0, 2, 3], "006ba4": 0, "ff800e": 0, "ababab": 0, "595959": 0, "5f9ed1": 0, "c85200": 0, "898989": 0, "a2c8ec": 0, "ffbc79": 0, "cfcfcf": 0, "list": [0, 1, 2], "color": [0, 2, 3], "cycl": [0, 2, 3], "through": [0, 2, 3], "i": [0, 1, 2, 3], "optimis": 0, "vision": 0, "defici": 0, "100": [0, 2, 3], "int": [0, 1, 2], "resolut": [0, 2], "dot": [0, 2], "per": 0, "inch": 0, "pty_fig_s": [0, 2], "none": [0, 1, 2, 3], "500": [0, 2, 3], "tupl": [0, 1, 2], "size": [0, 2], "backend": 0, "px": 0, "us": [0, 1, 2, 3], "adapt": [0, 2], "700": 0, "450": 0, "matplotlib": [0, 2, 3], "export_format": [0, 2], "png": [0, 2, 3], "str": [0, 2], "export": [0, 2, 3], "format": [0, 2, 3], "export_replac": 0, "n": [0, 2, 3], "_": 0, "": [0, 1, 2], "br": 0, "dict": [0, 1, 2], "replac": 0, "charact": 0, "titl": [0, 2, 3], "filenam": [0, 2], "dictionari": [0, 1], "regex": 0, "pattern": [0, 1], "kei": 0, "valu": [0, 1, 2], "pty_config": 0, "displaymodebar": 0, "displaylogo": 0, "layout": 0, "see": 0, "http": [0, 2, 3], "com": [0, 3], "python": [0, 1], "refer": [0, 3], "pty_line_styl": 0, "solid": [0, 2], "dash": [0, 2], "dashdot": [0, 2], "map": [0, 2], "style": [0, 2], "mpl_line_styl": 0, "pty_mark": 0, "circl": [0, 2], "squar": 0, "d": [0, 1], "diamond": 0, "p": [0, 2], "cross": 0, "triangl": 0, "up": [0, 1], "v": 0, "down": 0, "left": [0, 2], "right": [0, 2], "ne": 0, "se": 0, "sw": 0, "nw": 0, "pentagon": 0, "h": 0, "hexagon": 0, "hexagon2": 0, "8": [0, 2, 3], "octagon": 0, "star": 0, "hexagram": 0, "tall": 0, "wide": 0, "hourglass": 0, "bowti": 0, "thin": 0, "asterisk": 0, "hash": 0, "2": [0, 1, 2, 3], "1": [0, 1, 2, 3], "3": [0, 1, 3], "4": [0, 1, 2, 3], "ew": 0, "6": [0, 2, 3], "arrow": 0, "7": [0, 2, 3], "5": [0, 1, 2, 3], "bar": [0, 2, 3], "marker": [0, 2], "pty_markers_list": 0, "possibl": [0, 2], "mpl_marker": 0, "object": [0, 1, 2], "unpack_nozip": 1, "strict": 1, "over": [1, 2], "sever": 1, "parallel": 1, "produc": 1, "an": [1, 2], "item": 1, "from": [1, 2, 3], "each": [1, 2], "one": [1, 2], "like": [1, 2], "builtin": 1, "zip": 1, "function": [0, 1, 3], "argument": [1, 2], "except": 1, "string": [1, 2], "default": [1, 2, 3], "overrid": [0, 1, 2], "conf": [1, 2, 3], "non_iterable_typ": 1, "chang": 1, "To": [1, 2], "need": [0, 1], "have": 1, "__iter__": 1, "attribut": 1, "otherwis": [1, 2], "pai": 1, "attent": [1, 2], "paramet": [0, 1, 2, 3], "onli": [1, 2], "work": 1, "10": [1, 2, 3], "ignor": [1, 2], "rais": 1, "warn": 1, "unless": 1, "mute_strict_zip_warn": 1, "set": [1, 2], "alwai": [1, 2], "error": [1, 2], "sinc": 1, "endless": 1, "misc": 1, "element": [1, 2], "unpack": 1, "nozip": 1, "wrap": 1, "fail": 1, "ar": [1, 2, 3], "same": [1, 2, 3], "length": 1, "Not": [1, 2], "support": [1, 2, 3], "return": [0, 1, 2], "you": 1, "would": 1, "b": 1, "c": 1, "e": [1, 2, 3], "A": 1, "notic": 1, "extra": 1, "won": 1, "t": 1, "print": [1, 2], "arg": [1, 2], "ani": [1, 2], "arbitrati": 1, "instead": [1, 2], "protect": 1, "which": [1, 2], "no_iter_typ": 1, "recurs": [1, 2], "prevent": [1, 2], "certain": 1, "being": 1, "fix": [1, 2, 3], "so": [1, 2], "thei": 1, "By": [1, 2], "contain": [1, 2, 3], "float": [1, 2], "datetim": 1, "option": [1, 2], "found": [1, 3], "indic": [1, 2], "should": [1, 2], "either": [1, 2], "inp": 1, "iterable_typ": 1, "depth": 1, "custom_digest": 1, "add": [1, 2], "all": [1, 2, 3], "nest": [1, 2], "structur": 1, "ad": 1, "In": [1, 2], "If": [1, 2], "itself": 1, "hand": 1, "directli": [1, 3], "without": 1, "np": [1, 2, 3], "ndarrai": 1, "panda": [1, 2], "seri": [1, 2], "maximum": 1, "infinit": 1, "must": [1, 2, 3], "follow": 1, "lambda": 1, "digest": [1, 2], "The": [0, 1, 3], "result": 1, "treat": 1, "new": [1, 2], "sum": [1, 2], "class": [1, 3], "avoid": 1, "method": [1, 2, 3], "releas": 1, "origin": 1, "variabl": [1, 2], "creat": [2, 3], "hybrid": 2, "few": [2, 3], "code": [2, 3], "It": [2, 3], "combin": [2, 3], "best": [2, 3], "world": [2, 3], "unifi": [2, 3], "flat": [2, 3], "necessari": [2, 3], "boilerpl": [2, 3], "modul": [2, 3], "current": [2, 3], "scatter": [2, 3], "fill": [2, 3], "histogram": [2, 3], "heatmap": [2, 3], "boxplot": [2, 3], "linear": [2, 3], "regress": [2, 3], "text": [2, 3], "annot": [2, 3], "2d": [2, 3], "subplot": [2, 3], "row": [2, 3], "col": [2, 3], "xlabel": [2, 3], "ylabel": [2, 3], "xlim": 2, "ylim": 2, "shared_xax": 2, "shared_yax": [2, 3], "column_width": 2, "row_height": 2, "fig_siz": [2, 3], "legend_loc": 2, "legend_titl": 2, "save_fig": [2, 3], "save_format": [2, 3], "save_config": 2, "pty_update_layout": 2, "pty_custom_func": [0, 2], "mpl_custom_func": [0, 2], "base": 2, "displai": 2, "grid": 2, "column": 2, "axi": 2, "label": [2, 3], "entir": 2, "number": 2, "rang": [2, 3], "limit": 2, "case": 2, "multipl": 2, "provid": 2, "defin": 2, "how": 2, "share": 2, "ax": [0, 2], "ratio": 2, "width": 2, "height": 2, "dimens": 2, "Will": [0, 2], "normalis": 2, "2x": 2, "pixel": 2, "mpl": 2, "plt": [], "respons": 2, "auto": [2, 3], "detect": 2, "ha": 2, "its": 2, "own": 2, "legend": 2, "shape": 2, "mai": 2, "pty": 2, "just": 2, "pathlib": 2, "path": [2, 3], "jpg": 2, "svg": 2, "html": [2, 3], "save": [2, 3], "call": [0, 2], "instanc": [0, 2, 3], "post_process": [0, 2, 3], "directori": 2, "local": 2, "automat": [2, 3], "iter": [2, 3], "command": 2, "repeat": [2, 3], "declar": 2, "pass": 2, "keyword": [2, 3], "fig": [0, 2, 3], "update_layout": 2, "thu": [], "take": [], "full": 3, "control": [2, 3], "further": 2, "graph": 2, "accept": [0, 2, 3], "def": [0, 2, 3], "do_stuff": [0, 2], "note": 2, "coordin": 2, "even": 2, "1x1": 2, "do_mor": [0, 2], "everyth": [2, 3], "under": [2, 3], "800": [2, 3], "add_hist": [2, 3], "random": [2, 3], "normal": [2, 3], "1000": [2, 3], "add_boxplot": [2, 3], "20": [2, 3], "40": [2, 3], "60": [2, 3], "show": [0, 2, 3], "static": [2, 3], "init": 2, "kwarg": 2, "initi": [2, 3], "alreadi": 2, "__init__": 2, "get_cycle_color": 2, "increment": 2, "retriev": 2, "next": 2, "time": 2, "jump": 2, "get": 2, "index": [2, 3], "regular": 2, "hex": 2, "lead": 2, "hashtag": 2, "digest_color": 2, "alpha": 2, "pars": 2, "rgba": 2, "arrai": 2, "g": 2, "blue": 2, "0000ff": 2, "c3": 2, "pick": 2, "opac": 2, "input": 2, "cycler": 2, "increas": 2, "digest_mark": 2, "mode": [2, 3], "pty_marker_kwarg": 2, "given": 2, "integ": 2, "convert": 2, "correspond": 2, "determin": 2, "drawn": 2, "request": [2, 3], "add_lin": [0, 2, 3], "x_error": 2, "y_error": 2, "line_styl": 2, "marker_s": 2, "marker_line_width": 2, "marker_line_color": 2, "show_legend": 2, "linewidth": 2, "_serial_i": 2, "_serial_n": 2, "kwargs_pti": 2, "kwargs_mpl": 2, "draw": [2, 3], "assum": 2, "taken": 2, "els": 2, "datafram": 2, "loop": 2, "start": 2, "numpi": 2, "errorbar": 2, "scalar": 2, "symmetr": 2, "data": [2, 3], "point": 2, "separ": 2, "first": 2, "lower": 2, "second": 2, "upper": 2, "No": 2, "depend": 2, "add_scatt": 2, "add_linescatt": 2, "alias": 2, "can": [2, 3], "rgb": 2, "name": 2, "understood": 2, "trace": 2, "whether": 2, "shown": 2, "access": 2, "c0": 2, "c1": 2, "fallback": 2, "addit": 2, "specif": 2, "linescatt": [2, 3], "red": [2, 3], "abscissa": [2, 3], "ordin": [2, 3], "add_bar": 2, "horizont": 2, "line_width": 2, "line_color": 2, "regardless": 2, "vertic": 2, "both": 2, "posit": 2, "orient": 2, "rel": 2, "outlin": 2, "specifi": 2, "bin": 2, "densiti": 2, "undefin": 2, "hist": [2, 3], "color_median": 2, "black": 2, "notch": 2, "sequenc": 2, "vector": 2, "build": [2, 3], "median": 2, "add_heatmap": 2, "lim": 2, "aspect": 2, "invert_x": 2, "invert_i": 2, "cmap": 2, "rainbow": 2, "cmap_und": 2, "cmap_ov": 2, "cmap_bad": 2, "invert": 2, "direct": 2, "org": [2, 3], "stabl": 2, "galleri": 2, "colormap_refer": 2, "avail": 2, "librari": 2, "differ": [2, 3], "slightli": 2, "invalid": 2, "nan": 2, "add_regress": 2, "05": 2, "linspac": 2, "101": 2, "arraytool": 2, "linearregress": 2, "pre": 2, "exist": 2, "omit": 2, "model": 2, "confic": 2, "predict": 2, "interv": 2, "add_fil": [2, 3], "y1": 2, "y2": 2, "line_opac": 2, "between": [2, 3], "two": 2, "boundari": 2, "add_text": [0, 2], "horizontal_align": 2, "center": 2, "vertical_align": 2, "text_align": 2, "data_coord": 2, "x_data_coord": 2, "y_data_coord": 2, "where": 2, "box": 2, "anchor": 2, "top": 2, "bottom": 2, "align": 2, "insid": 2, "relat": 2, "finish": [0, 2], "print_confirm": 2, "file": [2, 3], "confirm": 2, "messag": 2, "been": 2, "_repr_mimebundle_": 2, "forward": 2, "self": 2, "_repr_html_": 2, "js_render_warn": 2, "skip_post_process": 2, "doc_decor": 2, "wrapper": 2, "your": [0, 2, 3], "feed": 2, "give": 2, "append": 2, "docstr": 2, "global": 2, "_docstring_decor": 2, "plot_lin": [2, 3], "sampl": [2, 3], "sigma": [2, 3], "mu": [2, 3], "gaussian": [2, 3], "nois": [2, 3], "200": [2, 3], "distribut": [2, 3], "magic_plot_preset": [2, 3], "kwargs_preset": 2, "preconfigur": 2, "preset": [2, 3], "strict_preset": 2, "view": [2, 3], "jupyt": 2, "notebook": [2, 3], "child": 2, "default_sel": 2, "default_isel": 2, "xarrai": 2, "dataarrai": 2, "more": [2, 3], "than": 2, "sel": 2, "isel": 2, "selector": 2, "reduc": 2, "select": 2, "subset": 2, "slice": 2, "count": 2, "default_var": 2, "dataset": 2, "var": 2, "document": 3, "api": 3, "core": 3, "plot": [0, 3], "One": 3, "magic_plot": 3, "decor": 3, "notebookinteract": 3, "parent": 3, "showdataarrai": 3, "showdataset": 3, "configur": 3, "tool": 3, "zip_smart": 3, "filter_nozip": 3, "sum_nest": 3, "deprec": 3, "chart": 3, "switch": 3, "singl": 3, "translat": 3, "block": 3, "plot_my_data": 3, "import": 3, "process": 3, "obtain": 3, "my": 3, "999": 3, "record": 3, "plot_my_data_preconfigur": 3, "janjo": 3, "ch": 3, "nbviewer": 3, "github": 3, "janjoch": 3, "tree": 3, "main": 3, "sourc": 3, "pypi": 3, "project": 3, "try": 3, "binder": 3, "pip": 3, "git": 3, "clone": 3, "cd": 3, "idea": 3, "bug": 3, "report": 3, "featur": 3, "submiss": 3, "veri": 3, "welcom": 3, "pleas": 3, "write": 3, "duck": 3, "pull": 3, "search": 3, "appli": 0, "custom": 0, "when": 0, "make": 0, "sure": 0, "graph_object": [0, 2], "m": 2, "allow": 2, "viewport": 2, "config": 2, "write_html": 2, "execut": [0, 2], "do": [0, 2], "customis": [0, 2], "also": [0, 2], "includ": [0, 2], "here": [0, 2], "close": 2, "non": 2, "pick_non_non": 2, "global_custom_func": [0, 2], "watermark": [0, 2], "grai": [0, 2], "There": 0, "helper": 3}, "objects": {"interplot": [[1, 0, 1, "", "NoZip"], [2, 0, 1, "", "NotebookInteraction"], [2, 0, 1, "", "Plot"], [2, 0, 1, "", "ShowDataArray"], [2, 0, 1, "", "ShowDataset"], [2, 3, 1, "", "bar"], [2, 3, 1, "", "boxplot"], [0, 4, 0, "-", "conf"], [2, 3, 1, "", "fill"], [1, 3, 1, "", "filter_nozip"], [2, 3, 1, "", "heatmap"], [2, 3, 1, "", "hist"], [1, 4, 0, "-", "iter"], [2, 3, 1, "", "line"], [2, 3, 1, "", "linescatter"], [2, 3, 1, "", "magic_plot"], [2, 3, 1, "", "magic_plot_preset"], [2, 3, 1, "", "pick_non_none"], [2, 4, 0, "-", "plot"], [2, 3, 1, "", "regression"], [1, 3, 1, "", "repeat"], [2, 3, 1, "", "scatter"], [1, 3, 1, "", "sum_nested"], [2, 3, 1, "", "text"], [1, 3, 1, "", "zip_smart"]], "interplot.NoZip": [[1, 1, 1, "", "release"]], "interplot.NotebookInteraction": [[2, 2, 1, "", "JS_RENDER_WARNING"], [2, 1, 1, "", "_repr_html_"], [2, 1, 1, "", "_repr_mimebundle_"]], "interplot.Plot": [[2, 2, 1, "", "JS_RENDER_WARNING"], [2, 1, 1, "", "_repr_html_"], [2, 1, 1, "", "_repr_mimebundle_"], [2, 1, 1, "", "add_bar"], [2, 1, 1, "", "add_boxplot"], [2, 1, 1, "", "add_fill"], [2, 1, 1, "", "add_heatmap"], [2, 1, 1, "", "add_hist"], [2, 1, 1, "", "add_line"], [2, 1, 1, "", "add_linescatter"], [2, 1, 1, "", "add_regression"], [2, 1, 1, "", "add_scatter"], [2, 1, 1, "", "add_text"], [2, 1, 1, "", "close"], [2, 1, 1, "", "digest_color"], [2, 1, 1, "", "digest_marker"], [2, 1, 1, "", "get_cycle_color"], [2, 1, 1, "", "init"], [2, 1, 1, "", "post_process"], [2, 1, 1, "", "save"], [2, 1, 1, "", "show"]], "interplot.ShowDataArray": [[2, 2, 1, "", "JS_RENDER_WARNING"], [2, 1, 1, "", "_repr_html_"], [2, 1, 1, "", "_repr_mimebundle_"], [2, 1, 1, "", "plot"]], "interplot.ShowDataset": [[2, 2, 1, "", "JS_RENDER_WARNING"], [2, 1, 1, "", "_repr_html_"], [2, 1, 1, "", "_repr_mimebundle_"], [2, 1, 1, "", "plot"]], "interplot.conf": [[0, 5, 1, "", "COLOR_CYCLE"], [0, 5, 1, "", "DPI"], [0, 5, 1, "", "EXPORT_FORMAT"], [0, 5, 1, "", "EXPORT_REPLACE"], [0, 3, 1, "", "GLOBAL_CUSTOM_FUNC"], [0, 5, 1, "", "INTERACTIVE"], [0, 3, 1, "", "MPL_CUSTOM_FUNC"], [0, 5, 1, "", "MPL_FIG_SIZE"], [0, 5, 1, "", "MPL_LINE_STYLES"], [0, 5, 1, "", "MPL_MARKERS"], [0, 5, 1, "", "PTY_CONFIG"], [0, 3, 1, "", "PTY_CUSTOM_FUNC"], [0, 5, 1, "", "PTY_FIG_SIZE"], [0, 5, 1, "", "PTY_LINE_STYLES"], [0, 5, 1, "", "PTY_MARKERS"], [0, 5, 1, "", "PTY_MARKERS_LIST"]]}, "objtypes": {"0": "py:class", "1": "py:method", "2": "py:attribute", "3": "py:function", "4": "py:module", "5": "py:data"}, "objnames": {"0": ["py", "class", "Python class"], "1": ["py", "method", "Python method"], "2": ["py", "attribute", "Python attribute"], "3": ["py", "function", "Python function"], "4": ["py", "module", "Python module"], "5": ["py", "data", "Python data"]}, "titleterms": {"interplot": [0, 1, 2, 3], "conf": 0, "modul": 0, "default": 0, "configur": 0, "On": [0, 1, 2, 3], "thi": [0, 1, 2, 3], "page": [0, 1, 2, 3], "variabl": 0, "iter": 1, "tool": 1, "document": [1, 2], "api": [1, 2], "refer": [1, 2], "zip_smart": 1, "repeat": 1, "filter_nozip": 1, "sum_nest": 1, "deprec": 1, "The": 2, "core": 2, "plot": 2, "class": 2, "One": 2, "line": 2, "magic_plot": 2, "decor": 2, "notebookinteract": 2, "parent": 2, "showdataarrai": 2, "showdataset": 2, "quick": 3, "start": 3, "resourc": 3, "licenc": 3, "demo": 3, "instal": 3, "dev": 3, "contribut": 3, "exampl": 3, "indic": 3, "tabl": 3, "pick_non_non": [], "helper": 2, "function": 2}, "envversion": {"sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx": 58}, "alltitles": {"On this page": [[1, "on-this-page"], [3, "on-this-page"], [0, "on-this-page"], [2, "on-this-page"]], "interplot.iter: Iteration Tools Documentation and API reference": [[1, "interplot-iter-iteration-tools-documentation-and-api-reference"]], "zip_smart": [[1, "zip-smart"]], "repeat": [[1, "repeat"]], "filter_nozip": [[1, "filter-nozip"]], "sum_nested": [[1, "sum-nested"]], "Deprecated": [[1, "deprecated"]], "interplot": [[3, "interplot"], [3, "id1"]], "Quick Start": [[3, "quick-start"]], "Resources": [[3, "resources"]], "Licence": [[3, "licence"]], "Demo": [[3, "demo"]], "Install": [[3, "install"]], "dev installation": [[3, "dev-installation"]], "Contribute": [[3, "contribute"]], "Examples": [[3, "examples"]], "Indices and tables": [[3, "indices-and-tables"]], "interplot.conf: Module Defaults Configuration": [[0, "interplot-conf-module-defaults-configuration"]], "VARIABLES": [[0, "variables"]], "interplot Documentation and API reference": [[2, "interplot-documentation-and-api-reference"]], "The core Plot class": [[2, "the-core-plot-class"]], "One-line Plotting": [[2, "one-line-plotting"]], "magic_plot decorators": [[2, "magic-plot-decorators"]], "NotebookInteraction Parent Class": [[2, "notebookinteraction-parent-class"]], "ShowDataArray, ShowDataset": [[2, "showdataarray-showdataset"]], "Helper functions": [[2, "helper-functions"]]}, "indexentries": {"js_render_warning (interplot.notebookinteraction attribute)": [[2, "interplot.NotebookInteraction.JS_RENDER_WARNING"]], "js_render_warning (interplot.plot attribute)": [[2, "interplot.Plot.JS_RENDER_WARNING"]], "js_render_warning (interplot.showdataarray attribute)": [[2, "interplot.ShowDataArray.JS_RENDER_WARNING"]], "js_render_warning (interplot.showdataset attribute)": [[2, "interplot.ShowDataset.JS_RENDER_WARNING"]], "notebookinteraction (class in interplot)": [[2, "interplot.NotebookInteraction"]], "plot (class in interplot)": [[2, "interplot.Plot"]], "showdataarray (class in interplot)": [[2, "interplot.ShowDataArray"]], "showdataset (class in interplot)": [[2, "interplot.ShowDataset"]], "_repr_html_() (interplot.notebookinteraction method)": [[2, "interplot.NotebookInteraction._repr_html_"]], "_repr_html_() (interplot.plot method)": [[2, "interplot.Plot._repr_html_"]], "_repr_html_() (interplot.showdataarray method)": [[2, "interplot.ShowDataArray._repr_html_"]], "_repr_html_() (interplot.showdataset method)": [[2, "interplot.ShowDataset._repr_html_"]], "_repr_mimebundle_() (interplot.notebookinteraction method)": [[2, "interplot.NotebookInteraction._repr_mimebundle_"]], "_repr_mimebundle_() (interplot.plot method)": [[2, "interplot.Plot._repr_mimebundle_"]], "_repr_mimebundle_() (interplot.showdataarray method)": [[2, "interplot.ShowDataArray._repr_mimebundle_"]], "_repr_mimebundle_() (interplot.showdataset method)": [[2, "interplot.ShowDataset._repr_mimebundle_"]], "add_bar() (interplot.plot method)": [[2, "interplot.Plot.add_bar"]], "add_boxplot() (interplot.plot method)": [[2, "interplot.Plot.add_boxplot"]], "add_fill() (interplot.plot method)": [[2, "interplot.Plot.add_fill"]], "add_heatmap() (interplot.plot method)": [[2, "interplot.Plot.add_heatmap"]], "add_hist() (interplot.plot method)": [[2, "interplot.Plot.add_hist"]], "add_line() (interplot.plot method)": [[2, "interplot.Plot.add_line"]], "add_linescatter() (interplot.plot method)": [[2, "interplot.Plot.add_linescatter"]], "add_regression() (interplot.plot method)": [[2, "interplot.Plot.add_regression"]], "add_scatter() (interplot.plot method)": [[2, "interplot.Plot.add_scatter"]], "add_text() (interplot.plot method)": [[2, "interplot.Plot.add_text"]], "bar() (in module interplot)": [[2, "interplot.bar"]], "boxplot() (in module interplot)": [[2, "interplot.boxplot"]], "close() (interplot.plot method)": [[2, "interplot.Plot.close"]], "digest_color() (interplot.plot method)": [[2, "interplot.Plot.digest_color"]], "digest_marker() (interplot.plot static method)": [[2, "interplot.Plot.digest_marker"]], "fill() (in module interplot)": [[2, "interplot.fill"]], "get_cycle_color() (interplot.plot method)": [[2, "interplot.Plot.get_cycle_color"]], "heatmap() (in module interplot)": [[2, "interplot.heatmap"]], "hist() (in module interplot)": [[2, "interplot.hist"]], "init() (interplot.plot static method)": [[2, "interplot.Plot.init"]], "interplot.plot": [[2, "module-interplot.plot"]], "line() (in module interplot)": [[2, "interplot.line"]], "linescatter() (in module interplot)": [[2, "interplot.linescatter"]], "magic_plot() (in module interplot)": [[2, "interplot.magic_plot"]], "magic_plot_preset() (in module interplot)": [[2, "interplot.magic_plot_preset"]], "module": [[2, "module-interplot.plot"]], "pick_non_none() (in module interplot)": [[2, "interplot.pick_non_none"]], "plot() (interplot.showdataarray method)": [[2, "interplot.ShowDataArray.plot"]], "plot() (interplot.showdataset method)": [[2, "interplot.ShowDataset.plot"]], "post_process() (interplot.plot method)": [[2, "interplot.Plot.post_process"]], "regression() (in module interplot)": [[2, "interplot.regression"]], "save() (interplot.plot method)": [[2, "interplot.Plot.save"]], "scatter() (in module interplot)": [[2, "interplot.scatter"]], "show() (interplot.plot method)": [[2, "interplot.Plot.show"]], "text() (in module interplot)": [[2, "interplot.text"]]}})
\ No newline at end of file
diff --git a/interplot/conf.py b/interplot/conf.py
index 408fa0f..ebd6fe7 100644
--- a/interplot/conf.py
+++ b/interplot/conf.py
@@ -100,6 +100,136 @@
 """
 
 
+def GLOBAL_CUSTOM_FUNC(fig):
+    """
+    Apply a custom function on figures on finishing the plot.
+
+    Will be called in `interplot.Plot.post_process()`.
+
+    When overriding this function, make sure it accepts `fig`.
+    There is no need to return `fig`.
+
+    Parameters
+    ----------
+    fig: interplot.Plot instance
+
+    Examples
+    --------
+    >>> def global_custom_func(fig):
+    ...     # do your customisations
+    ...     fig.add_text(0, 0, "watermark", color="gray")
+    ...
+    ...     # also include default function
+    ...     conf.GLOBAL_CUSTOM_FUNC(fig)
+    ...
+    ... fig = interplot.Plot(
+    ...     global_custom_func=global_custom_func
+    ... )
+    ... fig.add_line(x, y)
+    ... fig.post_process()  # global_custom_func will be executed here
+    ... fig.show()
+    """
+    pass
+
+
+def MPL_CUSTOM_FUNC(fig, ax):
+    """
+    Apply a custom function on matplotlib figures on finishing the plot.
+
+    Will be called in `interplot.Plot.post_process()`.
+
+    When overriding this function, make sure it accepts `fig` and `ax`
+    and returns `fig` and `ax`.
+
+    Parameters
+    ----------
+    fig : matplotlib.figure.Figure
+        The figure object.
+    ax : matplotlib.axes.Axes
+        The axes object.
+
+    Returns
+    -------
+    fig : matplotlib.figure.Figure
+        The modified figure object.
+    ax : matplotlib.axes.Axes
+        The modified axes object.
+
+    Examples
+    --------
+    >>> def mpl_custom_func(fig, ax):
+    ...     # do your customisations
+    ...     fig.do_stuff()
+    ...     ax[0, 0].do_more()
+    ...
+    ...     # also include default function
+    ...     fig, ax = conf.MPL_CUSTOM_FUNC(fig, ax)
+    ...
+    ...     return fig, ax
+    ...
+    ... fig = interplot.Plot(
+    ...     interactive=False,
+    ...     mpl_custom_func=mpl_custom_func
+    ... )
+    ... fig.add_line(x, y)
+    ... fig.post_process()  # mpl_custom_func will be executed here
+    ... fig.show()
+    """
+    return fig, ax
+
+
+def PTY_CUSTOM_FUNC(fig):
+    """
+    Apply a custom function on plotly figures on finishing the plot.
+
+    Will be called in `interplot.Plot.post_process()`.
+
+    When overriding this function, make sure it accepts `fig`
+    and returns `fig`.
+
+    Parameters
+    ----------
+    fig : plotly.graph_objects.Figure
+        The figure object.
+
+    Returns
+    -------
+    fig : plotly.graph_objects.Figure
+        The modified figure object.
+
+    Examples
+    --------
+    >>> def pty_custom_func(fig):
+    ...     # do your customisations
+    ...     fig.do_stuff()
+    ...
+    ...     # also include default function
+    ...     fig = conf.PTY_CUSTOM_FUNC(fig)
+    ...
+    ...     return fig
+    ...
+    ... fig = interplot.Plot(
+    ...     interactive=True,
+    ...     pty_custom_func=pty_custom_func
+    ... )
+    ... fig.add_line(x, y)
+    ... fig.post_process()  # pty_custom_func will be executed here
+    ... fig.show()
+    """
+    return fig
+
+
+PTY_UPDATE_LAYOUT = dict()
+"""
+PLOTLY ONLY.
+
+Pass keyword arguments to plotly's
+`plotly.graph_objects.Figure.update_layout(**pty_update_layout)`
+
+Default: None
+"""
+
+
 PTY_CONFIG = dict(
     displayModeBar=True,
     displaylogo=False,
@@ -248,7 +378,7 @@
         Display an interactive plotly line plot
         instead of the default matplotlib figure.
     rows, cols: int, default: 1
-        Create a grid with x rows and y columns.
+        Create a grid with `n` rows and `m` columns.
     title: str, default: None
         Plot title.
     xlabel, ylabel: str or str tuple, default: None
@@ -261,7 +391,7 @@
         In case of multiple rows/cols provide either:
             - a tuple
             - a tuple for each row
-            - a tuple for each row containing tuple for each column.
+            - a tuple for each row containing a tuple for each column.
     shared_xaxes, shared_yaxes: str, default: None
         Define how multiple subplots share there axes.
 
@@ -274,11 +404,11 @@
         Ratios of the width/height dimensions in each column/row.
         Will be normalised to a sum of 1.
     fig_size: tuple of 2x float, optional
-        Figure size in pixels.
+        Width, height in pixels.
 
-        Default behavior:
-            - MPL: Default figure size.
-            - PLT: Responsive sizing.
+        Default behavior defined by `conf.MPL_FIG_SIZE`
+        and `conf.PTY_FIG_SIZE`.
+        Plotly allows None for responsive size adapting to viewport.
     dpi: int, default: 100
         Plot resolution.
     legend_loc: str, optional
@@ -292,6 +422,11 @@
         the subplots may be provided.
 
         PTY: Just provide a `str`.
+    color_cycle: list, optional
+        Cycle through colors in the provided list.
+
+        If left `None`, the default color cycle
+        `conf.COLOR_CYCLE` will be used.
     save_fig: str or pathlib.Path, default: None
         Provide a path to export the plot.
 
@@ -310,31 +445,82 @@
 
         An iterable of multiple formats may be provided. In this case
         the save command will be repeated for each element.
-    pty_update_layout: dict, default: None
-        PLOTLY ONLY.
-        Pass keyword arguments to plotly's
-        `fig.update_layout(**pty_update_layout)`
-        Thus, take full control over
-    pty_custom_func: function, default: None
-        PLOTLY ONLY.
-        Pass a function reference to further style the plotly graphs.
-        Function must accept `fig` and return `fig`.
-
-        >>> def pty_custom_func(fig):
-        ...     fig.do_stuff()
-        ...     return fig
+    save_config: dict, default: None
+        Plotly only.
+
+        Pass config options to
+        `plotly.graph_objects.Figure.write_html(config=save_config)`.
+    global_custom_func: function, default: None
+        Pass a function reference to further style the figures.
+
+        >>> def global_custom_func(fig):
+        ...     # do your customisations
+        ...     fig.add_text(0, 0, "watermark", color="gray")
+        ...
+        ...     # also include default function
+        ...     conf.GLOBAL_CUSTOM_FUNC(fig)
+        ...
+        ... fig = interplot.Plot(
+        ...     global_custom_func=global_custom_func
+        ... )
+        ... fig.add_line(x, y)
+        ... fig.post_process()  # global_custom_func will be executed here
+        ... fig.show()
     mpl_custom_func: function, default: None
         MATPLOTLIB ONLY.
+
         Pass a function reference to further style the matplotlib graphs.
         Function must accept `fig, ax` and return `fig, ax`.
 
-        Note: `ax` always has `row` and `col` coordinates, even if the plot is
-        just 1x1.
+        Note: `ax` always has `row` and `col` coordinates, even if the
+        plot is just 1x1.
 
         >>> def mpl_custom_func(fig, ax):
+        ...     # do your customisations
         ...     fig.do_stuff()
         ...     ax[0, 0].do_more()
+        ...
+        ...     # also include default function
+        ...     fig, ax = conf.MPL_CUSTOM_FUNC(fig, ax)
+        ...
         ...     return fig, ax
+        ...
+        ... fig = interplot.Plot(
+        ...     interactive=False,
+        ...     mpl_custom_func=mpl_custom_func
+        ... )
+        ... fig.add_line(x, y)
+        ... fig.post_process()  # mpl_custom_func will be executed here
+        ... fig.show()
+    pty_custom_func: function, default: None
+        PLOTLY ONLY.
+
+        Pass a function reference to further style the plotly graphs.
+        Function must accept `fig` and return `fig`.
+
+        >>> def pty_custom_func(fig):
+        ...     # do your customisations
+        ...     fig.do_stuff()
+        ...
+        ...     # also include default function
+        ...     fig = conf.PTY_CUSTOM_FUNC(fig)
+        ...
+        ...     return fig
+        ...
+        ... fig = interplot.Plot(
+        ...     interactive=True,
+        ...     pty_custom_func=pty_custom_func
+        ... )
+        ... fig.add_line(x, y)
+        ... fig.post_process()  # pty_custom_func will be executed here
+        ... fig.show()
+    pty_update_layout: dict, default: None
+        PLOTLY ONLY.
+
+        Pass keyword arguments to plotly's
+        `plotly.graph_objects.Figure.update_layout(**pty_update_layout)`
+
+        Default: None
 
     Returns
     -------
diff --git a/interplot/plot.py b/interplot/plot.py
index 48e02fd..0bb8979 100644
--- a/interplot/plot.py
+++ b/interplot/plot.py
@@ -77,6 +77,32 @@ def init_notebook_mode(connected=False):
     init_notebook_mode()
 
 
+def pick_non_none(*args, fail=False):
+    """
+    Return the first non-None argument.
+
+    Parameters
+    ----------
+    *args: any
+        Any number of arguments.
+    fail: bool, default: False
+        Throw a ValueError if all args are None.
+
+        If set to False, None will be returned.
+
+    Returns
+    -------
+    any
+        The first non-None argument.
+    """
+    for arg in args:
+        if arg is not None:
+            return arg
+    if fail:
+        raise ValueError("All arguments have value None.")
+    return None
+
+
 def _rewrite_docstring(doc_core, doc_decorator=None, kwargs_remove=()):
     """
     Appends arguments to a docstring.
@@ -539,12 +565,14 @@ def __init__(
         dpi=None,
         legend_loc=None,
         legend_title=None,
+        color_cycle=None,
         save_fig=None,
         save_format=None,
         save_config=None,
-        pty_update_layout=None,
-        pty_custom_func=None,
+        global_custom_func=None,
         mpl_custom_func=None,
+        pty_custom_func=None,
+        pty_update_layout=None,
     ):
         # input verification
         if shared_xaxes == "cols":
@@ -552,10 +580,9 @@ def __init__(
         if shared_yaxes == "cols":
             shared_yaxes = "columns"
 
-        self.interactive = (
-            conf.INTERACTIVE
-            if interactive is None
-            else interactive
+        self.interactive = pick_non_none(
+            interactive,
+            conf.INTERACTIVE,
         )
         self.rows = rows
         self.cols = cols
@@ -564,25 +591,32 @@ def __init__(
         self.ylabel = ylabel
         self.xlim = xlim
         self.ylim = ylim
+        self.dpi = pick_non_none(
+            dpi,
+            conf.DPI,
+        )
         self.legend_loc = legend_loc
         self.legend_title = legend_title
-        self.dpi = conf.DPI if dpi is None else dpi
+        self.color_cycle = pick_non_none(
+            color_cycle,
+            conf.COLOR_CYCLE,
+        )
         self.save_fig = save_fig
         self.save_format = save_format
         self.save_config = save_config
-        self.pty_update_layout = pty_update_layout
-        self.pty_custom_func = pty_custom_func
+        self.global_custom_func = global_custom_func
         self.mpl_custom_func = mpl_custom_func
+        self.pty_custom_func = pty_custom_func
+        self.pty_update_layout = pty_update_layout
         self.element_count = np.zeros((rows, cols), dtype=int)
         self.i_color = 0
 
         # init plotly
         if self.interactive:
             self.title = self._encode_html(self.title)
-            self.fig_size = (
-                conf.PTY_FIG_SIZE
-                if fig_size is None
-                else fig_size
+            self.fig_size = pick_non_none(
+                fig_size,
+                conf.PTY_FIG_SIZE,
             )
 
             # init fig
@@ -663,10 +697,9 @@ def __init__(
             )
 
             # convert px to inches
-            self.fig_size = (
-                conf.MPL_FIG_SIZE
-                if fig_size is None
-                else fig_size
+            self.fig_size = pick_non_none(
+                fig_size,
+                conf.MPL_FIG_SIZE,
             )
             px = 1 / self.dpi
             figsize = (self.fig_size[0] * px, self.fig_size[1] * px)
@@ -771,7 +804,7 @@ def _get_plotly_legend_args(label, default_label=None, show_legend=None):
             on hover)
         """
         legend_kwargs = dict(
-            name=default_label if label is None else str(label)
+            name=pick_non_none(label, default_label),
         )
         if show_legend:
             legend_kwargs["showlegend"] = True
@@ -833,13 +866,13 @@ def get_cycle_color(self, increment=1, i=None):
             HEX color, with leading hashtag
         """
         if i is None:
-            if self.i_color >= len(conf.COLOR_CYCLE):
+            if self.i_color >= len(self.color_cycle):
                 self.i_color = 0
-            color = conf.COLOR_CYCLE[self.i_color]
+            color = self.color_cycle[self.i_color]
             self.i_color += increment
             return color
         else:
-            return conf.COLOR_CYCLE[i]
+            return self.color_cycle[i]
 
     def digest_color(self, color=None, alpha=None, increment=1):
         """
@@ -864,7 +897,7 @@ def digest_color(self, color=None, alpha=None, increment=1):
 
         # get index from COLOR_CYCLE
         elif color[0] == "C" or color[0] == "c":
-            color = conf.COLOR_CYCLE[int(color[1:]) % len(conf.COLOR_CYCLE)]
+            color = self.color_cycle[int(color[1:]) % len(self.color_cycle)]
 
         rgba = list(mcolors.to_rgba(color))
         if alpha is not None:
@@ -2059,71 +2092,111 @@ def add_text(
 
     def post_process(
         self,
-        pty_update_layout=None,
-        pty_custom_func=None,
+        global_custom_func=None,
         mpl_custom_func=None,
+        pty_custom_func=None,
+        pty_update_layout=None,
     ):
         """
         Finish the plot.
 
         Parameters
         ----------
-        Note: If not provided, the parameters given on init will be used.
-        pty_update_layout: dict, optional
-            PLOTLY ONLY.
-            Pass keyword arguments to plotly's
-            fig.update_layout(**pty_update_layout)
-            Thus, take full control over
-            Default: None
-        pty_custom_func: function, optional
-            PLOTLY ONLY.
-            Pass a function reference to further style the plotly graphs.
-            Function must accept fig and return fig.
-
-            Default: None
-
-            Example:
-
-            >>> def pty_custom_func(fig):
-            ...     fig.do_stuff()
-            ...     return fig
-        mpl_custom_func: function, optional
+        Note: If not provided, the parameters given on init or
+        the `interplot.conf` default values will be used.
+        mpl_custom_func: function, default: None
             MATPLOTLIB ONLY.
+
             Pass a function reference to further style the matplotlib graphs.
-            Function must accept fig, ax and return fig, ax.
+            Function must accept `fig, ax` and return `fig, ax`.
 
-            Default: None
+            If providing your own function reference, `conf.MPL_CUSTOM_FUNC`
+            will not be executed.
 
-            Example:
+            Note: `ax` always has `row` and `col` coordinates, even if the
+            plot is just 1x1.
 
             >>> def mpl_custom_func(fig, ax):
+            ...     # do your customisations
             ...     fig.do_stuff()
-            ...     ax.do_more()
+            ...     ax[0, 0].do_more()
+            ...
+            ...     # also include default function
+            ...     fig, ax = conf.MPL_CUSTOM_FUNC(fig, ax)
+            ...
             ...     return fig, ax
+            ...
+            ... fig = interplot.Plot(
+            ...     interactive=False,
+            ...     mpl_custom_func=mpl_custom_func
+            ... )
+            ... fig.add_line(x, y)
+            ... fig.post_process()  # mpl_custom_func will be executed here
+            ... fig.show()
+        pty_custom_func: function, default: None
+            PLOTLY ONLY.
+
+            Pass a function reference to further style the plotly graphs.
+            Function must accept `fig` and return `fig`.
+
+            If providing your own function reference, `conf.PTY_CUSTOM_FUNC`
+            will not be executed.
+
+            >>> def pty_custom_func(fig):
+            ...     # do your customisations
+            ...     fig.do_stuff()
+            ...
+            ...     # also include default function
+            ...     fig = conf.PTY_CUSTOM_FUNC(fig)
+            ...
+            ...     return fig
+            ...
+            ... fig = interplot.Plot(
+            ...     interactive=True,
+            ...     pty_custom_func=pty_custom_func
+            ... )
+            ... fig.add_line(x, y)
+            ... fig.post_process()  # pty_custom_func will be executed here
+            ... fig.show()
+        pty_update_layout: dict, default: None
+            PLOTLY ONLY.
+
+            Pass keyword arguments to plotly's
+            `plotly.graph_objects.Figure.update_layout(**pty_update_layout)`
         """
         # input verification
-        pty_update_layout = (
-            self.pty_update_layout
-            if pty_update_layout is None
-            else pty_update_layout
+        global_custom_func = pick_non_none(
+            global_custom_func,
+            self.global_custom_func,
+            conf.GLOBAL_CUSTOM_FUNC,
+            lambda _: None,
+        )
+        mpl_custom_func = pick_non_none(
+            mpl_custom_func,
+            self.mpl_custom_func,
+            conf.MPL_CUSTOM_FUNC,
+            lambda fig, ax: (fig, ax),
         )
-        pty_custom_func = (
-            self.pty_custom_func
-            if pty_custom_func is None
-            else pty_custom_func
+        pty_custom_func = pick_non_none(
+            pty_custom_func,
+            self.pty_custom_func,
+            conf.PTY_CUSTOM_FUNC,
+            lambda fig: fig,
         )
-        mpl_custom_func = (
-            self.mpl_custom_func
-            if mpl_custom_func is None
-            else mpl_custom_func
+        pty_update_layout = pick_non_none(
+            pty_update_layout,
+            self.pty_update_layout,
+            conf.PTY_UPDATE_LAYOUT,
+            dict(),
         )
 
+        # global custom function
+        global_custom_func(self)
+
         # PLOTLY
         if self.interactive:
-            if pty_update_layout is not None:
-                self.fig.update_layout(**pty_update_layout)
-            if pty_custom_func is not None:
-                self.fig = pty_custom_func(self.fig)
+            self.fig.update_layout(**pty_update_layout)
+            self.fig = pty_custom_func(self.fig)
 
         # MATPLOTLIB
         else:
@@ -2294,6 +2367,14 @@ def show(self):
             )
         return self.fig.show()
 
+    def close(self):
+        """Close the plot."""
+        if not self.interactive:
+            plt.close(self.fig)
+
+        else:
+            warn("close() is not supported for interactive plots.")
+
     def _repr_mimebundle_(self, *args, **kwargs):
         if self.interactive:
             return self.fig._repr_mimebundle_(*args, **kwargs)
@@ -2364,11 +2445,11 @@ def magic_plot(core, doc_decorator=None):
 
     def wrapper(
         *args,
+        fig=None,
+        skip_post_process=False,
         interactive=None,
         rows=1,
         cols=1,
-        fig=None,
-        skip_post_process=False,
         title=None,
         xlabel=None,
         ylabel=None,
@@ -2382,12 +2463,14 @@ def wrapper(
         dpi=None,
         legend_loc=None,
         legend_title=None,
+        color_cycle=None,
         save_fig=None,
         save_format=None,
         save_config=None,
-        pty_update_layout=None,
-        pty_custom_func=None,
+        global_custom_func=None,
         mpl_custom_func=None,
+        pty_custom_func=None,
+        pty_update_layout=None,
         **kwargs,
     ):
         # init Plot
@@ -2409,12 +2492,14 @@ def wrapper(
             dpi=dpi,
             legend_loc=legend_loc,
             legend_title=legend_title,
+            color_cycle=color_cycle,
             save_fig=save_fig,
             save_format=save_format,
             save_config=save_config,
-            pty_update_layout=pty_update_layout,
-            pty_custom_func=pty_custom_func,
+            global_custom_func=global_custom_func,
             mpl_custom_func=mpl_custom_func,
+            pty_custom_func=pty_custom_func,
+            pty_update_layout=pty_update_layout,
         )
 
         # execute core method
diff --git a/setup.py b/setup.py
index 6f7df09..1ab7039 100644
--- a/setup.py
+++ b/setup.py
@@ -10,7 +10,7 @@
 
 setup(
     name='interplot',
-    version='1.0.0',
+    version='1.0.1',
     description=(
         "Create matplotlib and plotly charts with the same few lines of code."
     ),
@@ -24,7 +24,7 @@
     install_requires=requirements,
 
     classifiers=[
-        'Development Status :: 3 - Alpha',
+        'Development Status :: 5 - Production/Stable',
         'Intended Audience :: Science/Research',
         'License :: OSI Approved :: GNU General Public License v3 (GPLv3)',
         'Operating System :: MacOS :: MacOS X',
@@ -33,5 +33,8 @@
         'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.7',
         'Programming Language :: Python :: 3.8',
+        'Framework :: Jupyter :: JupyterLab :: 4',
+        'Framework :: Matplotlib',
+        'Topic :: Scientific/Engineering :: Visualization',
     ],
 )
diff --git a/source/api_conf.rst b/source/api_conf.rst
index fa8df78..231d09f 100644
--- a/source/api_conf.rst
+++ b/source/api_conf.rst
@@ -27,6 +27,12 @@
 
 .. autovariable:: interplot.conf.EXPORT_REPLACE
 
+.. autofunction:: interplot.conf.GLOBAL_CUSTOM_FUNC
+
+.. autofunction:: interplot.conf.MPL_CUSTOM_FUNC
+
+.. autofunction:: interplot.conf.PTY_CUSTOM_FUNC
+
 .. autovariable:: interplot.conf.PTY_CONFIG
 
 .. autovariable:: interplot.conf.PTY_LINE_STYLES
diff --git a/source/api_plot.rst b/source/api_plot.rst
index 98d255f..739dbc8 100644
--- a/source/api_plot.rst
+++ b/source/api_plot.rst
@@ -90,3 +90,9 @@ One-line Plotting
     :private-members: _repr_html_, _repr_mimebundle_
     :member-order: bysource
 
+
+----------------
+Helper functions
+----------------
+
+.. autofunction:: interplot.pick_non_none


Test patch:
diff --git a/tests/plot_test.py b/tests/plot_test.py
index 72c651e..d95c558 100644
--- a/tests/plot_test.py
+++ b/tests/plot_test.py
@@ -35,7 +35,7 @@ def inner(interactive, *args, **kwargs):
         try:
             f(*args, **kwargs, interactive=interactive)
             if not interactive:
-                plt.close()
+                plt.close("all")
         except:  # noqa: E722
             no_error = False
 
@@ -157,6 +157,47 @@ def test_line_opacity(opacity, interactive):
     ip.line([1, 2, 3], [4, 5, 6], opacity=opacity, interactive=interactive)
 
 
+def test_default_funcs():
+    def mpl_custom_func_1(fig, ax):
+        fig.customvalue = 1
+        return fig, ax
+
+    def mpl_custom_func_2(fig, ax):
+        fig.customvalue = 2
+        return fig, ax
+
+    def mpl_custom_func_3(fig, ax):
+        fig.customvalue = 3
+        return fig, ax
+
+    ip.conf.MPL_CUSTOM_FUNC = mpl_custom_func_1
+
+    fig = ip.Plot(
+        interactive=False,
+    )
+    fig.add_line((1, 4))
+    fig.post_process()
+    assert fig.fig.customvalue == 1
+
+    fig = ip.Plot(
+        interactive=False,
+        mpl_custom_func=mpl_custom_func_2,
+    )
+    fig.add_line((1, 4))
+    fig.post_process()
+    assert fig.fig.customvalue == 2
+
+    fig = ip.Plot(
+        interactive=False,
+        mpl_custom_func=mpl_custom_func_2,
+    )
+    fig.add_line((1, 4))
+    fig.post_process(mpl_custom_func=mpl_custom_func_3)
+    assert fig.fig.customvalue == 3
+
+    plt.close("all")
+
+
 @test_for_errors
 def test_complex_plot(interactive):
     np.random.seed(0)