{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Outline: \n", " \n", "1. Package intro\n", "2. How to read food webs from file?\n", "3. How to work with food webs?\n", "4. How to visualize food webs?\n", "\n", "This tutorial is a Jupyter Notebook consisting of text ('Markdown') and code cells. A code cell (with grey background) can be evaluated by pressing Shift+Enter when the cell is selected (as indicated by a green rectangle). The output will appear below the code. You can insert a new cell selecting \"+\" in the menu above, to write and evaluate your own code." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# foodwebviz \n", "\n", "foodwebviz is a Python package for visualisation of foodwebs (trophic networks)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import foodwebviz as fw" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function read_from_XLS in module foodwebviz.io:\n", "\n", "read_from_XLS(filename)\n", " Read foodweb from an XLS (spreadsheet) file, see examples/data/Richards_Bay_C_Summer.xls.\n", " \n", " Parameters\n", " ----------\n", " foodweb : foodwebs.FoodWeb\n", " Object to save.\n", " filename: string\n", " Destination path.\n", " \n", " Description\n", " ----------\n", " The XLS file consists of three sheets: \n", " 'Title':\n", " containing the name of the food web\n", " 'Node properties':\n", " with a table describing nodes through the following columns: \n", " 'Names', 'IsAlive', 'Biomass', 'Import', 'Export', 'Respiration', 'TrophicLevel'\n", " 'Internal flows':\n", " with a table describing flows between the nodes in the system; \n", " the first row and the first column contain node names;\n", " table elements contain flow values from the node in the row to the node in the column.\n", "\n" ] } ], "source": [ "help(fw.read_from_XLS)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# How to read food webs?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`fw.read_from_SCOR(scor_path)` allows to read food web from a file in the SCOR format. An example is provided: data/Richards_Bay_C_Summer.scor. To check the format specification please evaluate `help(fw.read_from_SCOR)`.\n", "\n", "`fw.read_from_CSV(csv_path)` allows to read a food web from a text file with semicolon-separated variables. An example is provided: data/Richards_Bay_C_Summer.CSV. To check the format specification please evaluate `help(fw.read_from_CSV)`.\n", "\n", "`fw.read_from_XLS(csv_path)` allows to read a food web from an XLS file. An example is provided: data/Richards_Bay_C_Summer.XLS. To check the format specification please evaluate `help(fw.read_from_XLS)`.\n", "\n", "Each path can be an absolute or a relative path (w.r.t. the folder foodwebviz/examples)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading file: data/Alaska_Prince_William_Sound.scor\n" ] } ], "source": [ "foodweb = fw.read_from_SCOR('data/Alaska_Prince_William_Sound.scor')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# How to work with food webs? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's check some basic information about a food web:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Title: Test elasticities\n", "\n", "Number of nodes: 3\n", "Number of living nodes: 2\n" ] } ], "source": [ "print(f'Title: {foodweb.title}\\n')\n", "print(f'Number of nodes: {foodweb.n}')\n", "print(f'Number of living nodes: {foodweb.n_living}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's see the first rows of the table with information on nodes (vertices) - pandas.DataFrame:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IsAliveBiomassExportRespirationTrophicLevelImport
Names
DiatomsTrue0.50110.074.6492.0000000.0
FlagellatesTrue0.34240.053.4382.0000000.0
BacteriaTrue0.52000.027.5602.0299990.0
HM planktonTrue0.08180.05.8922.0299860.0
Small copepodsTrue0.03750.03.4502.8683660.0
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" ], "text/plain": [ " IsAlive Biomass Export Respiration TrophicLevel Import\n", "Names \n", "Diatoms True 0.5011 0.0 74.649 2.000000 0.0\n", "Flagellates True 0.3424 0.0 53.438 2.000000 0.0\n", "Bacteria True 0.5200 0.0 27.560 2.029999 0.0\n", "HM plankton True 0.0818 0.0 5.892 2.029986 0.0\n", "Small copepods True 0.0375 0.0 3.450 2.868366 0.0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "foodweb.node_df.head(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The information about biomass flows is encoded in the flow matrix (showing first five rows):" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DiatomsFlagellatesBacteriaHM planktonSmall copepodsMedium copepodsLarge copepodsOther lg zooplanktonSm macrobenthosLg suspension feeder...Sm pelagic fishLg pelagic fishSkates and raysSm benthic sharkLarge sharksCetaceansSusp POCSed POCDOCDIC
Names
Diatoms0.00.01.6770.2642.89001.80000.89400.03070.000.0...251.00000.00.00.00.00.041.100.00.00.0
Flagellates0.00.01.6770.2642.06001.23000.63800.02190.000.0...188.00000.00.00.00.00.022.100.00.00.0
Bacteria0.00.00.0000.0000.18600.79100.41000.02050.0058.8...0.00000.00.00.00.00.024.000.00.00.0
HM plankton0.00.00.0000.0000.00120.00480.00250.00057.760.0...0.00000.00.00.00.00.03.930.00.00.0
Small copepods0.00.00.0000.0000.12400.87800.20700.05790.000.0...0.05420.00.00.00.00.01.300.00.00.0
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5 rows × 33 columns

\n", "
" ], "text/plain": [ " Diatoms Flagellates Bacteria HM plankton Small copepods \\\n", "Names \n", "Diatoms 0.0 0.0 1.677 0.264 2.8900 \n", "Flagellates 0.0 0.0 1.677 0.264 2.0600 \n", "Bacteria 0.0 0.0 0.000 0.000 0.1860 \n", "HM plankton 0.0 0.0 0.000 0.000 0.0012 \n", "Small copepods 0.0 0.0 0.000 0.000 0.1240 \n", "\n", " Medium copepods Large copepods Other lg zooplankton \\\n", "Names \n", "Diatoms 1.8000 0.8940 0.0307 \n", "Flagellates 1.2300 0.6380 0.0219 \n", "Bacteria 0.7910 0.4100 0.0205 \n", "HM plankton 0.0048 0.0025 0.0005 \n", "Small copepods 0.8780 0.2070 0.0579 \n", "\n", " Sm macrobenthos Lg suspension feeder ... Sm pelagic fish \\\n", "Names ... \n", "Diatoms 0.00 0.0 ... 251.0000 \n", "Flagellates 0.00 0.0 ... 188.0000 \n", "Bacteria 0.00 58.8 ... 0.0000 \n", "HM plankton 7.76 0.0 ... 0.0000 \n", "Small copepods 0.00 0.0 ... 0.0542 \n", "\n", " Lg pelagic fish Skates and rays Sm benthic shark \\\n", "Names \n", "Diatoms 0.0 0.0 0.0 \n", "Flagellates 0.0 0.0 0.0 \n", "Bacteria 0.0 0.0 0.0 \n", "HM plankton 0.0 0.0 0.0 \n", "Small copepods 0.0 0.0 0.0 \n", "\n", " Large sharks Cetaceans Susp POC Sed POC DOC DIC \n", "Names \n", "Diatoms 0.0 0.0 41.10 0.0 0.0 0.0 \n", "Flagellates 0.0 0.0 22.10 0.0 0.0 0.0 \n", "Bacteria 0.0 0.0 24.00 0.0 0.0 0.0 \n", "HM plankton 0.0 0.0 3.93 0.0 0.0 0.0 \n", "Small copepods 0.0 0.0 1.30 0.0 0.0 0.0 \n", "\n", "[5 rows x 33 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "foodweb.flow_matrix.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# How to visualize food webs?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A heatmap of food web flows\n", "\n", "The heatmap representation of a food web has the network's nodes on its axes. The color intensity at the intersection of rows and columns marking these nodes represents the biomass flow from the node in row to the node in the column. Additionally, trophic levels related to the nodes can be showed as background colour.\n", "\n", "The heatmap can also show biomass flows normalised, e.g. to the total inflows (to show diet proportions) of the target node, or to the biomass of the source node (as in donor-control model), or to the sum of all flows in the network (total system throughflow, TST). The normalisation to TST is a natural way of comparing food webs with each other, as each food web is likely to use different units. \n", "\n", "The documentation of draw_hetmap() method that implements it is presented below:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function draw_heatmap in module foodwebviz.visualization:\n", "\n", "draw_heatmap(food_web, boundary=False, normalization='log', show_trophic_layer=True, switch_axes=False, width=1200, height=800, font_size=14, save=False, output_filename='heatmap.pdf')\n", " Visualize foodweb as a heatmap. On the interesction\n", " of X axis (\"from\" node) and Y axis (\"to\" node) flow weight\n", " is indicated.\n", " \n", " Parameters\n", " ----------\n", " food_web : foodwebs.FoodWeb\n", " Foodweb object.\n", " boundary : bool, optional (default=False)\n", " If True, boundary flows will be added to the graph.\n", " Boundary flows are: Import, Export, and Repiration.\n", " normalization : string, optional (default=log)\n", " Defines method of graph edges normalization.\n", " Available options are: 'diet', 'log', 'donor_control',\n", " 'predator_control', 'mixed_control', 'linear' and 'TST'.\n", " show_trophic_layer : bool, optional (default=False)\n", " If True, include additional heatmap layer presenting trophic levels relevant to X axis.\n", " switch_axes : bool, optional (default=False)\n", " If True, X axis will represent \"to\" nodes and Y - \"from\".\n", " width : int, optional (default=1200)\n", " Width of the heatmap plot, large foodwebs might not fit in default width.\n", " height : int, optional (default=800)\n", " Height of the heatmap plot, large foodwebs might not fit in default height.\n", " font_size: int, optional (default=18)\n", " font size of labels\n", " save: bool, optional (default=False)\n", " If True, the heatmap will be saved as a PDF, SVG, PNG or JPEG according to the output_filename parameter.\n", " output_filename: string, optional (default='heatmap.pdf')\n", " A filename denoting the destination to write the heatmap to, in PDF, SVG, PNG or JPEG formats.\n", " \n", " Returns\n", " -------\n", " heatmap : plotly.graph_objects.Figure\n", "\n" ] } ], "source": [ "help(fw.draw_heatmap)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example usage:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "colorscale": [ [ 0, "rgb(255, 255, 255)" ], [ 0.2, "rgb(214, 233, 255)" ], [ 0.4, "rgb(197, 218, 251)" ], [ 0.6, "rgb(182, 201, 247)" ], [ 0.8, "rgb(168, 183, 240 )" ], [ 1, "rgb(167, 167, 221 )" ] ], "hoverinfo": "skip", "name": "Trophic Layer", "showlegend": true, "showscale": false, "type": "heatmap", "x": [ "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish", "Wild salmon fry", "Epi. zoobenthos", "Intertidal inv.", "Herring", "Inf. zoobenthos", "Hatch. salmon fry", "Small pelagics", "Mesozooplankton", "Macrozooplankton", "Birds", "Pinnipeds", "Trans. mammals", "Res. mammals", "Sea otters", "Salmon", "✗ Detritus", "Demersal fish" ], "xgap": 0.2, "y": [ "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Intertidal inv.", "Res. mammals", "Res. mammals", "Res. mammals", "Res. mammals", "Res. mammals", "Res. mammals", "Res. mammals", "Res. mammals", "Res. mammals", "Res. mammals", "Res. mammals", "Res. mammals", "Res. mammals", "Res. mammals", "Res. mammals", "Res. mammals", "Res. mammals", "Sea otters", "Sea otters", "Sea otters", "Sea otters", "Sea otters", "Sea otters", "Sea otters", "Sea otters", "Sea otters", "Sea otters", "Sea otters", "Sea otters", "Sea otters", "Sea otters", "Sea otters", "Sea otters", "Sea otters", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Inf. zoobenthos", "Small pelagics", "Small pelagics", "Small pelagics", "Small pelagics", "Small pelagics", "Small pelagics", "Small pelagics", "Small pelagics", "Small pelagics", "Small pelagics", "Small pelagics", "Small pelagics", "Small pelagics", "Small pelagics", "Small pelagics", "Small pelagics", "Small pelagics", "Macrozooplankton", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fw.draw_heatmap(foodweb, normalization='diet', show_trophic_layer=True, boundary=True, height=1000)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To save a heatmap as a PDF, SVG, PNG or JPEG file, set _save_ to _True_ and _'output_filename'_ to the intended filename with an appropriate extension." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A graph\n", "\n", "A natural way of representing graphs (networks) is just to draw the vertices and links between them.\n", "To improve readability we present them in an interactive form. There are possibilities to zoom, drag and drop and further adjust the plot via parameters.\n", "\n", "Food webs have a natural hierarchy, described by trophic levels. Therefore, the vertical positions of nodes represent their trophic levels. \n", "\n", "When drawing graphs, readability demands adjusting node positions to minimize clutter and overlap of nodes and links. A helpful algorithm uses an analogy with a physical system: nodes repel each other and links act like springs. This algorithm is used to optimize horizontal positions of nodes.\n", "\n", "The method's documentation with available parameters is presented below:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function draw_network_for_nodes in module foodwebviz.visualization:\n", "\n", "draw_network_for_nodes(food_web, nodes=None, file_name='interactive_food_web_graph.html', notebook=True, height='800px', width='100%', no_flows_to_detritus=True, cmap='viridis', **kwargs)\n", " Visualize subgraph of foodweb as a network.\n", " Parameters notebook, height, and width refer to initialization parameters of pyvis.network.Network.\n", " Additional parameters may be passed to hierachical repulsion layout as defined in\n", " pyvis.network.Network.hrepulsion. Examples are: node_distance, central_gravity,\n", " spring_length, or spring_strength.\n", " \n", " Parameters\n", " ----------\n", " food_web : foodwebs.FoodWeb\n", " Foodweb object.\n", " nodes : list of strings\n", " Nodes to include in subgraph to visualize.\n", " file_name : string, optional (default=\"food_web.html\")\n", " File to save network (in html format)\n", " notebook - bool, optional (default=True)\n", " True if using jupyter notebook.\n", " height : string, optional (default=\"800px\")\n", " Height of the canvas. See: pyvis.network.Network.hrepulsion\n", " width : string, optional (default=\"100%\")\n", " Width of the canvas. See: pyvis.network.Network.hrepulsion\n", " no_flows_to_detritus : bool, optional (default=True)\n", " True if only flows to living nodes should be drawn\n", " cmap : str (default='viridis')\n", " Color map representing trophic level as node colour. \n", " One of named matplotlib continuous color maps: https://matplotlib.org/stable/tutorials/colors/colormaps.html\n", " \n", " Returns\n", " -------\n", " heatmap : pyvis.network.Network\n", "\n" ] } ], "source": [ "help(fw.draw_network_for_nodes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Whole food webs might have too many nodes and connections to be clearly readable in this format. Therefore, we provide a way to draw only a part of the network. If 'nodes' parameter is specified, only the given nodes and their neighbours (nodes they directly interact with) are drawn." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", " \n", " " ], "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fw.draw_network_for_nodes(foodweb, [])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note: By defualt, this method creates a file named 'interactive_food_web_graph.html' which can be directly opened as a separate web browser tab. It allows to see and manipulate the graph in a larger window. Also, it is easier to take a snapshot of the graph to save it.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Trophic flows distributions as bar plots\n", "\n", "\n", "One can aggregate nodes into integer trophic levels to summarize certain aspects of a food web. A stacked bar plot shows percentages of flows from one trophic level to another. Color bars and numbers indicate the flow distribution around each trophic level, marked on the Y axis. Optionally, they can be normalized to 100\\%.\n", "\n", "The method's documentation with available parameters is presented below:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function draw_trophic_flows_distribution in module foodwebviz.visualization:\n", "\n", "draw_trophic_flows_distribution(food_web, normalize=True, width=1000, height=800, font_size=24)\n", " Visualize flows between trophic levels as a stacked bar chart.\n", " \n", " Parameters\n", " ----------\n", " food_web : foodwebs.FoodWeb\n", " Foodweb object.\n", " normalize : bool, optional (default=True)\n", " If True, bars will represent percentages summing up to 100\n", " width : int, optional (default=600)\n", " Width of the plot.\n", " height : int, optional (default=800)\n", " Height of the plot.\n", " \n", " Returns\n", " -------\n", " heatmap : plotly.graph_objects.Figure\n", "\n" ] } ], "source": [ "help(fw.draw_trophic_flows_distribution)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "b=fw.draw_trophic_flows_distribution(foodweb)\n", "b.write_image(\"b.pdf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A heatmap of trophic flows\n", "\n", "This heatmap visualization presents the sum of all flows between trophic levels. The color intensity encodes the flow sum for the trophic levels on the axes. The color scale can be transformed to a logarithmic scale.\n", "\n", "Method's documentation with available parameters is presented below:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function draw_trophic_flows_heatmap in module foodwebviz.visualization:\n", "\n", "draw_trophic_flows_heatmap(food_web, switch_axes=False, log_scale=False, width=1200, height=800, font_size=24)\n", " Visualize flows between foodweb's trophic levels as a heatmap.\n", " The color at (x,y) represents the sum of flows from trophic level x to\n", " trophic level y.\n", " \n", " Parameters\n", " ----------\n", " food_web : foodwebs.FoodWeb\n", " Foodweb object.\n", " switch_axes : bool, optional (default=False)\n", " If True, X axis will represent \"to\" trophic levels and Y - \"from\".\n", " log_scale : bool, optional (default=False)\n", " If True, log color scale will be used.\n", " width : int, optional (default=1200)\n", " Width of the plot.\n", " height : int, optional (default=800)\n", " Height of the plot\n", " font_size: int, optional (default=18)\n", " Font size of labels\n", " \n", " Returns\n", " -------\n", " heatmap : plotly.graph_objects.Figure\n", "\n" ] } ], "source": [ "help(fw.draw_trophic_flows_heatmap)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Example:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "c=fw.draw_trophic_flows_heatmap(foodweb, log_scale=True)\n", "c.write_image(\"c.pdf\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Animation\n", "\n", "An animated graph presents matter actually moving in a food web. To present the most relevant information, the respirations, exports and flows to detrital nodes are skipped - they are present for every living node.\n", "Vertical node positions represent their trophic levels. The density of moving particles maps the magnitude of the flow. The node sizes map their biomass stock. The mapping function can be given as the 'map_fun' argument and is set to the square root by default. \n", "Nodes are coloured according to their trophic level. Particles inherit the colour of their source node.\n", "\n", "Creating an animation can take long, especially if you would aim at a good-looking result with many particles in larger networks. Therefore, the default values are minimal to facilitate quick evaluation - we invite you to play around with the function parameters to obtain the desired result. This tutorial ends with two pre-computed animations." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function animate_foodweb in module foodwebviz.create_animated_food_web:\n", "\n", "animate_foodweb(foodweb, gif_file_out, fps=10, anim_len=1, trails=1, min_node_radius=0.5, min_part_num=1, max_part_num=20, map_fun=, include_imports=True, include_exports=False, cmap=, max_luminance=0.85, particle_size=8)\n", " foodweb_animation creates a GIF animation saved as gif_file_out based on the food web\n", " provided as a SCOR file scor_file_in. The canvas size in units relevant\n", " to further parameters is [0,100]x[0,100].\n", " \n", " Parameters\n", " ----------\n", " trails : int\n", " the number of shades after each particle; shades are dots of diminishing opacity;\n", " it significantly impacts computation length\n", " min_node_radius : float\n", " the radius of the smallest node on canvas [0,100]x[0,100]\n", " min_part_num : int\n", " the number of particles representing the smallest flow\n", " max_part_num : int\n", " the number of particles representing the largest flow\n", " map_fun : function\n", " a function defined over (positive) real numbers and returning a positive real number;\n", " used to squeeze the biomasses and flows that can span many orders of magnitude into the ranges\n", " [min_node_radius, max_node_radius] and [min_part_num, max_part_num]\n", " recommended functions: square root (numpy.sqrt) and logarithm (numpy.log10)\n", " the map_fun is used as g(x) in mapping the interval [a,b] to a known interval [A, B]:\n", " f(x)= A + (B-A)g(x)/g(b) where g(a)=0\n", " cmap :\n", " a continuous colour map\n", " (e.g. seaborn.color_palette(\"cubehelix\", as_cmap=True), matplotlib.pyplot.cm.get_cmap('viridis'))\n", " max_luminance : float\n", " cut-off for the color range in cmap, in [0,1]: no cut-off is equivalent to max_luminance=1\n", " usually, the highest values in a cmap range are very bright and hardly visible\n", " particle_size: float\n", " size of the flow particles\n", "\n" ] } ], "source": [ "help(fw.animate_foodweb)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "MovieWriter imagemagick unavailable; using Pillow instead.\n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fw.animate_foodweb(foodweb,'Example_animation.gif')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To view the created animation, type `![](Example_animation.gif \"Animated food web\")`\n", "into a cell, change the cell type to \"Markdown\" and evaluate it. Change the \"Example_animation.gif\" to the file name that you chose when making the animation. Below are two precomputed sample animations: food webs of Prince William Sound in Alaska and Richards Bay in South Africa." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](sample_output/Animation_Prince_William_Sound_Alaska.gif \"Animated food web\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](sample_output/Animation_Richards_Bay_South_Africa.gif \"Animated food web\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.8" } }, "nbformat": 4, "nbformat_minor": 4 }