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Merge pull request #18 from Giuseppecipolla95/release
Tutorial of flow__distance utility
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‎3D_plot/Axes3D_for_LL.py‎

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"""
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Plot a surface + points (clasts) on it
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Use matplotlib Axes3D.plot_surface and Axes3D.scatter3D
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Axes3D.plot_surface(X, Y, Z, *args, **kwargs)
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X, Y and Z are 2D arrays of similar shape.
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In our case, the plot is clearer if we don't plot boundary nodes
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(but that could bean option). So the shape is that of the LL grid
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core nodes.
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X = dx * [[1, 2, 3, ..., nb_of_columns-1]
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[1, 2, 3, ..., nb_of_columns-1]
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...
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[1, 2, 3, ..., nb_of_columns-1]]
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Y = dy * [[1, 1, 1, ..., 1]
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[2, 2, 2, ..., 2]
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...
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[nb_of_rows-1, ..., nb_of_rows-1]]
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Z = [[z_core_node1, z_core_node2, ...]
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[..., ... ]
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...
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[..., ]
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The rstride and cstride kwargs set the stride used to sample the input data
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to generate the graph. If 1k by 1k arrays are passed in, the default values
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for the strides will result in a 100x100 grid being plotted. Defaults to 10.
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The kwargs alpha sets the transparency factor (0 to 1).
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The LL nodes are at the crossing of the white lines that form the surface
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(the surface is made of LL patches, not cells).
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Axes3D.scatter(xs, ys, zs=0, zdir='z', s=20, c=None,
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depthshade=True, *args, **kwargs)
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xs, ys, zs are 1D arrays defining the position of the points.
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s = size (in points, so relation to the space scales of the plot itself is
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not straightforward...)
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c = color
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depthshade = Whether or not to shade the scatter markers to give the
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appearance of depth. Default is True.
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"""
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from landlab import RasterModelGrid
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import numpy as np
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import matplotlib.pyplot as plt
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from mpl_toolkits.mplot3d import Axes3D
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### Create Raster Model Grid
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rows = 20
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columns = 25
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dx = 1
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dy = 2
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mg = RasterModelGrid((rows, columns), spacing=(dy, dx))
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# Add elevation field
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z = mg.node_y*0.1
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_ = mg.add_field('node', 'topographic__elevation', z)
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# Shape of core nodes:
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# to be modified for more complex grids (use a mask?)
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number_of_core_node_rows = mg.number_of_node_rows - 2
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number_of_core_node_columns = mg.number_of_node_columns - 2
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#####################################################################
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### Data for 3D plot of topographic surface
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xplot = mg.node_x[mg.core_nodes].reshape((
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number_of_core_node_rows, number_of_core_node_columns))
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yplot = mg.node_y[mg.core_nodes].reshape((
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number_of_core_node_rows, number_of_core_node_columns))
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# Elevation of core nodes, reshaped for 3D plot:
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zplot = mg.at_node['topographic__elevation'][mg.core_nodes].reshape(
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(number_of_core_node_rows, number_of_core_node_columns))
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#####################################################################
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### 3D plot of elevation surface:
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# Figure and type of projection:
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fig = plt.figure(1)
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ax = plt.axes(projection='3d')
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# Plot surface:
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ax.plot_surface(xplot, yplot, zplot, cmap='binary', rstride=1, cstride=1,
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alpha=0.5)
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# Set initial view of the graph (elevation and azimuth):
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ax.view_init(elev=None, azim=-130)
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ax.set_xlabel('X axis')
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ax.set_ylabel('Y axis')
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ax.set_zlabel('Z axis')
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#####################################################################
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### Data for 3D plot of topographic surface
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# The Clast Set class (from Clast Tracker) provides
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# clast node ID, x and y coordinates, clast elevation, etc.
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# but for this example, we define them here:
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clast__node = np.array([382, 386, 386, 390, 392, 392, 392, 392])
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clast__number = clast__node.size
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clast__x = mg.node_x[clast__node]
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clast__y= mg.node_y[clast__node]
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clast__elevation = np.array([3, 3, 3, 3, 3, 3, 3, 3])
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clast__size = np.array([0.5, 0.5, 0.5, 1, 1, 1, 1, 1])
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# For display purpose, clasts sizes are increased:
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# Marker size is in "points"...
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sizes = clast__size * 100
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# Count the number of clast on each node (WILL PUT THIS IN THE CLAST TRACKER)
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clast__number_at_node = np.zeros(mg.number_of_nodes)
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for i in range(0, mg.number_of_nodes):
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clast__number_at_node[i] = list(clast__node).count(mg.nodes.reshape(
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clast__number_at_node.shape)[i])
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# Assign colors to clast markers as a function of clast density on the node:
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clast__color = np.zeros(clast__number)
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for j in range(0, clast__number):
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clast__color[j] = clast__number_at_node[clast__node[j]]
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#####################################################################
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### 3D plot of points (scatter):
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plot = ax.scatter(clast__x, clast__y, clast__elevation, marker='H',
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s=sizes, c=clast__color, label=clast__color, cmap='cool')
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cbar = plt.colorbar(plot, ticks=[1, 2, 3, 4], shrink=0.7)
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cbar.set_label('# of clasts')

‎CONTRIBUTING.md‎

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be rendered.
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* Your tutorial must be able to run without error for the **most
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recent landlab release**.
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* If your tutorial runs with the most recent **release** of landlab,
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a pull request should be sent to the *release* branch.
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* If you tutorial does not work with the most recent release but does
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work with a development version of landlab that's yet to be release,
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submit a pull request to the *next* branch.
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Thanks! :heart: :heart: :heart:
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‎README.md‎

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[![Landlab header](./landlab_header.png)](http://landlab.github.io)
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Most of these Landlab tutorials can either be read as text files or run as interactive IPython notebooks (recommended!).
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To run the IPython notebook tutorials locally, you can copy this [landlab/tutorials](https://github.com/landlab/tutorials) repo to your local working environment (use the ``download ZIP`` button or fork/clone, whichever is most familiar to you).
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Alternatively, you can also access each notebook online from [https://nbviewer.jupyter.org/github/landlab/tutorials](https://nbviewer.jupyter.org/github/landlab/tutorials) and download an individual notebook (navigate to the specific IPython notebook you want, open it, and click the download button that appears in the upper right).
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After downloading/cloning, navigate into your new directory (or to the directory containing your new download) from the command line in your terminal.
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Use the command ``$ jupyter notebook`` to launch Jupyter, the IPython notebook viewer (it will open locally in your browser). Then navigate to the ``.ipynb`` tutorial you want to run and click to open it.
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To run the code in the notebook, place your cursor in a code cell, hold down ``shift``, and press ``enter``. The order in which you run the cells matters. You can even experiment with typing your own code into the cell and running that.
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Here is a short IPython notebook tutorial along with a screencast (the tutorial uses an example with statistics, but you can substitute Landlab!): http://www.randalolson.com/2012/05/12/a-short-demo-on-how-to-use-ipython-notebook-as-a-research-notebook/
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Most of these Landlab tutorials can either be read as text files or run
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as interactive IPython notebooks (recommended!).
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To run the IPython notebook tutorials locally, you can copy this
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[landlab/tutorials](https://github.com/landlab/tutorials) repo to your
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local working environment (use the ``download ZIP`` button or fork/clone,
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whichever is most familiar to you).
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Alternatively, you can also access each notebook online from
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[https://nbviewer.jupyter.org/github/landlab/tutorials](https://nbviewer.jupyter.org/github/landlab/tutorials)
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and download an individual notebook (navigate to the specific IPython
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notebook you want, open it, and click the download button that appears
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in the upper right).
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After downloading/cloning, navigate into your new directory (or to
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the directory containing your new download) from the command line
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in your terminal.
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Use the command ``$ jupyter notebook`` to launch Jupyter, the IPython
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notebook viewer (it will open locally in your browser). Then navigate
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to the ``.ipynb`` tutorial you want to run and click to open it.
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To run the code in the notebook, place your cursor in a code cell,
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hold down ``shift``, and press ``enter``. The order in which you
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run the cells matters. You can even experiment with typing your own code
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into the cell and running that.
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Here is a short IPython notebook tutorial along with a screencast
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(the tutorial uses an example with statistics, but you can
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substitute Landlab!): http://www.randalolson.com/2012/05/12/a-short-demo-on-how-to-use-ipython-notebook-as-a-research-notebook/

‎boundary_conds/untitled.txt‎

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