Toy model view #16
Toy model view #16
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Updated pan and zoom
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I think this is an awesome first pass!
Overall summary
- Great to see the iterative version of the coordinate calculations working! Just to need to get that refactored a little and it should be good to go!
- The ToyModel.py should be merged into model.py, so that we don't have two different model classes floating around
- The view model could be fleshed out a little bit to enable more flexibility. I'd even suggest testing this out on a small example (see here) and try to decorate tree according to the traits under metadata.tsv
- We'll want to have some unittests to sanity check these methods.
Great work!
| from skbio import TreeNode | ||
| import pandas as pd | ||
| import numpy as np | ||
| class ToyModel(TreeNode): |
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In order to reduce code redundancy, I suggest
- Move this into model.py
- Rename this to Model, rather than ToyModel.
| #Needs to be cleaned up | ||
| #Note: This function assumes a node can have n-children | ||
| # I believe we are only going to use bifurcating trees | ||
| # which means this code can be cleaned up quite a bit |
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Right, I'd suggest cleaning this up. I think the preorder method will help simply this, as it can emulate a stack to perform depth-first search.
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| class Model(object): |
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Let's move all of the class methods within ToyModel into here.
| self.mask = np.array() | ||
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| # Coordinate manipulation | ||
| def layout(self, layout_type): |
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We can move the contents within the coords method here.
| import numpy as np | ||
| import matplotlib.pyplot as plt | ||
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| def plot(node_metadata, edge_metadata): |
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Can we have this method accept an axes object? Preferably something similar to the standards in seaborn (see the boxplot example here)
The advantage of doing this is that we can enable multiple subplots -- we can embed trees alongside heatmaps, boxplots, scatter plots, and many other useful visualizations.
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Along the same lines, I'd suggest looking at the boxplot API to see how colors are specified.
For example, it would be extremely useful if the following API were available
def plot(node_metadata, edge_metadata,
node_color=None, node_size=None,
node_alpha=None, edge_color=None,
edge_width=None, edge_alpha=None):
"""
Parameters
-----------
node_metadata : pd.DataFrame
Contains all of the species attributes.
Every row corresponds to a unique species
and every column corresponds to an attribute.
Metadata may also contain ancestors.
edge_metadata : pd.DataFrame
Contains all of the edge attributes.
Every row corresponds to a unique edge
and every column corresponds to an attribute.
node_color : str
Name of column in `node_metadata` to plot node colors. If None, all nodes will be colored black.
node_size : str
Name of column in `node_metadata` to resize the nodes. If None, all nodes will be 10.
node_alpha : str
Name of column in `node_metadata` to specify transparency of nodes. If None, the nodes won't be transparent.
edge_color : str
Name of column in `edge_metadata` to plot edge colors. If None, all edges will be colored black.
edge_size : str
Name of column in `edge_metadata` to resize the nodes. If None, all edge will have a width of 1.
edge_alpha : str
Name of column in `edge_metadata` to specify transparency of edges. If None, all edges won't be transparent.
ax : matplotlib.axes.Axes
optional matplotlib axes object
Returns
--------
matplotlib.axes.Axes
Axes with fully rendered tree
"""| col_node_x = node_metadata['x'] | ||
| col_node_y = node_metadata['y'] | ||
| # TODO: annotation on points | ||
| plt.scatter(col_node_x,col_node_y) |
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I suggest swapping this out with an matplotlib Axes object. This will not only make the code more modular, but it will allow for multiple plots to coexist (plt is a global variable and should be avoided when making visualization libraries).
| Contains all of the edge attributes. | ||
| Every row corresponds to a unique edge | ||
| and every column corresponds to an attribute. | ||
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I'd recommend returning an axes object here
This is resolving issue #7 and #8