Ancestor lengths in genome and time bins #68
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Some issues that remain with this PR:
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model.py
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| num_x_wins = int(np.ceil(nodes_right.max() - nodes_left.min()) / win_x_size) | ||
| num_y_wins = int(np.ceil(nodes_time.max() / win_y_size)) | ||
| heatmap_sums = np.zeros((num_x_wins, num_y_wins)) |
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I'm creating a numpy 2D array here as it's easier to keep track of bin indices, and then flattening the array later. Is this acceptable?
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Have you looked at using np.digitize to do the binning, rather than flooring things manually?
model.py
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| ) # map the node span to the x-axis bins it overlaps | ||
| x_end = int(np.floor(nodes_right[u] / win_x_size)) | ||
| y = max(0, int(np.floor(nodes_time[u] / win_y_size)) - 1) | ||
| heatmap_sums[x_start:x_end, y] += min( |
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min() operation is only really required for the first and last bins as they may not completely overlap the node span. Every other bin can just be summed by the window size.
ancestor_span_heatmap.mp4 |
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@savitakartik Would you like a review here now? |
Yes that would be great, thanks @benjeffery. |
model.py
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| num_x_wins = int(np.ceil(nodes_right.max() - nodes_left.min()) / win_x_size) | ||
| num_y_wins = int(np.ceil(nodes_time.max() / win_y_size)) | ||
| heatmap_sums = np.zeros((num_x_wins, num_y_wins)) |
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Have you looked at using np.digitize to do the binning, rather than flooring things manually?
model.py
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| @@ -551,3 +555,48 @@ def calc_mutations_per_tree(self): | |||
| mutations_per_tree = np.zeros(self.ts.num_trees, dtype=np.int64) | |||
| mutations_per_tree[unique_values] = counts | |||
| return mutations_per_tree | |||
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| def compute_ancestor_spans_heatmap_data(self, win_x_size=1_000_000, win_y_size=500): | |||
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Are these sizes of genome coordinates? If so, it's going to be tricky to make default that work across species (compare humans to sars 2, eg.).
What you using number of x-bins and number of y-bins instead? That way you can be sure of a given level of resolution and that you won't use too much memory.
Can use np.linspace to generate the bin coordinates.
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Can you rebase please @savitakartik to pull in the logging changes? |
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now done, @jeromekelleher. I will try to address the failing tests now. |
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I just looked at the plot for the Unified genealogy trees and it's not terribly informative. Let's have a chat about it tomorrow. |
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Recording.2023-09-24.104825.mp4 |
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I think this is very useful, @benjeffery can you help getting it merge please? |
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@savitakartik Looks good - but I think you need to increment the cache version for nodes. |
…unt on hover and plot titles added
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| @@ -449,6 +449,10 @@ def nodes_df(self): | |||
| "time": ts.nodes_time, | |||
| "num_mutations": self.nodes_num_mutations, | |||
| "ancestors_span": child_right - child_left, | |||
| "child_left": child_left, # FIXME add test for this | |||
| "child_right": child_right, # FIXME add test for this | |||
| "child_left": child_left, # FIXME add test for this | |||
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Also, looks like it's tested now?
| x_ends = np.digitize(nodes_right, x_bins, right=True) | ||
| y_starts = np.digitize(nodes_time, y_bins, right=True) | ||
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| for u in range(len(nodes_left)): |
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This is probably slow - should we do it with numba?
Addresses #20
Added
child_left,child_rightcolumns to nodes_df and tests for these.Changed computation of ancestor-spans-heatmap data to avoid iterate over nodes instead of bins.