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2f5ce3dc01
* horizontally put the bar figures * 1) use rangebreaks to handle gaps in datetime axis instead of make them string; 2) allow simultaneously plot rankic in ic_figure * pylint improvement * fix black lint * better axis formatting * default not show gaps * resolve doc built error * fix pylint * Update qlib/contrib/report/analysis_model/analysis_model_performance.py More detailed description Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * Update qlib/contrib/report/analysis_model/analysis_model_performance.py for Python backward compatibility Co-authored-by: you-n-g <you-n-g@users.noreply.github.com> * add doc string * fix black * 1) limit numpy version as numba support for 1.24+ has not been released; 2) no need to use custom numba version for pytest. Co-authored-by: you-n-g <you-n-g@users.noreply.github.com>
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import matplotlib.pyplot as plt
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import pandas as pd
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def sub_fig_generator(sub_fs=(3, 3), col_n=10, row_n=1, wspace=None, hspace=None, sharex=False, sharey=False):
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"""sub_fig_generator.
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it will return a generator, each row contains <col_n> sub graph
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FIXME: Known limitation:
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- The last row will not be plotted automatically, please plot it outside the function
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Parameters
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----------
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sub_fs :
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the figure size of each subgraph in <col_n> * <row_n> subgraphs
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col_n :
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the number of subgraph in each row; It will generating a new graph after generating <col_n> of subgraphs.
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row_n :
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the number of subgraph in each column
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wspace :
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the width of the space for subgraphs in each row
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hspace :
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the height of blank space for subgraphs in each column
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You can try 0.3 if you feel it is too crowded
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Returns
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-------
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It will return graphs with the shape of <col_n> each iter (it is squeezed).
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"""
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assert col_n > 1
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while True:
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fig, axes = plt.subplots(
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row_n, col_n, figsize=(sub_fs[0] * col_n, sub_fs[1] * row_n), sharex=sharex, sharey=sharey
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)
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plt.subplots_adjust(wspace=wspace, hspace=hspace)
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axes = axes.reshape(row_n, col_n)
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for col in range(col_n):
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res = axes[:, col].squeeze()
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if res.size == 1:
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res = res.item()
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yield res
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plt.show()
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def guess_plotly_rangebreaks(dt_index: pd.DatetimeIndex):
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"""
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This function `guesses` the rangebreaks required to remove gaps in datetime index.
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It basically calculates the difference between a `continuous` datetime index and index given.
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For more details on `rangebreaks` params in plotly, see
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https://plotly.com/python/reference/layout/xaxis/#layout-xaxis-rangebreaks
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Parameters
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----------
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dt_index: pd.DatetimeIndex
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The datetimes of the data.
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Returns
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-------
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the `rangebreaks` to be passed into plotly axis.
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"""
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dt_idx = dt_index.sort_values()
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gaps = dt_idx[1:] - dt_idx[:-1]
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min_gap = gaps.min()
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gaps_to_break = {}
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for gap, d in zip(gaps, dt_idx[:-1]):
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if gap > min_gap:
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gaps_to_break.setdefault(gap - min_gap, []).append(d + min_gap)
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return [dict(values=v, dvalue=int(k.total_seconds() * 1000)) for k, v in gaps_to_break.items()]
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