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import numpy as np | ||
import pandas as pd | ||
from datetime import datetime | ||
import matplotlib.pyplot as plt | ||
from emcpy.plots.plots import LinePlot | ||
from emcpy.plots import CreatePlot | ||
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__all__ = ['plot_minimization'] | ||
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def _plot_cost_function(df, config, outdir): | ||
""" | ||
Use data from dataframe to plot the cost function. | ||
""" | ||
# Get cycle info | ||
cycle = df.index.get_level_values(0)[-1] | ||
cyclestr = datetime.strftime(cycle, '%Y%m%d%H') | ||
n_cycles = len(np.unique(df.index.get_level_values(0))) | ||
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# Create approriate dataframes | ||
current_cycle_df = df.loc[cycle] | ||
avg_df = df.groupby(level=[1, 2]).mean() | ||
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# Grab data | ||
j = current_cycle_df['J'].to_numpy() | ||
x = np.arange(len(j)) | ||
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avg_j = avg_df['J'].to_numpy() | ||
avg_x = np.arange(len(avg_j)) | ||
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# Create LinePlot objects | ||
cost_plot = LinePlot(x, j) | ||
cost_plot.linewidth = 2 | ||
cost_plot.label = 'Cost' | ||
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avg_cost_plot = LinePlot(avg_x, avg_j) | ||
avg_cost_plot.color = 'tab:red' | ||
avg_cost_plot.linewidth = 2 | ||
avg_cost_plot.label = f'Last {n_cycles} cycles Average' | ||
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# Create Plot | ||
myplot = CreatePlot() | ||
myplot.draw_data([cost_plot, avg_cost_plot]) | ||
myplot.set_yscale('log') | ||
myplot.add_grid() | ||
myplot.set_xlim(0, len(j)-1) | ||
myplot.add_xlabel('Iterations') | ||
myplot.add_ylabel('log (J)') | ||
myplot.add_legend(loc='upper right', | ||
fontsize='large') | ||
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title = f"{config['experiment']} Cost - {config['tm']}" | ||
myplot.add_title(title, loc='left') | ||
myplot.add_title(cyclestr, loc='right', | ||
fontweight='semibold') | ||
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fig = myplot.return_figure() | ||
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savefile = (f"{cyclestr}_{config['experiment']}_" + | ||
f"tm0{config['tm']}_cost_function.png") | ||
plt.savefig(outdir + savefile, bbox_inches='tight', | ||
pad_inches=0.1) | ||
plt.close('all') | ||
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def _plot_gnorm(df, config, outdir): | ||
""" | ||
Use date from dataframe to plot gnorm. | ||
""" | ||
# Get cycle info | ||
cycle = df.index.get_level_values(0)[-1] | ||
cyclestr = datetime.strftime(cycle, '%Y%m%d%H') | ||
n_cycles = len(np.unique(df.index.get_level_values(0))) | ||
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# Create approriate dataframes | ||
current_cycle_df = df.loc[cycle] | ||
avg_df = df.groupby(level=[1, 2]).mean() | ||
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# Grab data | ||
gJ = current_cycle_df['gJ'].to_numpy() | ||
gJ = np.log(gJ/gJ[0]) | ||
x = np.arange(len(gJ)) | ||
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avg_gJ = avg_df['gJ'].to_numpy() | ||
avg_gJ = np.log(avg_gJ/avg_gJ[0]) | ||
avg_x = np.arange(len(avg_gJ)) | ||
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# Create LinePlot objects | ||
gnorm = LinePlot(x, gJ) | ||
gnorm.linewidth = 2 | ||
gnorm.label = 'gnorm' | ||
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avg_gnorm = LinePlot(avg_x, avg_gJ) | ||
avg_gnorm.color = 'tab:red' | ||
avg_gnorm.linewidth = 2 | ||
avg_gnorm.linestyle = '--' | ||
avg_gnorm.label = f'Last {n_cycles} cycles Average' | ||
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# Create Plot | ||
myplot = CreatePlot() | ||
myplot.draw_data([gnorm, avg_gnorm]) | ||
# myplot.set_yscale('log') | ||
myplot.add_grid() | ||
myplot.set_xlim(0, len(gJ)-1) | ||
myplot.add_xlabel('Iterations') | ||
myplot.add_ylabel('log (gnorm)') | ||
myplot.add_legend(loc='upper right', | ||
fontsize='large') | ||
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title = f"{config['experiment']} gnorm - {config['tm']}" | ||
myplot.add_title(title, loc='left') | ||
myplot.add_title(cyclestr, loc='right', | ||
fontweight='semibold') | ||
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fig = myplot.return_figure() | ||
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savefile = (f"{cyclestr}_{config['experiment']}_" + | ||
f"tm0{config['tm']}_gnorm.png") | ||
plt.savefig(outdir + savefile, bbox_inches='tight', | ||
pad_inches=0.1) | ||
plt.close('all') | ||
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def plot_minimization(df, plotting_config, outdir): | ||
""" | ||
Plot minimization plots including gnorm and cost function | ||
and save them to outdir. | ||
Args: | ||
df : (pandas dataframe) dataframe with appropriate information | ||
from GSI Stat file | ||
plotting_config : (dict) dictionary with information about | ||
minimization period | ||
outdir : (str) path to output diagnostics | ||
""" | ||
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_plot_gnorm(df, plotting_config, outdir) | ||
_plot_cost_function(df, plotting_config, outdir) |
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