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# Bar Plot from SRO: from plot_fate_byOilAndLocation.ipynb
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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import yaml
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plt.style.use(r'extraction_from_rachaels.template')
Bad key xtick.labelcolor in file extraction_from_rachaels.template, line 22 ('xtick.labelcolor: inherit # color of the tick labels or inherit from xtick.color')
You probably need to get an updated matplotlibrc file from
https://github.com/matplotlib/matplotlib/blob/v3.3.3/matplotlibrc.template
or from the matplotlib source distribution
Bad key ytick.labelcolor in file extraction_from_rachaels.template, line 32 ('ytick.labelcolor: inherit # color of the tick labels or inherit from ytick.color')
You probably need to get an updated matplotlibrc file from
https://github.com/matplotlib/matplotlib/blob/v3.3.3/matplotlibrc.template
or from the matplotlib source distribution
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MIDOSScolors={
'ANS': 'darkslategrey',
'Bunker-C':'teal',
'Dilbit':'slategrey',
'Diesel':'darkgoldenrod',
}
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with open('SRO_files_try3_t2.yaml') as file:
sro = yaml.safe_load(file)
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df={}
df['ANS'] = pd.DataFrame(sro['akns'])
df['Bunker-C'] = pd.DataFrame(sro['bunker'])
df['Diesel'] = pd.DataFrame(sro['diesel'])
df['Dilbit'] = pd.DataFrame(sro['dilbit'])
oil_types = ['ANS', 'Bunker-C', 'Diesel', 'Dilbit']
locations = ['water','sfc','beach','air']
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final = {}
initial={}
for location in locations:
final[location]={}
for oil in oil_types:
final[location][oil]=[]
for oil in oil_types:
final['water'][oil] = (df[oil]['MDispersed'] +
df[oil]['MDissolved'] +
df[oil]['MBio'])
final['sfc'][oil] = df[oil]['MassOil']
final['beach'][oil] = df[oil]['MBeached']
final['air'][oil] = df[oil]['MEvaporated']
initial[oil]=df[oil]['MInitial']
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persist = {}
# Calculate Quantiles
persist['Q1']={}
persist['Q2']={}
persist['Q3']={}
for oil in ['ANS','Bunker-C','Diesel']:
persist['Q1'][oil]={}
persist['Q2'][oil]={}
persist['Q3'][oil]={}
for loc in locations:
#Quantile 0.25 (in percent form)
persist['Q1'][oil][loc]=100*np.quantile(
final[loc][oil]/(initial[oil]), .25)
#Quantile 0.5 (median, in percent form)
persist['Q2'][oil][loc]=100*np.quantile(
final[loc][oil]/(initial[oil]), .50)
#Quantile 0.75 (in percent form)
persist['Q3'][oil][loc]=100*np.quantile(
final[loc][oil]/(initial[oil]), .75)
persist_df = {}
for opp in ['Q1','Q2','Q3']:
persist_df[opp] = pd.DataFrame({
"ANS":[persist[opp]["ANS"]["water"], persist[opp]["ANS"]["sfc"], persist[opp]["ANS"]["beach"], persist[opp]["ANS"]["air"]],
"Bunker-C":[persist[opp]["Bunker-C"]["water"], persist[opp]["Bunker-C"]["sfc"], persist[opp]["Bunker-C"]["beach"], persist[opp]["Bunker-C"]["air"]],
"Diesel":[persist[opp]["Diesel"]["water"], persist[opp]["Diesel"]["sfc"], persist[opp]["Diesel"]["beach"], persist[opp]["Diesel"]["air"]]
},
index=["water", "surface", "beach", "air"]
)
persist_df[opp] = persist_df[opp].rename_axis(opp, axis=1)
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error_minxx = persist_df['Q2'] - persist_df['Q1']
error_maxxx = persist_df['Q3'] - persist_df['Q2']
print (error_minxx)
print (error_minxx.to_numpy())
Q2 ANS Bunker-C Diesel water 0.021132 0.000078 0.049216 surface 0.636309 0.341071 0.066490 beach 18.150134 11.264523 16.996533 air 1.693576 1.163776 8.503893 [[2.11317052e-02 7.76002149e-05 4.92162097e-02] [6.36309089e-01 3.41070740e-01 6.64895907e-02] [1.81501341e+01 1.12645232e+01 1.69965332e+01] [1.69357583e+00 1.16377621e+00 8.50389329e+00]]
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yerr_reorg = np.zeros((6, 4))
for ii in range(0, 6, 2):
jj = int(ii/2)
yerr_reorg[ii] = error_minxx.to_numpy()[:,jj]
yerr_reorg[ii+1] = error_maxxx.to_numpy()[:,jj]
print(yerr_reorg)
[[2.11317052e-02 6.36309089e-01 1.81501341e+01 1.69357583e+00] [2.50453882e-01 7.58300837e+00 3.39583924e+00 1.28769846e+00] [7.76002149e-05 3.41070740e-01 1.12645232e+01 1.16377621e+00] [2.38240787e-03 5.61651883e+00 3.37988162e+00 3.26644017e+00] [4.92162097e-02 6.64895907e-02 1.69965332e+01 8.50389329e+00] [1.29953371e+00 5.53670112e-01 9.77984958e+00 1.12220080e+01]]
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fig, ax = plt.subplots(1, 1, figsize=(5, 5))
persist_df['Q2'].plot(ax=ax,
kind="bar",
color=[MIDOSScolors["ANS"],MIDOSScolors["Bunker-C"],MIDOSScolors["Diesel"]],
legend=False,
yerr=yerr_reorg, #np.append(error_max, error_min, axis=0),
error_kw=dict(lw=3, capsize=4, capthick=3),
ecolor='lightsteelblue'
)
ax.set_ylabel("Fate (%)")
ax.legend(bbox_to_anchor=(1.5, 1), loc='upper right')
ax.set_ylim(0,100)
ax.set_yticks([0, 20, 40, 60, 80, 100])
ax.set_xticklabels(
["water\ncolumn", "water\nsurface", "coast", "air"],
rotation=45
)
plt.savefig('Fate_ByOilLocation_1panel_MedianErr.png')
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# For Jose
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from salishsea_tools import river_201702
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