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Plotter_AltitudeProfiles.py
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import Data as D
import numpy as np
import os
import math
import plotly
import chart_studio.plotly as py
import plotly.graph_objects as go
import plotly.figure_factory as ff
from plotly.subplots import make_subplots
def plotAltProfiles( VariableName, Buckets, SuperTitle="", ResultsFilename="" ):
'''
Creates a plot of altitude profiles of various percentiles for the variable in the parameters.
One sub-figure for each Magnetic Local Time - Kp index combination is created.
Args:
VariableName (string): The physical variable on which the calculation has been applied.
Buckets (dictionary): The data structure which contains the statistical calculation results. See the function Data.init_ResultDataStructure() for details.
SuperTitle (string): This title will be displayed at the top of the plot.
ResultsFilename (string): The netCDF filename into which the statistical calculation results will be stored.
'''
if VariableName == "Joule Heating" or "JH" in VariableName:
x_axes_range=[0, 20]
MultiplicationFactor = 10**8
new_units = "10^-8 W/m3"
elif VariableName == "Pedersen Conductivity":
x_axes_range=[0, 0.4]
MultiplicationFactor = 10**3
new_units = "mS/m"
else:
x_axes_range=[0, 10]
MultiplicationFactor = 1
new_units = "?"
# alter visibleALTsequence so that the point is displayed in the middle of the sub-bin
visibleALTsequence = D.ALTsequence.copy()
for i in range(1, len(visibleALTsequence)-1):
visibleALTsequence[i] += D.ALT_distance_of_a_bucket/2
visibleALTsequence[0] = D.ALTsequence[0]
visibleALTsequence[-1] = D.ALTsequence[-1] + D.ALT_distance_of_a_bucket
# plot
Color10 = '#c4dfe6'
Color25 = '#a1d6e2'
Color50 = '#1995ad'
Color75 = '#a1d6e2'
Color90 = '#c4dfe6'
# construct the column MLT titles #("0-3", "3-6", "6-9", "9-12", "12-15", "15-18", "18-21", "21-24")
ColumnTitles = list()
for i in range(0, len(D.MLTsequence)):
MLTfrom = int(D.MLTsequence[i])
if MLTfrom > 24: MLTfrom -=24
MLTto = int(D.MLTsequence[i]+D.MLT_duration_of_a_bucket)
if MLTto > 24: MLTto -=24
ColumnTitles.append( "MLT " + str(MLTfrom) + "-" + str(MLTto) )
# define secondary y-axis at the right of the plot
mySpecs = list()
for row in range(0, len(D.KPsequence)):
mySpecs.append( list() )
for col in range(0, len(D.MLTsequence)):
mySpecs[row].append( {"secondary_y": True} )
#make plot
if VariableName == "Joule Heating":
XXtitle = 'Joule heating (10<sup>-8</sup> W/m<sup>3</sup>)'
elif VariableName == "Pedersen conductivity":
XXtitle = 'Pedersen conductivity (mS/m)'
else:
XXtitle = VariableName
fig = make_subplots(rows=len(D.KPsequence), cols=len(D.MLTsequence), x_title=XXtitle, shared_xaxes=True, shared_yaxes=True, vertical_spacing=0.035, horizontal_spacing=0.02, subplot_titles=ColumnTitles, specs=mySpecs)
# set font sizes
fig.update_layout( font=dict( family="arial black", size=22 ) )
fig.update_annotations( font=dict( family="arial black", size=24 ) )
#fig.update_xaxes(title_font_family="Arial black", title_font_size=20)
#fig.update_yaxes(title_font_family="Arial black", title_font_size=20)
fig.update_xaxes(tickfont_size=22)
fig.update_yaxes(tickfont_size=22)
fig.layout.annotations[4]["font"] = {'size': 30} # this is the XXtitle at the bottom
figure_hits=0
for aKP in D.KPsequence:
for aMLT in D.MLTsequence:
#Means = list()
Percentiles10 = list()
Percentiles25 = list()
Percentiles50 = list()
Percentiles75 = list()
Percentiles90 = list()
hits = 0
# compute maximums of median
PanelMax = 0
Alt_of_Max = 0
for anALT in D.ALTsequence:
if PanelMax < Buckets[aKP, anALT, D.LAT_min, aMLT, "Percentile50"] * MultiplicationFactor:
PanelMax = Buckets[aKP, anALT, D.LAT_min, aMLT, "Percentile50"] * MultiplicationFactor
Alt_of_Max = anALT
#print( "MAXIMUM at", aKP, aMLT, ":", PanelMax, "at", Alt_of_Max, "km" )
#fig.add_annotation(xref='x domain', yref='y domain', x=0.97, y=0.90, text=F"max at <b>{int(Alt_of_Max)}-{int(Alt_of_Max+D.ALT_distance_of_a_bucket)}km</b>", showarrow=False, row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1, font=dict(color="red") )
# compute percentiles
for anALT in D.ALTsequence:
Percentiles10.append( Buckets[aKP, anALT, D.LAT_min, aMLT, "Percentile10"] * MultiplicationFactor )
Percentiles25.append( Buckets[aKP, anALT, D.LAT_min, aMLT, "Percentile25"] * MultiplicationFactor )
Percentiles50.append( Buckets[aKP, anALT, D.LAT_min, aMLT, "Percentile50"] * MultiplicationFactor )
Percentiles75.append( Buckets[aKP, anALT, D.LAT_min, aMLT, "Percentile75"] * MultiplicationFactor )
Percentiles90.append( Buckets[aKP, anALT, D.LAT_min, aMLT, "Percentile90"] * MultiplicationFactor )
fig.add_trace( go.Scatter(x=[0]*len(visibleALTsequence), y=visibleALTsequence, mode='lines', fill='tonexty', fillcolor=Color10, line=dict(color='gray',width=1,), showlegend=False), row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1 )
fig.add_trace( go.Scatter(x=Percentiles10, y=visibleALTsequence, mode='lines', fill='tonexty', fillcolor=Color10, line=dict(color='gray',width=1,), showlegend=False), row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1 )
fig.add_trace( go.Scatter(x=Percentiles25, y=visibleALTsequence, mode='lines', fill='tonexty', fillcolor=Color25, line=dict(color='gray',width=1,), showlegend=False), row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1 )
fig.add_trace( go.Scatter(x=Percentiles50, y=visibleALTsequence, mode='lines', fill='tonexty', fillcolor=Color50, line=dict(color='black',width=2,), showlegend=False), row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1 )
# plot mean
#fig.add_trace( go.Scatter(x=Means, y=visibleALTsequence, mode='lines', fill='tonexty', fillcolor='black', line=dict(color='black',width=1,), showlegend=False), row=KPsequence.index(aKP)+1, col=MLTsequence.index(aMLT)+1 )
# plot percentiles
fig.add_trace( go.Scatter(x=Percentiles75, y=visibleALTsequence, mode='lines', fill='tonexty', fillcolor=Color75, line=dict(color='gray',width=1,), showlegend=False), row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1 )
fig.add_trace( go.Scatter(x=Percentiles90, y=visibleALTsequence, mode='lines', fill='tonexty', fillcolor=Color90, line=dict(color='gray',width=1,), showlegend=False), row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1, )
# add a trace in order to display secondary y-axis at the right
fig.add_trace( go.Scatter(x=[-1000], y=[-1000], showlegend=False), row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1, secondary_y=True )
# ~~~~ display number of measurements for each sub-figure ~~~~
subfigure_hits = 0
fname = "results/" + ResultsFilename[0:-3] + "/" + str(aKP) + "_" + str(aMLT) + ".txt"
if os.path.exists( fname ):
f = open( fname, "r" )
subfigure_hits = int( f.read() )
f.close()
figure_hits += subfigure_hits
s = "N=" + format(subfigure_hits,'.1E')
fig.add_annotation(xref='x domain', yref='y domain', x=0.99, y=0.00, text=s, showarrow=False, row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1, font=dict(color="blue") )
fig.layout.annotations[-1]["font"] = {'family':'arial', 'size':18}
# CALCULATE the Height_integration-Vaue = area under median curve ~~~~~~~~~~~~~~~~~~~~~~~~~~
area50 = 0
for i in range(0, len(Percentiles50)):
if math.isnan(Percentiles50[i]) == False:
area50 += Percentiles50[i]*D.ALT_distance_of_a_bucket
#
if VariableName=="Joule Heating" or "JH" in VariableName:
area50 = area50 * 0.01 # area50*1000 * math.pow(10,-8) * 1000;
fig.add_annotation(xref='x domain', yref='y domain', x=0.99, y=1, text=F"<b>{round(area50,2)} mW/m<sup>2</sup></b>", showarrow=False, row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1, font=dict(color="royalblue") )
elif VariableName == "Pedersen Conductivity":
fig.add_annotation(xref='x domain', yref='y domain', x=0.99, y=1, text=F"<b>{round(area50,2)} S</b>", showarrow=False, row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1, font=dict(color="blue") )
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# display legends
'''
fig.add_trace( go.Scatter(name='10th Perc.', x=Percentiles10, y=visibleALTsequence, mode='lines', fill='tonexty', fillcolor=Color10, line=dict(color='gray',width=1,), showlegend=True), row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1 )
fig.add_trace( go.Scatter(name='25th Perc.', x=Percentiles25, y=visibleALTsequence, mode='lines', fill='tonexty', fillcolor=Color25, line=dict(color='gray',width=1,), showlegend=True), row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1 )
fig.add_trace( go.Scatter(name='50th Perc.', x=Percentiles50, y=visibleALTsequence, mode='lines', fill='tonexty', fillcolor=Color50, line=dict(color='black',width=2,), showlegend=True), row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1 )
#fig.add_trace( go.Scatter(name='Mean value', x=Means, y=visibleALTsequence, mode='lines', fill='tonexty', fillcolor='#5cc5ef', line=dict(color='black',width=1,), showlegend=True), row=KPsequence.index(aKP)+1, col=MLTsequence.index(aMLT)+1 )
fig.add_trace( go.Scatter(name='75th Perc.', x=Percentiles75, y=visibleALTsequence, mode='lines', fill='tonexty', fillcolor=Color75, line=dict(color='gray',width=1,), showlegend=True), row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1 )
fig.add_trace( go.Scatter(name='90th Perc.', x=Percentiles90, y=visibleALTsequence, mode='lines', fill='tonexty', fillcolor=Color90, line=dict(color='gray',width=1,), showlegend=True), row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1 )
'''
print( "TOTAL number of measruments for the figure N_total= ", figure_hits )
fig.update_xaxes( range=x_axes_range, row=1, col=1)
fig.update_xaxes( range=x_axes_range, row=1, col=2)
fig.update_xaxes( range=x_axes_range, row=1, col=3)
fig.update_xaxes( range=x_axes_range, row=1, col=4)
fig.update_xaxes( range=x_axes_range, row=2, col=1)
fig.update_xaxes( range=x_axes_range, row=2, col=2)
fig.update_xaxes( range=x_axes_range, row=2, col=3)
fig.update_xaxes( range=x_axes_range, row=2, col=4)
fig.update_xaxes( range=x_axes_range, row=3, col=1)
fig.update_xaxes( range=x_axes_range, row=3, col=2)
fig.update_xaxes( range=x_axes_range, row=3, col=3)
fig.update_xaxes( range=x_axes_range, row=3, col=4)
for aKP in D.KPsequence:
fig.update_yaxes( title_text="Altitude(km)", row=D.KPsequence.index(aKP)+1, col=1, side='left', secondary_y=False)
row_title = "Kp " + str(aKP) + " - "
if aKP == 0:
row_title += "2"
elif aKP == 2:
row_title += "4"
else:
row_title += "9"
fig.update_yaxes( title_text=row_title, row=D.KPsequence.index(aKP)+1, col=len(D.MLTsequence), side='right', secondary_y=True, showticklabels=False )
for aMLT in D.MLTsequence:
fig.update_yaxes( row=D.KPsequence.index(aKP)+1, col=D.MLTsequence.index(aMLT)+1, secondary_y=True, showticklabels=False )
fig.update_yaxes( range=[80, 150], tick0=90, dtick=20 )
fig.update_layout( title = 'TIE-GCM (2009-2019)', title_font_color=Color50,
width=400+len(D.MLTsequence)*250, height=200+200*len(D.KPsequence), showlegend=True, legend_orientation="h", legend_y=-0.04)
plotly.offline.init_notebook_mode(connected=True)
plotly.offline.iplot(fig)
# plot more zoom versions
'''
new_x_axes_range = [x * (2/3) for x in x_axes_range]
fig.update_xaxes( range=new_x_axes_range )
plotly.offline.iplot(fig)
new_x_axes_range = [x * (1/2) for x in x_axes_range]
fig.update_xaxes( range=new_x_axes_range )
plotly.offline.iplot(fig)
new_x_axes_range = [x * (3/2) for x in x_axes_range]
fig.update_xaxes( range=new_x_axes_range )
plotly.offline.iplot(fig)
new_x_axes_range = [x * (2.5) for x in x_axes_range]
fig.update_xaxes( range=new_x_axes_range )
plotly.offline.iplot(fig)
new_x_axes_range = [x * (10) for x in x_axes_range]
fig.update_xaxes( range=new_x_axes_range )
plotly.offline.iplot(fig)
'''