Python Plotly Subplots with an x shared axis and multiple y axis
I would like to know if it is possible to create subplots with Plotly with an x shared axis and multiple y axis. This what I want to do: where '##' means the plot
[y2, y1, ##, y3] [y5, y4, ##, y6] [ y7, ## ] [ y8, ##, y9] [ y10, ##, y11] [ y12, ## ] [ x ]
This is my code: I am using make_subplots with 6 rows and 1 col, then I definded 12 y axes with their relative position and I added 5 more x axis only to use anchor on my different y axes.
def plot_xshared(dfs):
df_flow_tmp, df_watlow, df_pico, df_flow, df_UV, df_UV_RMS, df_UV_hide, df_P, df_IR, df_IR_RMS, df_IR_hide, df_Pd, df_nearfield, df_farfield = dfs
w, yd = xyaxes_dom_yaxes_pos(gap=0, rows=6)
color_UV=['#19D3F3']
color_UV_RMS=['#636EFA']
color_UV_hide=['brown', 'black', 'blue']
color_P=['#AB63FA']
color_IR=['#EF553B']
color_RMS=['#FFA15A']
color_IR_hide=['brown', 'black', 'blue']
color_Pd=['#636EFA']
color_Position=['blue', 'red']
color_width=['blue', 'red']
fig = make_subplots(rows=6, cols=1,
shared_xaxes=True
)
fig.update_layout(showlegend=True,
legend=dict(groupclick='toggleitem'),
legend_title_text='Legend',
xaxis1=dict(anchor='y1',
domain=[w[1],w[-2]]
), #common x shared axis
yaxis1=dict(title='Energy (mJ)',
anchor='x1',
overlaying='y1',
side='left',
domain=yd[0]
), #UV
yaxis2=dict(title='RMS ()',
anchor='free',
overlaying='y1',
side='left',
position=w[0]
), #UV
yaxis3=dict(title='Power (W)',
anchor='x1',
overlaying='y1',
side='right'
), #UV
xaxis2=dict(anchor='y4',
domain=[w[1],w[-2]]
),
yaxis4=dict(title='Energy (mJ)',
anchor='x2',
overlaying='y4',
side='left',
domain=yd[1]
), #IR
yaxis5=dict(title='RMS ()',
anchor='free',
overlaying='y4',
side='left',
position=w[0]
), #IR
yaxis6=dict(title='Power (W)',
anchor='x2',
overlaying='y4',
side='right'
), #IR
xaxis3=dict(anchor='y7',
domain=[w[1],w[-2]]
),
yaxis7=dict(title='Temperature (°C)',
anchor='x3',
overlaying='y7',
side='left',
domain=yd[2]
), #temperature
xaxis4=dict(anchor='y8',
domain=[w[1],w[-2]]
),
yaxis8=dict(title='Position ()',
anchor='x4',
overlaying='y8',
side='left',
domain=yd[3]
), #far field
yaxis9=dict(title='Width ()',
anchor='x4',
overlaying='y8',
side='right'
), #far field
xaxis5=dict(anchor='y10',
domain=[w[1],w[-2]]
),
yaxis10=dict(title='Position ()',
anchor='x5',
overlaying='y10',
side='left',
domain=yd[4]
), #near field
yaxis11=dict(title='Width ()',
anchor='x5',
overlaying='y10',
side='right'
), #near field
xaxis6=dict(anchor='y12',
domain=[w[1],w[-2]]
),
yaxis12=dict(title='Flow ()',
anchor='x6',
overlaying='y12',
side='left',
domain=yd[5]
) #flow
)
### UV ###
#Energy
for i in range(len(df_UV.columns)):
fig.add_trace(go.Scatter(x=df_UV.index,
y=df_UV[df_UV.columns[i]],
name=('%s' %(df_UV.columns[i])),
xaxis='x1',
yaxis='y1',
line=dict(color=color_UV[i]),
legendgroup='UV',
legendgrouptitle_text='UV'
),
row=1,
col=1
)
#RMS
fig.add_trace(go.Scatter(x=df_UV_RMS.index,
y=df_UV_RMS[df_UV_RMS.columns[0]],
name=('%s' %(df_UV_RMS.columns[i])),
xaxis='x1',
yaxis='y2',
line=dict(color=color_UV_RMS[i]),
legendgroup='UV',
legendgrouptitle_text='UV'
),
row=1,
col=1
)
#Mean, Max, Min
for i in range(len(df_UV_hide.columns)):
fig.add_trace(go.Scatter(x=df_UV_hide.index,
y=df_UV_hide[df_UV_hide.columns[i]],
name=('%s' %(df_UV_hide.columns[i])),
xaxis='x1',
yaxis='y1',
line=dict(color=color_UV_hide[i]),
visible='legendonly',
legendgroup='UV',
legendgrouptitle_text='UV'
),
row=1,
col=1
)
#Power
for i in range(len(df_P.columns)):
fig.add_trace(go.Scatter(x=df_P.index,
y=df_P[df_P.columns[i]],
name=('%s' %(df_P.columns[i])),
xaxis='x1',
yaxis='y3',
line=dict(color=color_P[i]),
legendgroup='UV',
legendgrouptitle_text='UV'
),
row=1,
col=1
)
### IR ###
#Energy
for i in range(len(df_IR.columns)):
fig.add_trace(go.Scatter(x=df_IR.index,
y=df_IR[df_IR.columns[i]],
name=('%s' %(df_IR.columns[i])),
xaxis='x2',
yaxis='y4',
line=dict(color=color_IR[i]),
legendgroup='IR',
legendgrouptitle_text='IR'
),
row=2,
col=1
)
#RMS
fig.add_trace(go.Scatter(x=df_IR_RMS.index,
y=df_IR_RMS[df_IR_RMS.columns[0]],
name='IR RMS',
xaxis='x2',
yaxis='y5',
line=dict(color=color_RMS[0]),
legendgroup='IR',
legendgrouptitle_text='IR'
),
row=2,
col=1
)
#Mean, Max, Min
for i in range(len(df_IR_hide.columns)):
fig.add_trace(go.Scatter(x=df_IR_hide.index,
y=df_IR_hide[df_IR_hide.columns[i]],
name=('%s' %(df_IR_hide.columns[i])),
xaxis='x2',
yaxis='y4',
line=dict(color=color_IR_hide[i]),
visible='legendonly',
legendgroup='IR',
legendgrouptitle_text='IR'
),
row=2,
col=1
)
#Depolar power
for i in range(len(df_Pd.columns)):
fig.add_trace(go.Scatter(x=df_Pd.index,
y=df_Pd[df_Pd.columns[i]],
name=('%s' %(df_Pd.columns[i])),
xaxis='x2',
yaxis='y6',
line=dict(color=color_Pd[i]),
legendgroup='IR',
legendgrouptitle_text='IR'
),
row=2,
col=1
)
### TEMPERATURE ###
#Flowmeter
for i in range(len(df_flow_tmp.columns)):
fig.add_trace(go.Scatter(x=df_flow_tmp.index,
y=df_flow_tmp[df_flow_tmp.columns[i]],
name=('%s' %(df_flow_tmp.columns[i])),
xaxis='x3',
yaxis='y7',
legendgroup='Temperature',
legendgrouptitle_text='Temperature'
),
row=3,
col=1
)
#Watlow
for i in range(len(df_watlow.columns)):
fig.add_trace(go.Scatter(x=df_watlow.index,
y=df_watlow[df_watlow.columns[i]],
name=('%s' %(df_watlow.columns[i])),
xaxis='x3',
yaxis='y7',
legendgroup='Temperature',
legendgrouptitle_text='Temperature'
),
row=3,
col=1
)
#Picolog
for i in range(len(df_pico.columns)):
fig.add_trace(go.Scatter(x=df_pico.index,
y=df_pico[df_pico.columns[i]],
name=('%s' %(df_pico.columns[i])),
xaxis='x3',
yaxis='y7',
legendgroup='Temperature',
legendgrouptitle_text='Temperature'
),
row=3,
col=1
)
### FAR FIELD ###
#Position x
fig.add_trace(go.Scatter(x=df_farfield.index,
y=df_farfield['Position x'],
name='Position x - Far Field',
xaxis='x4',
yaxis='y8',
line=dict(color=color_Position[0]),
legendgroup='Far Field',
legendgrouptitle_text='Far Field',
),
row=4,
col=1
)
#Position y
fig.add_trace(go.Scatter(x=df_farfield.index,
y=df_farfield['Position y'],
name='Position y - Far Field',
xaxis='x4',
yaxis='y8',
line=dict(color=color_Position[1]),
legendgroup='Far Field',
legendgrouptitle_text='Far Field',
),
row=4,
col=1
)
#Width x
fig.add_trace(go.Scatter(x=df_farfield.index,
y=df_farfield['1/e2 X width'],
name='1/e2 x - Far Field',
xaxis='x4',
yaxis='y9',
line=dict(color=color_width[0]),
legendgroup='Far Field',
legendgrouptitle_text='Far Field',
),
row=4,
col=1
)
#Width y
fig.add_trace(go.Scatter(x=df_farfield.index,
y=df_farfield['1/e2 Y width'],
name='1/e2 y - Far Field',
xaxis='x4',
yaxis='y9',
line=dict(color=color_width[1]),
legendgroup='Far Field',
legendgrouptitle_text='Far Field',
),
row=4,
col=1
)
### NEAR FIELD ###
#Position x
fig.add_trace(go.Scatter(x=df_nearfield.index,
y=df_nearfield['Position x'],
name='Position x - Near Field',
xaxis='x5',
yaxis='y10',
line=dict(color=color_Position[0]),
legendgroup='Near Field',
legendgrouptitle_text='Near Field',
),
row=5,
col=1
)
#Position y
fig.add_trace(go.Scatter(x=df_nearfield.index,
y=df_nearfield['Position y'],
name='Position y - Near Field',
xaxis='x5',
yaxis='y10',
line=dict(color=color_Position[1]),
legendgroup='Near Field',
legendgrouptitle_text='Near Field',
),
row=5,
col=1
)
#Width x
fig.add_trace(go.Scatter(x=df_nearfield.index,
y=df_nearfield['1/e2 X width'],
name='1/e2 x - Near Field',
xaxis='x5',
yaxis='y11',
line=dict(color=color_width[0]),
legendgroup='Near Field',
legendgrouptitle_text='Near Field',
),
row=5,
col=1
)
#Width y
fig.add_trace(go.Scatter(x=df_nearfield.index,
y=df_nearfield['1/e2 Y width'],
name='1/e2 y - Near Field',
xaxis='x5',
yaxis='y11',
line=dict(color=color_width[1]),
legendgroup='Near Field',
legendgrouptitle_text='Near Field',
),
row=5,
col=1
)
### FLOW ###
for i in range(len(df_flow.columns)):
fig.add_trace(go.Scatter(x=df_flow.index,
y=df_flow[df_flow.columns[i]],
name=('%s' %(df_flow.columns[i])),
xaxis='x6',
yaxis='y12',
legendgroup='Flow',
legendgrouptitle_text='Flow'
),
row=6,
col=1
)
fig.show()
return 1
And this my code to calculate the position:
#function that returns default yaxis domain for each subplot and the additional yaxes positions
def xyaxes_dom_yaxes_pos(gap, rows):
if rows < 2:
raise ValueError('This function works for subplots with rows>2 and cols=1')
h_window= (1-gap)/rows #window height
d = 3/10/2
#xaxis{k} has the domain [w[2],w[-3]] k=1,...rows
#w[1], w[-2] give the left, resp right yaxes position associated to the default yaxis of the plot window
yd = []
for k in range(rows):
start = k*(h_window+gap)
end = start+h_window
yd.append([start, end])
w = [0, d, 2*d, 1-2*d, 1-d, 1]
return w, yd[::-1] #yd[::-1] contains the domains of the default yaxes
Finally, I got this:
Thank you for your advice and help.
do you know?
how many words do you know
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How can i remove the "figure" which is located above the figuretitle?
Thanks in advance for your answers!
fig = plt.figure(figsize=(50/2.54,40/2.54)) fig.suptitle(f'Features {list_required_colums[:3]}',fontsize=15) ax = fig.add_subplot(221, projection='3d') ax.scatter(Frame_Datas[list_required_colums[0]] , Frame_Datas[list_required_colums[1]] ,Frame_Datas[list_required_colums[2]] ,c=Frame_Datas.color_labels, alpha = 0.5, s=15) ax.set_xlabel(list_required_colums[0],labelpad=10, fontsize=13) ax.set_ylabel(list_required_colums[1],labelpad=10, fontsize=13) ax.set_zlabel(list_required_colums[2], labelpad=10,fontsize=13) ax.legend(handles=legend_elements, loc='upper right',ncol=1,bbox_to_anchor=(1.0, 0.75)) ax.set_xticks([]) ax.tick_params(axis='both', which='major', labelsize=10) ax.yaxis._axinfo['tick']['inward_factor'],ax.zaxis._axinfo['tick']['inward_factor'],ax.xaxis._axinfo['tick']['inward_factor']=2,2,2 ax.yaxis._axinfo['tick']['outward_factor'],ax.zaxis._axinfo['tick']['outward_factor'],ax.xaxis._axinfo['tick']['outward_factor']=2,2,2 ax.view_init(0,0) ax2 = fig.add_subplot(222, projection='3d') ax2.scatter(Frame_Datas[list_required_colums[0]] , Frame_Datas[list_required_colums[1]] ,Frame_Datas[list_required_colums[2]] , c=Frame_Datas.color_labels, alpha = 0.5, s=15) ax2.yaxis._axinfo['tick']['inward_factor'],ax2.zaxis._axinfo['tick']['inward_factor'],ax2.xaxis._axinfo['tick']['inward_factor']=2,0.2,2 ax2.yaxis._axinfo['tick']['outward_factor'],ax2.zaxis._axinfo['tick']['outward_factor'],ax2.xaxis._axinfo['tick']['outward_factor']=2,0.2,2 ax2.set_xlabel(list_required_colums[0],labelpad=10, fontsize=13) ax2.set_ylabel(list_required_colums[1],labelpad=10, fontsize=13) ax2.set_zlabel(list_required_colums[2],labelpad=10, fontsize=13) ax2.legend(handles=legend_elements, loc='upper right',ncol=1,bbox_to_anchor=(0.18, 0.75)) ax2.set_yticks([]) ax2.tick_params(axis='both', which='major', labelsize=10) ax2.zaxis._axinfo['juggled'] = (1,2,0) ax2.view_init(0,90) ax3 = fig.add_subplot(223, projection='3d') ax3.scatter(Frame_Datas[list_required_colums[0]] , Frame_Datas[list_required_colums[1]] ,Frame_Datas[list_required_colums[2]] , c=Frame_Datas.color_labels, alpha = 0.5, s=15) ax3.yaxis._axinfo['tick']['inward_factor'],ax3.zaxis._axinfo['tick']['inward_factor'],ax3.xaxis._axinfo['tick']['inward_factor']=0.2,0.2,0.2 ax3.yaxis._axinfo['tick']['outward_factor'],ax3.zaxis._axinfo['tick']['outward_factor'],ax3.xaxis._axinfo['tick']['outward_factor']=0.2,0.2,0.2 ax3.set_xlabel(list_required_colums[0],labelpad=10, fontsize=13) ax3.set_ylabel(list_required_colums[1],labelpad=10, fontsize=13) ax3.set_zlabel(list_required_colums[2],labelpad=10, fontsize=13) ax3.legend(handles=legend_elements, loc='upper right',ncol=1) ax3.tick_params(axis='both', which='major', labelsize=10) ax3.view_init(30,30) ax4 = fig.add_subplot(224, projection='3d') ax4.scatter(Frame_Datas[list_required_colums[0]] , Frame_Datas[list_required_colums[1]] ,Frame_Datas[list_required_colums[2]] , c=Frame_Datas.color_labels, alpha = 0.5, s=15) ax4.yaxis._axinfo['tick']['inward_factor'],ax4.zaxis._axinfo['tick']['inward_factor'],ax4.xaxis._axinfo['tick']['inward_factor']=0.2,0.2,0.2 ax4.yaxis._axinfo['tick']['outward_factor'],ax4.zaxis._axinfo['tick']['outward_factor'],ax4.xaxis._axinfo['tick']['outward_factor']=0.2,0.2,0.2 ax4.set_xlabel(list_required_colums[0],labelpad=10, fontsize=13) ax4.set_ylabel(list_required_colums[1],labelpad=10, fontsize=13) ax4.set_zlabel(list_required_colums[2],labelpad=10, fontsize=13) ax4.legend(handles=legend_elements, loc='upper right',ncol=1) ax4.view_init(30,140)
