My goal is to create an animation with my 3D data in plotly.
I have 3 variables x,y,z for simplicity and I plot the 4th value depending on these x,y,z.
I create a 3D scatter plot where the 4th dim sort to speak is the color like this:
from numpy import genfromtxt
import numpy as np
import plotly.io as pio
import plotly.express as px
pio.renderers.default = 'notebook'
import plotly.graph_objects as go
import math
import pandas as pd
data = pd.read_csv("paramtp_1e-05_big.txt")
data.head()
data = data.iloc[::10, :]
color_data = data['gopt'].astype(float).round(decimals=2)
color_data[color_data>= 10] = 10
color_data_nopt = data['nopt'].astype(float).round(decimals=3)
color_data_mc = data['mc'].astype(float).round(decimals=3)
color_data_P= data['P']
color_data_P[color_data_P >= 1] = 1
data= data.replace(np.nan, '', regex=True)
data.tail()
fig = px.scatter_3d(data, x='NpN0', y='s', z='mu',log_x=True, log_z=True,
opacity = 0.5,
color=color_data,color_continuous_scale=px.colors.sequential.Viridis)
fig.add_trace(
go.Scatter(
mode='markers',
marker=dict(
size=1,
opacity=0.5,
),
)
)
fig.show()
Similarly to this wonderful animation: https://plotly.com/python/visualizing-mri-volume-slices/
I would like to slice up my data to isosurfaces with respect to any x,y,z coordinates.
As in the example they use images, I could not wrap my head around to create the same with my raw data.
Thank you in advance.
Related
I've got a simple plotly line graph:
import plotly.express as px
fig = px.line(data, x="x-axis", y="variable")
fig.show()
I want to add data labels displaying each y-axis value to each point, but I can't work out how to do it using the plotly api. Is it possible? Can anyone point out how?
have simulated dataframe for your figure
two steps
define text parameter so trace / figure is built appropriately by Plotly Express
updated texttemplate so that formatting of y-axis is used
import plotly.express as px
import pandas as pd
import numpy as np
data = pd.DataFrame(
{
"x-axis": np.arange(0, 12),
"variable": (np.cos(np.linspace(np.pi / 2, np.pi, 12)) + 1) / 25,
}
)
fig = px.line(data, x="x-axis", y="variable", text="variable")
fig.update_traces(texttemplate="%{y}")
fig.update_layout(yaxis_tickformat=".2%")
I want to make a histogram with this style:
But using plotly in Python. I.e. I want to merge the bars and plot only the contour. I am using this code:
import plotly.graph_objects as go
import numpy as np
x = np.random.randn(500)
fig = go.Figure(data=[go.Histogram(x=x)])
fig.show()
I have been looking for examples on how to do this but could not find any.
Your best option is to handle the histogram with numpy like count, index = np.histogram(df['data'], bins=25) , and then use go.Scatter() and set the linetype to horizontal, vertical, horizontal with line=dict(width = 1, shape='hvh'). Take a look at the very last section why go.Histogram() will not be your best option. With a few other specifications for the layout of go.Scatter(), the snippet below will produce the following plot:
Complete code
import plotly.graph_objects as go
import pandas as pd
import numpy as np
import plotly.io as pio
import plotly.express as px
pio.templates.default = "plotly_white"
# random numbers to a df
np.random.seed(12)
df = pd.DataFrame({'data': np.random.randn(500)})
# produce histogram data wiht numpy
count, index = np.histogram(df['data'], bins=25)
# plotly, go.Scatter with line shape set to 'hvh'
fig = go.Figure()
fig.add_traces(go.Scatter(x=index, y = count,
line=dict(width = 1, shape='hvh')))
# y-axis cosmetics
fig.update_yaxes(
showgrid=False,
ticks="inside",
tickson="boundaries",
ticklen=10,
showline=True,
linewidth=1,
linecolor='black',
mirror=True,
zeroline=False)
# x-axis cosmetics
fig.update_xaxes(
showgrid=False,
ticks="inside",
tickson="boundaries",
ticklen=10,
showline=True,
linewidth=1,
linecolor='black',
mirror=True,
zeroline=False)
fig.show()
Why go.Scatter() and not go.Histogram()?
The closest you'll get to your desired plot using your approach with fig = go.Figure(data=[go.Histogram(x=x)]) is this:
And that's pretty close, but you specifically wanted to exclude the vertical lines for each "bar". And I have yet not found a way to exclude or hide them with the go.Histogram setup.
Code for go.Histogram()
import plotly.graph_objects as go
import pandas as pd
import numpy as np
import plotly.io as pio
import plotly.express as px
pio.templates.default = "plotly_white"
import numpy as np
x = np.random.randn(500)
fig = go.Figure(data=[go.Histogram(x=x)])
fig.update_traces(marker=dict(color='rgba(0,0,0,0)', line=dict(width=1, color='blue')))
fig.show()
for a variation plotly.go.Histogram(): Show only horizontal lines of distribution. Plot just the lines
using pandas instead of numpy to build data for histogram then plotting as a line scatter
import plotly.graph_objects as go
import numpy as np
import pandas as pd
x = np.random.randn(100)
# build data frame that is histogram
df = pd.cut(x, bins=10).value_counts().to_frame().assign(
l=lambda d: pd.IntervalIndex(d.index).left,
r=lambda d: pd.IntervalIndex(d.index).right,
).sort_values(["l","r"]).rename(columns={0:"y"}).astype(float)
# lines in plotly are delimited by none
def line_array(df, cols):
return np.pad(
df.loc[:, cols].values, [(0, 0), (0, 1)], constant_values=None
).reshape(1, (len(df) * 3))[0]
# plot just lines
go.Figure(go.Scatter(x=line_array(df, ["l","r"]), y=line_array(df, ["y","y"]), marker={"color":"black"}))
I have several histograms that I succeded to plot using plotly like this:
fig.add_trace(go.Histogram(x=np.array(data[key]), name=self.labels[i]))
I would like to create something like this 3D stacked histogram but with the difference that each 2D histogram inside is a true histogram and not just a hardcoded line (my data is of the form [0.5 0.4 0.5 0.7 0.4] so using Histogram directly is very convenient)
Note that what I am asking is not similar to this and therefore also not the same as this. In the matplotlib example, the data is presented directly in a 2D array so the histogram is the 3rd dimension. In my case, I wanted to feed a function with many already computed histograms.
The snippet below takes care of both binning and formatting of the figure so that it appears as a stacked 3D chart using multiple traces of go.Scatter3D and np.Histogram.
The input is a dataframe with random numbers using np.random.normal(50, 5, size=(300, 4))
We can talk more about the other details if this is something you can use:
Plot 1: Angle 1
Plot 2: Angle 2
Complete code:
# imports
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import plotly.io as pio
pio.renderers.default = 'browser'
# data
np.random.seed(123)
df = pd.DataFrame(np.random.normal(50, 5, size=(300, 4)), columns=list('ABCD'))
# plotly setup
fig=go.Figure()
# data binning and traces
for i, col in enumerate(df.columns):
a0=np.histogram(df[col], bins=10, density=False)[0].tolist()
a0=np.repeat(a0,2).tolist()
a0.insert(0,0)
a0.pop()
a1=np.histogram(df[col], bins=10, density=False)[1].tolist()
a1=np.repeat(a1,2)
fig.add_traces(go.Scatter3d(x=[i]*len(a0), y=a1, z=a0,
mode='lines',
name=col
)
)
fig.show()
Unfortunately you can't use go.Histogram in a 3D space so you should use an alternative way. I used go.Scatter3d and I wanted to use the option to fill line doc but there is an evident bug see
import numpy as np
import plotly.graph_objs as go
# random mat
m = 6
n = 5
mat = np.random.uniform(size=(m,n)).round(1)
# we want to have the number repeated
mat = mat.repeat(2).reshape(m, n*2)
# and finally plot
x = np.arange(2*n)
y = np.ones(2*n)
fig = go.Figure()
for i in range(m):
fig.add_trace(go.Scatter3d(x=x,
y=y*i,
z=mat[i,:],
mode="lines",
# surfaceaxis=1 # bug
)
)
fig.show()
I get different results when trying to plot the identical data with mathplotlib and plotly. Plotly doesn't show me the whole data range.
import plotly.plotly as py
import plotly.graph_objs as go
# filter the data
df3 = df[df.line_item_returned==0][['created_at', 'line_item_price']].copy()
# remove the time part from datetime
df3.created_at = df3.created_at.dt.floor('d')
# set the datatime column as index
df3 = df3.set_index('created_at')
# Create traces
trace0 = go.Scatter(
x = df3.index,
y = df3.line_item_price.resample('d').sum().rolling(90, center=True).mean(),
mode = 'markers',
name = 'markers'
)
data = [trace0]
py.iplot(data, filename='scatter-mode')
The chart shows only the range Oct-Dec 2018.
Plotting the same data with matplotlib shows the whole data range 2016-2018:
import matplotlib.pyplot as plt
%matplotlib inline
plt.plot(df3.line_item_price.resample('d').sum().rolling(90, center=True).mean())
The index contains all data 2016-2018:
df3.line_item_price.resample('d').sum().rolling(31, center=True).mean().index
DatetimeIndex(['2015-11-18', '2015-11-19', '2015-11-20', '2015-11-21',
'2015-11-22', '2015-11-23', '2015-11-24', '2015-11-25',
'2015-11-26', '2015-11-27',
...
'2018-12-10', '2018-12-11', '2018-12-12', '2018-12-13',
'2018-12-14', '2018-12-15', '2018-12-16', '2018-12-17',
'2018-12-18', '2018-12-19'],
dtype='datetime64[ns]', name='created_at', length=1128, freq='D')
Why is this happening?
I guess it's a problem with indices.
%matplotlib inline
import plotly.offline as py
import plotly.graph_objs as go
import pandas as pd
import numpy as np
N = 2000
df = pd.DataFrame({"value":np.random.randn(N)},
index=pd.date_range(start='2015-01-01', periods=N))
# you don't really need to us `plt`
df.resample('d').sum().rolling(90, center=True).mean().plot();
But then if you want to use plotly you should use the index from the resampled Series.
df_rsmpl = df.resample('d').sum().rolling(90, center=True).mean()
trace0 = go.Scatter(x = df_rsmpl.index,
y = df_rsmpl["value"])
data = [trace0]
py.iplot(data)
I created a plot with Seaborn:
# imports
import random
%matplotlib inline
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
# create some random data
N = 20
rand_matrix = np.asarray([random.randrange(1,11)/10 for _ in range(1, N*N+1) ]).reshape(N,N)
data = np.flip(np.triu(rand_matrix), 1)
df = pd.DataFrame(data, index=pd.date_range(start='2015-01-01', freq='MS',\
periods=N), columns = range(1,N+1))
df[1]=1
# seaborn plot
plt.figure(figsize=(20,20))
sns.heatmap(data = df,
annot = True,
vmin = 0.0,
vmax = 1.0,
cmap = 'PuBuGn')
Here is how I create the Plotly plot:
import plotly.offline as py
import plotly.graph_objs as go
py.init_notebook_mode(connected=True)
data = [
go.Heatmap(
z=df.values,
x=df.columns.tolist(),
y=df.index.tolist()
)
]
layout = go.Layout(
xaxis = dict(ticks='', nticks=N),
yaxis = dict(ticks='', nticks=N))
fig = go.Figure(data=data, layout=layout)
iplot(fig)
I have following questions:
Seaborn: How do I change the y axis labels to YYYY-MM?
Plotly: How do I get the older data to the top (like with Seaborn)
Plotly: How do I get the data labels displayed in the heatmap (like Seaborn does).
Plotly: I noticed that rendering is much slower than Seaborn. Can it be optimized, if yes how?
2. How do I get the older data to the top?
fig.update_yaxes(autorange="reversed")
3. How do i get the data labels displayed in the heatmap?
ff.create_annotated_heatmap()
4. Can plotly be optimized?
No quick-fixes here.
Plot:
Plotly:
from plotly.offline import iplot
import plotly.graph_objs as go
import plotly.figure_factory as ff
#py.init_notebook_mode(connected=True)
data = [
go.Heatmap(
z=df.values,
x=df.columns.tolist(),
y=df.index.tolist()
)
]
font_colors = ['white', 'black']
fig = ff.create_annotated_heatmap(z=df.values, colorscale='Jet', font_colors=font_colors)
fig.update_layout( yaxis = dict(ticks='', nticks=N))
# Make text size smaller
for i in range(len(fig.layout.annotations)):
fig.layout.annotations[i].font.size = 8
# reverse scales
fig.update_yaxes(autorange="reversed")
fig.show()