How To Draw Thicker Candlestick In Python
Calculation Lines and Polygons to Figures¶
As a general rule, in that location are 2 means to add shapes (lines or polygons) to figures:
- Trace types in the
scatterfamily (eastward.thou.scatter,scatter3d,scattergeoetc) can exist drawn withmode="lines"and optionally support afill="self"attribute, and then can be used to draw open or closed shapes on figures. - Standalone lines, ellipses and rectangles can be added to figures using
fig.add_shape(), and they can be positioned absolutely inside the figure, or they tin be positioned relative to the axes of 2d cartesian subplots i.east. in data coordinates.
Annotation: there are special methods add_hline, add_vline, add_hrect and add_vrect for the mutual cases of wanting to draw horizontal or vertical lines or rectangles that are stock-still to data coordinates in one axis and absolutely positioned in some other.
The differences betwixt these two approaches are that:
- Traces tin can optionally support hover labels and tin appear in legends.
- Shapes can be positioned absolutely or relative to data coordinates in 2d cartesian subplots simply.
- Traces cannot be positioned absolutely but can be positioned relative to date coordinates in any subplot type.
- Traces as well back up optional text, although at that place is a textual equivalent to shapes in text annotations.
Shape-drawing with Besprinkle traces¶
There are ii means to depict filled shapes: scatter traces and layout.shapes which is mostly useful for the 2d subplots, and defines the shape type to be drawn, and tin can be rectangle, circle, line, or path (a custom SVG path). You also can utilise scatterpolar, scattergeo, scattermapbox to draw filled shapes on any kind of subplots. To set an area to be filled with a solid colour, you need to define Scatter.fill="toself" that connects the endpoints of the trace into a closed shape. If mode=line (default value), then you need to echo the initial betoken of a shape at the of the sequence to have a airtight shape.
In [1]:
import plotly.graph_objects as go fig = go . Figure ( get . Scatter ( x = [ 0 , 1 , two , 0 ], y = [ 0 , 2 , 0 , 0 ], fill up = "toself" )) fig . show ()
You can have more shapes either past adding more traces or interrupting the series with None.
In [2]:
import plotly.graph_objects as go fig = go . Effigy ( go . Scatter ( ten = [ 0 , ane , 2 , 0 , None , 3 , iii , five , 5 , iii ], y = [ 0 , 2 , 0 , 0 , None , 0.v , 1.5 , 1.5 , 0.5 , 0.v ], make full = "toself" )) fig . show ()
Shapes in Dash¶
Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the lawmaking and run python app.py.
Get started with the official Nuance docs and acquire how to effortlessly style & deploy apps like this with Dash Enterprise.
Vertical and Horizontal Lines Positioned Relative to the Centrality Data¶
In [4]:
import plotly.graph_objects every bit go fig = go . Figure () # Create scatter trace of text labels fig . add_trace ( go . Scatter ( 10 = [ 2 , iii.5 , half-dozen ], y = [ i , 1.5 , 1 ], text = [ "Vertical Line" , "Horizontal Dashed Line" , "Diagonal dotted Line" ], mode = "text" , )) # Ready axes ranges fig . update_xaxes ( range = [ 0 , 7 ]) fig . update_yaxes ( range = [ 0 , ii.5 ]) # Add shapes fig . add_shape ( blazon = "line" , x0 = i , y0 = 0 , x1 = 1 , y1 = two , line = dict ( color = "RoyalBlue" , width = 3 ) ) fig . add_shape ( type = "line" , x0 = ii , y0 = ii , x1 = 5 , y1 = 2 , line = dict ( colour = "LightSeaGreen" , width = 4 , dash = "dashdot" , ) ) fig . add_shape ( blazon = "line" , x0 = iv , y0 = 0 , x1 = half-dozen , y1 = 2 , line = dict ( colour = "MediumPurple" , width = 4 , nuance = "dot" , ) ) fig . update_shapes ( dict ( xref = 'x' , yref = 'y' )) fig . testify ()
Lines Positioned Relative to the Plot & to the Axis Information¶
In [5]:
import plotly.graph_objects as go fig = get . Figure () # Create scatter trace of text labels fig . add_trace ( go . Scatter ( x = [ ii , 6 ], y = [ ane , 1 ], text = [ "Line positioned relative to the plot" , "Line positioned relative to the axes" ], mode = "text" , )) # Fix axes ranges fig . update_xaxes ( range = [ 0 , 8 ]) fig . update_yaxes ( range = [ 0 , 2 ]) fig . add_shape ( type = "line" , xref = "x" , yref = "y" , x0 = four , y0 = 0 , x1 = 8 , y1 = i , line = dict ( color = "LightSeaGreen" , width = 3 , ), ) fig . add_shape ( type = "line" , xref = "paper" , yref = "newspaper" , x0 = 0 , y0 = 0 , x1 = 0.five , y1 = 0.5 , line = dict ( color = "DarkOrange" , width = 3 , ), ) fig . show ()
Rectangles Positioned Relative to the Axis Information¶
In [6]:
import plotly.graph_objects as go fig = become . Figure () fig . add_trace ( go . Scatter ( x = [ 1.5 , 4.five ], y = [ 0.75 , 0.75 ], text = [ "Unfilled Rectangle" , "Filled Rectangle" ], mode = "text" , )) # Set axes properties fig . update_xaxes ( range = [ 0 , 7 ], showgrid = False ) fig . update_yaxes ( range = [ 0 , 3.five ]) # Add shapes fig . add_shape ( type = "rect" , x0 = 1 , y0 = 1 , x1 = ii , y1 = 3 , line = dict ( color = "RoyalBlue" ), ) fig . add_shape ( type = "rect" , x0 = three , y0 = ane , x1 = 6 , y1 = 2 , line = dict ( color = "RoyalBlue" , width = ii , ), fillcolor = "LightSkyBlue" , ) fig . update_shapes ( dict ( xref = 'x' , yref = 'y' )) fig . show ()
Rectangle Positioned Relative to the Plot & to the Centrality Data¶
In [7]:
import plotly.graph_objects as get fig = get . Figure () # Create besprinkle trace of text labels fig . add_trace ( go . Scatter ( ten = [ ane.5 , 3 ], y = [ 2.5 , two.five ], text = [ "Rectangle reference to the plot" , "Rectangle reference to the axes" ], mode = "text" , )) # Set axes backdrop fig . update_xaxes ( range = [ 0 , 4 ]) fig . update_yaxes ( range = [ 0 , 4 ]) # Add shapes fig . add_shape ( type = "rect" , xref = "10" , yref = "y" , x0 = 2.5 , y0 = 0 , x1 = 3.5 , y1 = ii , line = dict ( color = "RoyalBlue" , width = 3 , ), fillcolor = "LightSkyBlue" , ) fig . add_shape ( type = "rect" , xref = "newspaper" , yref = "paper" , x0 = 0.25 , y0 = 0 , x1 = 0.5 , y1 = 0.5 , line = dict ( color = "LightSeaGreen" , width = 3 , ), fillcolor = "PaleTurquoise" , ) fig . show ()
A Rectangle Placed Relative to the Axis Position and Length¶
A shape can exist placed relative to an axis's position on the plot by adding the string ' domain' to the centrality reference in the xref or yref attributes for shapes. The following code places a rectangle that starts at 60% and ends at 70% along the ten-axis, starting from the left, and starts at fourscore% and ends at ninety% along the y-axis, starting from the lesser.
In [eight]:
import plotly.graph_objects equally go import plotly.limited as px df = px . data . air current () fig = px . besprinkle ( df , y = "frequency" ) fig . update_layout ( xaxis = dict ( domain = [ 0 , 0.5 ]), yaxis = dict ( domain = [ 0.25 , 0.75 ])) # Add a shape whose x and y coordinates refer to the domains of the x and y axes fig . add_shape ( type = "rect" , xref = "ten domain" , yref = "y domain" , x0 = 0.half-dozen , x1 = 0.7 , y0 = 0.8 , y1 = 0.9 , ) fig . bear witness ()
In [9]:
import plotly.graph_objects as go fig = go . Figure () # Add together besprinkle trace for line fig . add_trace ( become . Besprinkle ( x = [ "2015-02-01" , "2015-02-02" , "2015-02-03" , "2015-02-04" , "2015-02-05" , "2015-02-06" , "2015-02-07" , "2015-02-08" , "2015-02-09" , "2015-02-10" , "2015-02-11" , "2015-02-12" , "2015-02-xiii" , "2015-02-14" , "2015-02-15" , "2015-02-16" , "2015-02-17" , "2015-02-18" , "2015-02-19" , "2015-02-20" , "2015-02-21" , "2015-02-22" , "2015-02-23" , "2015-02-24" , "2015-02-25" , "2015-02-26" , "2015-02-27" , "2015-02-28" ], y = [ - fourteen , - 17 , - viii , - 4 , - vii , - x , - 12 , - 14 , - 12 , - seven , - 11 , - 7 , - xviii , - 14 , - fourteen , - sixteen , - thirteen , - 7 , - 8 , - 14 , - viii , - 3 , - 9 , - nine , - 4 , - 13 , - 9 , - 6 ], way = "lines" , name = "temperature" )) # Add shape regions fig . add_vrect ( x0 = "2015-02-04" , x1 = "2015-02-06" , fillcolor = "LightSalmon" , opacity = 0.five , layer = "below" , line_width = 0 , ), fig . add_vrect ( x0 = "2015-02-20" , x1 = "2015-02-22" , fillcolor = "LightSalmon" , opacity = 0.5 , layer = "below" , line_width = 0 , ) fig . show ()
Circles Positioned Relative to the Axis Data¶
In [10]:
import plotly.graph_objects as go fig = go . Effigy () # Create scatter trace of text labels fig . add_trace ( become . Scatter ( x = [ 1.five , three.5 ], y = [ 0.75 , 2.v ], text = [ "Unfilled Circle" , "Filled Circle" ], way = "text" , )) # Set axes properties fig . update_xaxes ( range = [ 0 , 4.5 ], zeroline = False ) fig . update_yaxes ( range = [ 0 , 4.5 ]) # Add circles fig . add_shape ( type = "circumvolve" , xref = "x" , yref = "y" , x0 = 1 , y0 = 1 , x1 = 3 , y1 = three , line_color = "LightSeaGreen" , ) fig . add_shape ( blazon = "circle" , xref = "x" , yref = "y" , fillcolor = "PaleTurquoise" , x0 = 3 , y0 = iii , x1 = iv , y1 = 4 , line_color = "LightSeaGreen" , ) # Fix figure size fig . update_layout ( width = 800 , acme = 800 ) fig . evidence ()
Highlighting Clusters of Besprinkle Points with Circle Shapes¶
In [eleven]:
import plotly.graph_objects every bit get import numpy every bit np np . random . seed ( 1 ) # Generate information x0 = np . random . normal ( ii , 0.45 , 300 ) y0 = np . random . normal ( 2 , 0.45 , 300 ) x1 = np . random . normal ( 6 , 0.four , 200 ) y1 = np . random . normal ( half dozen , 0.4 , 200 ) # Create effigy fig = get . Figure () # Add together scatter traces fig . add_trace ( become . Scatter ( 10 = x0 , y = y0 , fashion = "markers" )) fig . add_trace ( get . Besprinkle ( x = x1 , y = y1 , mode = "markers" )) # Add shapes fig . add_shape ( type = "circumvolve" , xref = "x" , yref = "y" , x0 = min ( x0 ), y0 = min ( y0 ), x1 = max ( x0 ), y1 = max ( y0 ), opacity = 0.2 , fillcolor = "blue" , line_color = "blue" , ) fig . add_shape ( type = "circle" , xref = "x" , yref = "y" , x0 = min ( x1 ), y0 = min ( y1 ), x1 = max ( x1 ), y1 = max ( y1 ), opacity = 0.2 , fillcolor = "orange" , line_color = "orange" , ) # Hide fable fig . update_layout ( showlegend = False ) fig . testify ()
Venn Diagram with Circumvolve Shapes¶
In [12]:
import plotly.graph_objects as go fig = get . Figure () # Create scatter trace of text labels fig . add_trace ( go . Besprinkle ( 10 = [ one , 1.75 , 2.5 ], y = [ 1 , ane , 1 ], text = [ "$A$" , "$A+B$" , "$B$" ], mode = "text" , textfont = dict ( color = "black" , size = 18 , family = "Arail" , ) )) # Update axes properties fig . update_xaxes ( showticklabels = False , showgrid = False , zeroline = Imitation , ) fig . update_yaxes ( showticklabels = False , showgrid = False , zeroline = Imitation , ) # Add circles fig . add_shape ( type = "circle" , line_color = "blueish" , fillcolor = "blue" , x0 = 0 , y0 = 0 , x1 = 2 , y1 = 2 ) fig . add_shape ( blazon = "circumvolve" , line_color = "grayness" , fillcolor = "greyness" , x0 = 1.five , y0 = 0 , x1 = three.5 , y1 = 2 ) fig . update_shapes ( opacity = 0.iii , xref = "ten" , yref = "y" ) fig . update_layout ( margin = dict ( l = 20 , r = 20 , b = 100 ), height = 600 , width = 800 , plot_bgcolor = "white" ) fig . show ()
Calculation Shapes to Subplots¶
Here we use the dissimilar axes (x1, x2) created by make_subplots as reference in order to draw shapes in effigy subplots.
In [13]:
import plotly.graph_objects equally become from plotly.subplots import make_subplots # Create Subplots fig = make_subplots ( rows = 2 , cols = 2 ) fig . add_trace ( become . Scatter ( x = [ 2 , six ], y = [ 1 , i ]), row = 1 , col = i ) fig . add_trace ( become . Bar ( ten = [ 1 , 2 , 3 ], y = [ iv , five , 6 ]), row = i , col = ii ) fig . add_trace ( become . Scatter ( 10 = [ 10 , 20 ], y = [ 40 , 50 ]), row = 2 , col = 1 ) fig . add_trace ( go . Bar ( x = [ 11 , 13 , 15 ], y = [ 8 , 11 , 20 ]), row = 2 , col = 2 ) # Add shapes fig . update_layout ( shapes = [ dict ( type = "line" , xref = "10" , yref = "y" , x0 = 3 , y0 = 0.5 , x1 = 5 , y1 = 0.8 , line_width = 3 ), dict ( type = "rect" , xref = "x2" , yref = 'y2' , x0 = four , y0 = ii , x1 = five , y1 = vi ), dict ( type = "rect" , xref = "x3" , yref = "y3" , x0 = 10 , y0 = 20 , x1 = xv , y1 = thirty ), dict ( type = "circle" , xref = "x4" , yref = "y4" , x0 = 5 , y0 = 12 , x1 = 10 , y1 = 18 )]) fig . show ()
Adding the Same Shapes to Multiple Subplots¶
The same shape can be added to multiple facets past using the 'all' keyword in the row and col arguments. For example
In [14]:
import plotly.limited as px df = px . data . tips () fig = px . scatter ( df , ten = "total_bill" , y = "tip" , facet_row = "smoker" , facet_col = "sex" ) # Adds a rectangle to all facets fig . add_shape ( dict ( type = "rect" , x0 = 25 , x1 = 35 , y0 = 4 , y1 = half-dozen , line_color = "purple" ), row = "all" , col = "all" , ) # Adds a line to all the rows of the second column fig . add_shape ( dict ( type = "line" , x0 = 20 , x1 = 25 , y0 = 5 , y1 = 6 , line_color = "yellow" ), row = "all" , col = 2 ) # Adds a circle to all the columns of the outset row fig . add_shape ( dict ( type = "circle" , x0 = 10 , y0 = 2 , x1 = twenty , y1 = 7 ), row = ane , col = "all" , line_color = "green" ) fig . bear witness ()
SVG Paths¶
In [15]:
import plotly.graph_objects equally go fig = become . Effigy () # Create scatter trace of text labels fig . add_trace ( become . Scatter ( x = [ ii , 1 , 8 , 8 ], y = [ 0.25 , 9 , two , six ], text = [ "Filled Triangle" , "Filled Polygon" , "Quadratic Bezier Curves" , "Cubic Bezier Curves" ], mode = "text" , )) # Update axes backdrop fig . update_xaxes ( range = [ 0 , 9 ], zeroline = Imitation , ) fig . update_yaxes ( range = [ 0 , xi ], zeroline = False , ) # Add shapes fig . update_layout ( shapes = [ # Quadratic Bezier Curves dict ( type = "path" , path = "M 4,iv Q 6,0 8,four" , line_color = "RoyalBlue" , ), # Cubic Bezier Curves dict ( type = "path" , path = "M i,4 C 2,viii 6,4 8,8" , line_color = "MediumPurple" , ), # filled Triangle dict ( type = "path" , path = " Grand one 1 L 1 three L 4 i Z" , fillcolor = "LightPink" , line_color = "Crimson" , ), # filled Polygon dict ( type = "path" , path = " Yard three,vii L2,8 L2,9 L3,ten, L4,x L5,9 L5,viii L4,7 Z" , fillcolor = "PaleTurquoise" , line_color = "LightSeaGreen" , ), ] ) fig . bear witness ()
Drawing shapes with a Mouse on Cartesian plots¶
introduced in plotly four.7
You tin can create layout shapes programmatically, merely yous can also draw shapes manually by setting the dragmode to 1 of the shape-cartoon modes: 'drawline','drawopenpath', 'drawclosedpath', 'drawcircle', or 'drawrect'. If y'all demand to switch betwixt different shape-drawing or other dragmodes (panning, selecting, etc.), modebar buttons can be added in the config to select the dragmode. If you switch to a different dragmode such as pan or zoom, you will need to select the drawing tool in the modebar to go back to shape drawing.
This shape-drawing characteristic is particularly interesting for annotating graphs, in particular epitome traces or layout images.
In one case you lot have drawn shapes, you can select and change an existing shape past clicking on its boundary (notation the arrow pointer). Its fillcolor turns to pink to highlight the activated shape and then you tin can
- drag and resize information technology for lines, rectangles and circles/ellipses
- elevate and motion individual vertices for closed paths
- move private vertices for open paths.
An activated shape is deleted by clicking on the eraseshape push button.
Cartoon or modifying a shape triggers a relayout result, which can be captured by a callback inside a Dash awarding.
In [16]:
import plotly.graph_objects as go fig = get . Figure () text = "Click and drag here <br> to draw a rectangle <br><br> or select another shape <br>in the modebar" fig . add_annotation ( x = 0.5 , y = 0.v , text = text , xref = "paper" , yref = "paper" , showarrow = False , font_size = 20 ) # shape defined programatically fig . add_shape ( editable = True , x0 =- i , x1 = 0 , y0 = 2 , y1 = iii , xref = 'x' , yref = 'y' ) # define dragmode and add modebar buttons fig . update_layout ( dragmode = 'drawrect' ) fig . show ( config = { 'modeBarButtonsToAdd' :[ 'drawline' , 'drawopenpath' , 'drawclosedpath' , 'drawcircle' , 'drawrect' , 'eraseshape' ]})
Style of user-drawn shapes¶
The layout newshape attribute controls the visual advent of new shapes drawn by the user. newshape attributes have the same names equally layout shapes.
Annotation on shape opacity: having a new shape's opacity > 0.5 makes it possible to actuate a shape by clicking inside the shape (for opacity <= 0.5 you have to click on the edge of the shape), simply y'all cannot kickoff a new shape within an existing shape (which is possible for an opacity <= 0.v).
In [17]:
import plotly.graph_objects as go fig = become . Figure () text = "Click and drag<br> to draw a rectangle <br><br> or select another shape <br>in the modebar" fig . add_annotation ( x = 0.five , y = 0.5 , text = text , xref = "paper" , yref = "newspaper" , showarrow = False , font_size = 20 ) # shape defined programatically fig . add_shape ( line_color = 'yellow' , fillcolor = 'turquoise' , opacity = 0.4 , editable = True , x0 = 0 , x1 = i , y0 = two , y1 = three , xref = 'x' , yref = 'y' ) fig . update_layout ( dragmode = 'drawrect' , # style of new shapes newshape = dict ( line_color = 'yellow' , fillcolor = 'turquoise' , opacity = 0.5 )) fig . show ( config = { 'modeBarButtonsToAdd' :[ 'drawline' , 'drawopenpath' , 'drawclosedpath' , 'drawcircle' , 'drawrect' , 'eraseshape' ]})
What Well-nigh Dash?¶
Dash is an open up-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.
Learn almost how to install Dash at https://dash.plot.ly/installation.
Everywhere in this page that you see fig.show(), you tin can display the same figure in a Dash application by passing information technology to the effigy argument of the Graph component from the built-in dash_core_components parcel like this:
import plotly.graph_objects as go # or plotly.express as px fig = become . Effigy () # or any Plotly Express function e.1000. px.bar(...) # fig.add_trace( ... ) # fig.update_layout( ... ) import nuance import dash_core_components equally dcc import dash_html_components as html app = dash . Dash () app . layout = html . Div ([ dcc . Graph ( figure = fig ) ]) app . run_server ( debug = True , use_reloader = Simulated ) # Turn off reloader if inside Jupyter 
Source: https://plotly.com/python/shapes/
Posted by: ortizziese1943.blogspot.com
0 Response to "How To Draw Thicker Candlestick In Python"
Post a Comment