Develop Reference

Automatic Adjust of Y axis values using slider on matplotlib

I was working on making a graph that displays the full line by keping x axis constant and left axis adjusting. I am calculating the cost to produce egg tray with multiple variables.
Using jupyter notebook with ipywidgets as widgets i was able to get the answer.
jypyter auto adjusting y axis
import ipywidgets as widgets
from IPython.display import display
import matplotlib.pyplot as plt
import numpy as np
%matplotlib nbagg
x = np.linspace(50000, 80000, 30000)
fig, ax = plt.subplots(1, figsize=(10,4))
plt.suptitle('Cost To Produce')
def production_cost(carton_percent,assorted_percent,white_percent,dry_eggtray_weight,electric_cost,ebiogas_sold,skilled_labor,manual_labor):
ax.clear()
total_paper_weight = x*dry_eggtray_weight/1000
carton_price = total_paper_weight*carton_percent*1.9/100
assorted_price = total_paper_weight*assorted_percent*4.25/100
white_price = total_paper_weight*white_percent*10/100
burner_consumption = (x+8679)/17.3
electric_consumption = (x+11074)/19.1
skilledlabor_cost = skilled_labor*6*346/7+skilled_labor*6*3.5*54/7
manuallabor_cost = manual_labor*6*290/7+manual_labor*6*3.5*36/7
rawmats_price = carton_price + assorted_price + white_price
burner_price = burner_consumption*2.2*ebiogas_sold
electric_price = electric_consumption*electric_cost
labor_price = skilledlabor_cost + manuallabor_cost
cellophane_price = x*13/(140*2)
maintenance_price = 2000
admin_price = 4000
overall_price = rawmats_price + burner_price + electric_price + labor_price + cellophane_price + maintenance_price + admin_price
y = overall_price/x
ax.plot(x,y)
ax.set_xlabel('Egg Tray Production')
ax.set_ylabel('Cost per Tray')
plt.show()
carton_percent = widgets.FloatSlider(min=0, max=100, value=37.5, description='% Carton:')
assorted_percent = widgets.FloatSlider(min=0, max=100, value=37.5, description='% Assorted:')
white_percent = widgets.FloatSlider(min=0, max=100, value=25, description='% White:')
dry_eggtray_weight = widgets.IntSlider(min=0, max=90, value=80, description='Dry Try (g):')
electric_cost = widgets.FloatSlider(min=6, max=20, value=11.37, description='Elec Cost:')
ebiogas_sold = widgets.FloatSlider(min=6, max=20, value=6.7, description='EBio SellP:')
skilled_labor = widgets.IntSlider(min=0, max=12, value=6, description='Skilled C:')
manual_labor = widgets.IntSlider(min=0, max=16, value=12, description='Manual C:')
widgets.interactive(production_cost, carton_percent=carton_percent, assorted_percent=assorted_percent, white_percent=white_percent, dry_eggtray_weight=dry_eggtray_weight, electric_cost=electric_cost, ebiogas_sold=ebiogas_sold, skilled_labor=skilled_labor, manual_labor=manual_labor)
But using python idle, i wasnt able to copy the result
I tried narrowing variables so it wouldnt be hard to trace. So i tried this code with widgets from matplotlib directly. but only the drawing or graph is moving and both x and y axis is steady. I have tried autoscale but it does not work. Like ax.autoscale
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Slider, Button
# The parametrized function to be plotted
def f(x,electric_cost,ebiogas_sold):
burner_consumption = (x+8679)/17.3
electric_consumption = (x+11074)/19.1
burner_price = burner_consumption*2.2*ebiogas_sold
electric_price = electric_consumption*electric_cost
overall_price = burner_price + electric_price
return overall_price/x
x = np.linspace(50000, 80000, 30001)
# Define initial parameters
init_electric_cost = 11.37
init_ebiogas_sold = 6.7
# Create the figure and the line that we will manipulate
fig, ax = plt.subplots()
line, = ax.plot(x, f(x, init_electric_cost, init_ebiogas_sold), lw=2)
ax.autoscale(enable=True, axis="y", tight=True)
ax.set_xlabel('Egg Tray Produced [pcs]')
# adjust the main plot to make room for the sliders
fig.subplots_adjust(left=0.25, bottom=0.25)
# Make a vertically oriented slider to control the cost of electricity
electric_cost = fig.add_axes([0.25, 0.1, 0.65, 0.03])
electric_cost_slider = Slider(
ax=electric_cost,
label="Electric Cost [pesos]",
valmin=0,
valmax=20,
valinit=init_electric_cost,
)
# Make a vertically oriented slider to control the cost of biogas converted to power and then sold
biogas_cost = fig.add_axes([0.1, 0.25, 0.0225, 0.63])
biogas_cost_slider = Slider(
ax=biogas_cost,
label="Biogas Power Sold [pesos]",
valmin=0,
valmax=20,
valinit=init_ebiogas_sold,
orientation="vertical"
)
# The function to be called anytime a slider's value changes
def update(val):
line.set_ydata(f(x, electric_cost_slider.val, biogas_cost_slider.val))
#fig.canvas.draw_idle()
# register the update function with each slider
electric_cost_slider.on_changed(update)
biogas_cost_slider.on_changed(update)
# Create a `matplotlib.widgets.Button` to reset the sliders to initial values.
resetax = fig.add_axes([0.8, 0.025, 0.1, 0.04])
button = Button(resetax, 'Reset', hovercolor='0.975')
def reset(event):
biogas_cost_slider.reset()
electric_cost_slider.reset()
button.on_clicked(reset)
plt.show()

I finally got the answer.
I just have to change the limits of my Y
My changes are highlighted as BOLD
I dont know how to properly construct yet. so ill just paste the screenshot of changes
enter image description here
enter image description here

Update python bokeh layout using bokeh server?

What I want to do:
I made a dynamic map using bokeh, pandas and geopandas. The data to be displayed is loaded from a table, then mapped to a country per year. The year to be displayed is determined by a bokeh slider. You can also hover over a country and get its value. I now want to be able to change the data source by selecting a radio button. To display the data correctly, I want to change the color palette, rescale it (a range from e.g. 50 to 100 instead of 0 to 4.5), update the scaling on the slider to the new lowest year to highest year, and then display the world-map with the new data. I also want to update the title of the map from e.g. "Fertility" to "Life expectancy".
What I already have:
I have a working dynamic map with Slider and Hover tool. I also have a list from which the data to be used is taken (datapath, title to be used, color palette to be used, highest and lowest year, highest and lowest value). I have a radio button group, with three different data sources to choose from. All paths are relative, the data is provided with a consistent structure. I had the map changing the data below and displaying the new stuff, but than I did something and it stopped working. I also had the Hover tool display the right values, but with a wrong (old) description.
What I need help with:
Updating the color bar to accomodate the new palette and the new range
Updating the slider to accomodate a changed range
Updating the title displayed to show what's actually displayed
What I already tried:
I've put the whole loading and displaying in the function executed when the radio button group is changed. The first thing this function does is clearing the layout and then rebuilding it. Unfortunately, this is neither efficient, nor working, since I only get the radio button Group and an empty space below, no matter what I do. I've searched for a solution, but all I found (and tried) didn't do what I needed.
I can provide the actual code, if needed (though some of the variables and documentations are in German), but since I'm pretty new to the whole python thing, I don't now, what exactly of that about 300 lines of code you need. Just let me now, and I'll try and provide.
Hope you can help me with that.
Thanks in advance,
Asd789
EDIT: As correctly pointed out in the comments, some code to help understand what I did.
I'll cut all the imports for the sake of brevity, as a mistake there would have shown up as error in my terminal. Also leaving out comments.
geoFrame #dataframe for geopanda shapefile
configList = [0, "Fertility", 'YlGnBu', 'Year' ]
df #dataframe for the .csv file
higStep = df['step'].max()
lowStep = df['step'].min()
configList.append(higStep)
higVal = df['valueInterest'].max()
merged = geoFrame.merge(df, left_on = 'country_code', right_on = 'code')
merged_json = json.loads(merged.to_json())
json_data = json.dumps(merged_json)
geosource = GeoJSONDataSource(geojson = json_data)
palette = brewer[configList[2]][8]
color_mapper = LinearColorMapper(palette = palette, low = 0, high = 4)
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 500, height = 20,
border_line_color=None,location = (0,0), orientation = 'horizontal')
p = figure(title = configList[1], plot_height = 600 , plot_width = 950, toolbar_location = None)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.patches('xs','ys', source = geosource,fill_color = {'field' :'valueInterest', 'transform' : color_mapper},
line_color = 'black', line_width = 0.25, fill_alpha = 1)
p.add_layout(color_bar, 'below')
df_curr = df[df['step'] == higStep]
color_mapper = LinearColorMapper(palette = palette, low = 0, high = 40, nan_color = '#d9d9d9')
def json_data(selectedStep):
st = selectedStep
df_st = df[df['step'] == st]
merged = geoFrame.merge(df_st, left_on = 'country_code', right_on = 'code', how = 'left')
merged_json = json.loads(merged.to_json())
json_data = json.dumps(merged_json)
return json_data
geosource = GeoJSONDataSource(geojson = json_data(higStep))
palette = brewer[configList[2]][8]
palette = palette[::-1]
color_mapper = LinearColorMapper(palette = palette, low = 0, high = higVal/2, nan_color = '#d9d9d9')
hover = HoverTool(tooltips = [ ('Country/region','#country'),(configList[1], '#valueInterest')])
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 500, height = 20,
border_line_color=None,location = (0,0), orientation = 'horizontal')
p = figure(title = configList[1], plot_height = 600 , plot_width = 950, toolbar_location = None, tools = [hover])
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.patches('xs','ys', source = geosource,fill_color = {'field' :'valueInterest', 'transform' : color_mapper},
line_color = 'black', line_width = 0.25, fill_alpha = 1)
p.add_layout(color_bar, 'below')
def update_plot(attr, old, new):
st = slider.value
new_data = json_data(st)
geosource.geojson = new_data
p.title.text = configList[1] %st
slider = Slider(title = configList[3],start = 1950, end = 2015, step = 1, value = 2015)
slider.on_change('value', update_plot)
def radHandler(attr, new, old):
if radio.active == 0:
datapath = os.path.join(dataloc, 'children-per-woman-UN.csv')
configList = [0, "Fertility", 'YlGnBu', 'Year']
elif radio.active == 1:
#see above with diferent data
elif radio.active == 2:
#see above with different data
curdoc().clear()
higStep = df['step'].max()
lowStep = df['step'].min()
configList.append(higStep)
update_plot(attr, new, old)
hover = HoverTool(tooltips = [ ('Country/region','#country'),(configList[1], '#valueInterest')])
palette = brewer[configList[2]][8]
palette = palette[::-1]
olor_mapper = LinearColorMapper(palette = palette, low = 0, high = higVal/2, nan_color = '#d9d9d9')
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 500, height = 20,
border_line_color=None,location = (0,0), orientation = 'horizontal'
p = figure(title = configList[1]+' '+str(configList[4]), plot_height = 600 , plot_width = 950, toolbar_location = None, tools = [hover])
layout = column(widgetbox(radio),p,widgetbox(slider))
curdoc().add_root(layout)
radio = RadioButtonGroup(labels=['Fertility', 'Life expectancy', 'Covid-19 total cases'], active=0)
radio.on_change('active',radHandler)
layout = column(p, widgetbox(radio),widgetbox(slider))
curdoc().title = configList[1]
curdoc().add_root(layout)
Sorry I could'nt cook it down further, but I don't know what's essential and what's just fancy stuff around.
This code works until I touch the radio button group. After that, the plot itself is just blank, without any prompts anywhere. The code itself is not entirely my fault, I got it to fix and maybe expand on it, the expansion being the ability to switch between data sources as decribed above.

Updating a matplotlib figure during simulation

I try to implement a matplotlib figure that updates during the simulation of my environment.
The following Classes works fine in my test but doesn't update the figure when I use it in my environment. During the simulation of the environment, the graph is shown, but no lines are plotted.
My guess is that .draw() is not working how I think it does.
Can anyone figure out the issue here?
class Visualisation:
def __init__(self, graphs):
self.graphs_dict = {}
for graph in graphs:
fig = plt.figure()
ax = fig.add_subplot(111)
line, = ax.plot(graph.x, graph.y, 'r-')
self.graphs_dict[graph.title] = {"fig": fig, "ax": ax, "line": line, "graph": graph}
self.graphs_dict[graph.title]["fig"].canvas.draw()
plt.ion()
plt.show()
def update(self, graph):
graph = self.graphs_dict[graph.title]["graph"]
self.graphs_dict[graph.title]["line"].set_xdata(graph.x)
self.graphs_dict[graph.title]["line"].set_ydata(graph.y)
self.graphs_dict[graph.title]["fig"].canvas.flush_events()
x_lim, y_lim = self.get_lim(graph)
self.graphs_dict[graph.title]["ax"].set_xlim(x_lim)
self.graphs_dict[graph.title]["ax"].set_ylim(y_lim)
self.graphs_dict[graph.title]["fig"].canvas.draw()
#staticmethod
def get_lim(graph):
if graph.x_lim is None:
x = np.array(graph.x)
y = np.array(graph.y)
x_lim = [x.min(), x.max()]
y_lim = [y.min(), y.max()]
else:
x_lim = graph.x_lim
y_lim = graph.y_lim
return x_lim, y_lim
class Graph:
def __init__(self, title, x, y, x_label="", y_label=""):
"""
Sets up a graph for Matplotlib
Parameters
----------
title : String
Title of the plot
x : float
y : float
x_label : String
x Label
y_label : String
y Label
"""
self.title = title
self.x = x
self.y = y
self.x_label = x_label
self.y_label = y_label
self.x_lim, self.y_lim = None, None
def set_lim(self, x_lim, y_lim):
self.x_lim = x_lim
self.y_lim = y_lim
class Environment:
def __init__(self, [..], verbose=0):
"""verbose : int
0 - No Visualisation
1 - Visualisation
2 - Visualisation and Logging"""
self.vis = None
self.verbose = verbose
[......]
def simulate(self):
for _ in range(self.n_steps):
[...]
self.visualize()
def visualize(self):
if self.verbose == 1 or self.verbose == 2:
if self.vis is None:
graphs = [Graph(title="VariableY", x=[], y=[])]
graphs[0].set_lim(x_lim=[0, 100], y_lim=[0, 300])
self.vis = Visualisation(graphs=graphs)
else:
self.vis.graphs_dict["VariableY"]["graph"].x.append(self.internal_step)
self.vis.graphs_dict["VariableY"]["graph"].y.append(150)
self.vis.update(self.vis.graphs_dict["VariableY"]["graph"])
When I run the code I more or less just write: env.simulate().
The code runs fine here:
class TestSingularVisualisation(unittest.TestCase):
def setUp(self):
self.graph = Graph(title="Test", x=[0], y=[0])
self.vis = Visualisation(graphs=[self.graph])
class TestSingleUpdate(TestSingularVisualisation):
def test_repeated_update(self):
for i in range(5):
self.graph.x.append(i)
self.graph.y.append(np.sin(i))
self.vis.update(self.graph)
time.sleep(1)

Turns out your code works the way it is set up. Here is the sole problem with the code you provided:
self.vis.graphs_dict["VariableY"]["graph"].x.append(self.internal_step)
self.vis.graphs_dict["VariableY"]["graph"].y.append(150)
You are plotting a line and correctly updating the canvas, however, you keep appending the exact same (x, y) coordinate. So the simulation does update the line, but the line simplifies to a point. Your test case does not do this. You can run a dummy example with your code by simply adding a line like this:
self.internal_step += 5
before adding the new x point, and you will produce a horizontal line.
Let me know if this solves your problem.

Probably not the most elegant, but I use plt.pause(0.1) when I want to update plots during execution. It pauses for 0.1s and forces all plots to be actually displayed. (It work in %debug in ipython as a bonus)

bokeh, dynamically change plot width

I'm trying to change plot width when taptool selects something.
I invoke this using bokeh serve, and navigating to the localhost website
when I click on one of the rectangles, my console prints out "callback" and "callback2" but my plot doesn't change width.
What am I doing wrong?
counts = [1*10**7,2*10**7,3*10**7] #dummy data
l_edge = [x for x in range(len(counts))]
r_edge = [x + .85 for x in range(len(counts))]
data = {
'height': counts,
'leftEdges': l_edge,
'rightEdges': r_edge,
}
p = figure()
s = ColumnDataSource(data)
p.add_tools(TapTool())
def callbackfcn(attr,old,new):
global p
print('callback')
p.width = np.random.choice([100,200,300,1000,10000])
p.height = np.random.choice([100,200,300,1000,10000])
print('callback2')
r = p.quad(top='height',bottom=0,
left = 'leftEdges',right = 'rightEdges',
source = s)
r.data_source.on_change('selected',callbackfcn)
layout = column(p)
curdoc().add_root(layout)

Have you tried instead including sizing_mode in your layout? Take a look at the Bokeh docs for an example.

Using a matplotlib button to alternate/switch between plots I created

So I've created several charts using the matplotlib library in python 3.5, but I want to be able to have the flexibility to utilize a button to alternate between the views I created within a single window. I've been trying to experiment with an example here, but have not succeeded in doing so. I was curious in how to have the flexibility to click through different views that I created.
My code is sort of organized like this:
def plot1(data1, 'name1'):
...
ax.plot(x,y)
plt.draw()
def plot2(data2, 'name2'):
...
ax2.plot(x,y)
plt.draw()
def plot3(data3, 'name3'):
...
ax3.plot(x,y)
plt.draw()
plot1(data1,'name1')
plot2(data2,'name2')
plot3(data3,'name3')
plt.show()
Currently it will show up in three different windows. Now when I try to make this all into one view accessible via buttons, I'm unable to do so because quite frankly I'm unfamiliar with how to pass on the variables in my methods to create my desired subplots with the callback function. Is there a way to sort of structure my code to have them all run under one matplotlib window?

The following would be a class that uses the functions that you create. Those would not actually plot anything, but provide the required data. They should be put in a list called funcs, and when you click next or prev the corresponding graph would pop up. This should get you started.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.widgets import Button
fig, ax = plt.subplots()
plt.subplots_adjust(bottom=0.2)
x = range(-50,50)
y = range(-50,50)
l, = plt.plot(x, y, lw=2)
ax.title.set_text('y = x')
class Index(object):
ind = 0
global funcs # used so yu can access local list, funcs, here
def next(self, event):
self.ind += 1
i = self.ind %(len(funcs))
x,y,name = funcs[i]() # unpack tuple data
l.set_xdata(x) #set x value data
l.set_ydata(y) #set y value data
ax.title.set_text(name) # set title of graph
plt.draw()
def prev(self, event):
self.ind -= 1
i = self.ind %(len(funcs))
x,y, name = funcs[i]() #unpack tuple data
l.set_xdata(x) #set x value data
l.set_ydata(y) #set y value data
ax.title.set_text(name) #set title of graph
plt.draw()
def plot1():
x = range(-20,20)
y = x
name = "y = x"
return (x,y, name)
def plot2():
x = range(-20,20)
y = np.power(x, 2)
name = "y = x^2"
return (x,y,name)
def plot3():
x = range(-20,20) # sample data
y = np.power(x, 3)
name = "y = x^3"
return (x,y, name)
funcs = [plot1, plot2, plot3] # functions in a list so you can interate over
callback = Index()
axprev = plt.axes([0.7, 0.05, 0.1, 0.075])
axnext = plt.axes([0.81, 0.05, 0.1, 0.075])
bnext = Button(axnext, 'Next')
bnext.on_clicked(callback.next)
bprev = Button(axprev, 'Previous')
bprev.on_clicked(callback.prev)
plt.show()