Using Enthought Canopy's TraitsUI, I'm using TabularAdapters to display some Arrays, but they always produce evenly proportioned column widths...I'd like to make some widths smaller than others, but haven't found any simple way yet...Anyone have any suggestions?
One way to control the widths of the columns is to override the get_width() method of the TabularArrayAdapter. For example,
import numpy as np
from traits.api import HasTraits, Array
from traitsui.api import View, Item, TabularEditor
from traitsui.tabular_adapter import TabularAdapter
test_dtype = np.dtype([('x', 'int'),
('y', 'int'),
('r', 'float')])
class TestArrayAdapter(TabularAdapter):
columns = [(name, idx) for idx, name in enumerate(test_dtype.names)]
even_bg_color = 0xF0F4FF
def get_width(self, object, name, col):
widths = {0: 50, 1: 50, 2: 150}
return widths[col]
class Test(HasTraits):
array1 = Array(dtype=test_dtype)
view = \
View(
Item(name='array1', show_label=False,
editor=TabularEditor(adapter=TestArrayAdapter())),
resizable=True,
)
a1 = np.array([(10, 20, 1.5), (15, 31, 2.4), (14, 11, 1.9), (21, 13, 2.5)],
dtype=test_dtype)
test = Test(array1=a1)
test.configure_traits()
Screenshot:
Related
Hello
Here is my code :
from scipy.stats import randint
param_distributions = {'n_estimators': randint(1, 5),
'max_depth': randint(5, 10)}
param_distributions["max_depth"] gives as a result :
{'n_estimators': <scipy.stats._distn_infrastructure.rv_frozen object
at 0x7f05f1b05210>, 'max_depth':
<scipy.stats._distn_infrastructure.rv_frozen object at
0x7f05f1b053d0>}
Why can't I get a value for this ?
randint(low, high) will return a distribution object. To sample from it, you need to use the rvs() method:
from scipy.stats import randint
param_distributions = {'n_estimators': randint(1, 5).rvs(),
'max_depth': randint(5, 10).rvs()}
>>> param_distributions
{'n_estimators': 3, 'max_depth': 9}
The docs list all methods available.
I am fairly new to python coding so I apologize in advance for my ignorance. I am trying to create a Dash App that drops outliers using standard deviation. The user selects a standard deviation using RadioItem inputs.
My question is what amendments do I need to make to my code so that the RadioItem value updates max_deviations using a callback?
Import packages, clean the data and define a query
import dash
import plotly.express as px
from dash import Dash, dcc, html, Input, Output, State
import pandas as pd
import numpy as np
app = dash.Dash(__name__)
server = app.server
df=pd.read_csv(r'C:\SVS_GIS\POWER BI\CSV_DATA\QSAS2021.csv', encoding='unicode_escape')
#SET DATE OF VALUATION
df['TIME'] = ((pd.to_datetime(df['Sale Date'], dayfirst=True)
.rsub(pd.to_datetime('01/10/2021', dayfirst=True))
.dt.days
)*-1)
df=df[df['TIME'] >= -365]
df = df.query("(SMA >=1 and SMA <= 3) and (LGA==60)")
prepare dataframe for dropping outliers
data = pd.DataFrame(data=df)
x = df.TIME
y = df.CHANGE
mean = np.mean(y)
standard_deviation = np.std(y)
distance_from_mean = abs(y - mean)
app layout
app.layout = html.Div([
html.Label("Standard Deviation Picker:", style={'fontSize':25, 'textAlign':'center'}),
html.Br(),
html.Label("1.0 = 68%, 2.0 = 95%, 3.0 = 99.7%", style={'fontSize':15,
'textAlign':'center'}),
html.Div(id="radio_items"),
dcc.RadioItems(
options=[{'label': i, 'value': i} for i in [1.0, 2.0, 3.0]],
value=2.0
),
html.Div([
dcc.Graph(id="the_graph")]
)])
callback
#app.callback(
Output("the_graph", "figure"),
Input("radio_items", 'value')
)
def update_graph(max_deviations):
not_outlier = distance_from_mean < max_deviations * standard_deviation
no_outliers = y[not_outlier]
trim_outliers = pd.DataFrame(data=no_outliers)
dff = pd.merge(trim_outliers, df, left_index=True, right_index=True)
return (dff)
fig = px.scatter(dff, x='TIME', y='CHANGE_y',
color ='SMA',
trendline='ols',
size='PV',
height=500,
width=800,
hover_name='SMA',
)
return dcc.Graph(id='the_graph', figure=fig)
if __name__ == '__main__':
app.run_server(debug=False)
Your dcc.RadioItems doesn't have an id prop. Add that, and make sure it matches the ID given in the callback, and you should be good.
Question:
Create a series named heights_A with values 176.2, 158.4, 167.6, 156.2, and 161.4. These values represent heights of 5 students of class A.
Label each student as s1, s2, s3, s4, and s5.
Create another series named weights_A with values 85.1, 90.2, 76.8, 80.4, and 78.9. These values represent weights of 5 students of class A.
Label each student as s1, s2, s3, s4, and s5.
Create a dataframe named df_A, which contains the height and weight of five students namely s1, s2, s3, s4 and s5.
Label the columns as Student_height and Student_weight, respectively.
Write the contents of df_A to a CSV file named classA.csv.
Note: Use the to_csv method associated with a dataframe.
Verify if the file classA.csv exists in the present directory using command ls -l.
You can also view the contents of the file using the command cat classA.csv
My code:
import pandas as pd
heights_A = pd.Series([176.2, 158.4, 167.6, 156.2,161.4])
heights_A.index = ["S1","S2","S3","S4","S5"]
weights_A = pd.Series([85.1, 90.2, 76.8, 80.4, 78.9])
weights_A.index = ["S1","S2","S3","S4","S5"]
df_A = pd.DataFrame({'Student_height': heights_A,'Student_weight':weights_A}, index = weights_A.index)
df_A.to_csv("classA.csv")
while checking with ls -l and cat classA.csv I can see the expected contents yet the checker does not allow me to proceed. Not sure where I am wrong
Use small letters for s1,s2...
import pandas as pd
heights_A = pd.Series([176.2, 158.4, 167.6, 156.2,161.4])
heights_A.index = ["s1","s2","s3","s4","s5"]
print(heights_A[1])
weights_A = pd.Series([85.1, 90.2, 76.8, 80.4, 78.9])
weights_A.index = ["s1","s2","s3","s4","s5"]
df_A = pd.DataFrame({'Student_height': heights_A,'Student_weight':weights_A}, index = weights_A.index)
df_A.to_csv("classA.csv")
import os
import numpy as np
import pandas as pd
# Creating the Series
heights_A = pd.Series([ 176.2, 158.4, 167.6, 156.2, 161.4 ])
# Creating the row axis labels
heights_A.index = ['s1', 's2', 's3', 's4','s5']
# Creating the Series
weights_A = pd.Series([85.1, 90.2, 76.8, 80.4 , 78.9])
# Creating the row axis labels
weights_A.index = ['s1', 's2', 's3', 's4','s5']
df_A = pd.DataFrame()
df_A['Student_height'] = heights_A
df_A['Student_weight'] = weights_A
# Display the shape of dataframe df_A
df_A.shape
df_A = pd.DataFrame({'Student_height': heights_A,'Student_weight':weights_A}, index = weights_A.index)
df_A.to_csv("classA.csv")
os.system("cat classA.csv")
df_A2=pd.read_csv("classA.csv")
print(df_A2)
df_A3=pd.read_csv("classA.csv", index_col=0)
print(df_A3)
np.random.seed(100)
x=np.random.normal(loc=170.0,scale=25.0,size=5)
np.random.seed(100)
heights_B=pd.Series(x,index=['s1','s2','s3','s4','s5'])
np.random.seed(100)
y=np.random.normal(loc=75.0,scale=12.0,size=5)
weights_B=pd.Series(y,index=['s1','s2','s3','s4','s5'])
df_B = pd.DataFrame({'Student_height': heights_B,'Student_weight':weights_B}, index = weights_B.index)
df_B.to_csv("classB.csv",index=False)
os.system("cat classB.csv")
df_B2=pd.read_csv("classB.csv")
print(df_B2)
df_B3=pd.read_csv("classB.csv", header=None)
print(df_B3)
df_B4=pd.read_csv("classB.csv", header=None, skiprows=2)
print(df_B4)
I'm tackling with VRPtw problem and struggling that the solver finds no solution with any data except for artificial small one.
The setting is as below.
There are several depots and locations to visit. Each locations have the time-window. Each vehicles have break time and work time. Also, the locations have some constraints and only the vehicles which satisfy that demand can visit there.
Based on this experiment setting, I wrote the code below.
As I wrote, it looks that it is working with small artificial data, but with real data, it never found the solution. I tried with 5 different data sets.
Although I set the 7200 sec time limit, previously I ran for longer than 10 hours and it was same.
The data's scale is 40~50 vehicles and 200~300 locations.
Does this code have a problem? If not, on what kind of order, should I change the approach(such as initialization, searching method and so on)?
(Edited to use integer for time matrix)
from dataclasses import dataclass
from typing import List, Tuple
from ortools.constraint_solver import pywrapcp
from ortools.constraint_solver import routing_enums_pb2
# TODO: Refactor
BIG_ENOUGH = 100000000
TIME_DIMENSION = 'Time'
TIME_LIMIT = 7200
#dataclass
class DataSet:
time_matrix: List[List[int]]
locations_num: int
vehicles_num: int
vehicles_break_time_window: List[Tuple[int, int, int]]
vehicles_work_time_windows: List[Tuple[int, int]]
location_time_windows: List[Tuple[int, int]]
vehicles_depots_indices: List[int]
possible_vehicles: List[List[int]]
def execute(data: DataSet):
manager = pywrapcp.RoutingIndexManager(data.locations_num,
data.vehicles_num,
data.vehicles_depots_indices,
data.vehicles_depots_indices)
routing_parameters = pywrapcp.DefaultRoutingModelParameters()
routing_parameters.solver_parameters.trace_propagation = True
routing_parameters.solver_parameters.trace_search = True
routing = pywrapcp.RoutingModel(manager, routing_parameters)
def time_callback(source_index, dest_index):
from_node = manager.IndexToNode(source_index)
to_node = manager.IndexToNode(dest_index)
return data.time_matrix[from_node][to_node]
transit_callback_index = routing.RegisterTransitCallback(time_callback)
routing.SetArcCostEvaluatorOfAllVehicles(transit_callback_index)
routing.AddDimension(
transit_callback_index,
BIG_ENOUGH,
BIG_ENOUGH,
False,
TIME_DIMENSION)
time_dimension = routing.GetDimensionOrDie(TIME_DIMENSION)
# set time window for locations start time
# set condition restrictions
possible_vehicles = data.possible_vehicles
for location_idx, time_window in enumerate(data.location_time_windows):
index = manager.NodeToIndex(location_idx + data.vehicles_num)
time_dimension.CumulVar(index).SetRange(time_window[0], time_window[1])
routing.SetAllowedVehiclesForIndex(possible_vehicles[location_idx], index)
solver = routing.solver()
for i in range(data.vehicles_num):
routing.AddVariableMinimizedByFinalizer(
time_dimension.CumulVar(routing.Start(i)))
routing.AddVariableMinimizedByFinalizer(
time_dimension.CumulVar(routing.End(i)))
# set work time window for vehicles
for vehicle_index, work_time_window in enumerate(data.vehicles_work_time_windows):
start_index = routing.Start(vehicle_index)
time_dimension.CumulVar(start_index).SetRange(work_time_window[0],
work_time_window[0])
end_index = routing.End(vehicle_index)
time_dimension.CumulVar(end_index).SetRange(work_time_window[1],
work_time_window[1])
# set break time for vehicles
node_visit_transit = {}
for n in range(routing.Size()):
if n >= data.locations_num:
node_visit_transit[n] = 0
else:
node_visit_transit[n] = 1
break_intervals = {}
for v in range(data.vehicles_num):
vehicle_break = data.vehicles_break_time_window[v]
break_intervals[v] = [
solver.FixedDurationIntervalVar(vehicle_break[0],
vehicle_break[1],
vehicle_break[2],
True,
'Break for vehicle {}'.format(v))
]
time_dimension.SetBreakIntervalsOfVehicle(
break_intervals[v], v, node_visit_transit
)
search_parameters = pywrapcp.DefaultRoutingSearchParameters()
search_parameters.first_solution_strategy = (
routing_enums_pb2.FirstSolutionStrategy.PATH_CHEAPEST_ARC)
search_parameters.local_search_metaheuristic = (
routing_enums_pb2.LocalSearchMetaheuristic.GREEDY_DESCENT)
search_parameters.time_limit.seconds = TIME_LIMIT
search_parameters.log_search = True
solution = routing.SolveWithParameters(search_parameters)
return solution
if __name__ == '__main__':
data = DataSet(
time_matrix=[[0, 0, 4, 5, 5, 6],
[0, 0, 6, 4, 5, 5],
[1, 3, 0, 6, 5, 4],
[2, 1, 6, 0, 5, 4],
[2, 2, 5, 5, 0, 6],
[3, 2, 4, 4, 6, 0]],
locations_num=6,
vehicles_num=2,
vehicles_depots_indices=[0, 1],
vehicles_work_time_windows=[(720, 1080), (720, 1080)],
vehicles_break_time_window=[(720, 720, 15), (720, 720, 15)],
location_time_windows=[(735, 750), (915, 930), (915, 930), (975, 990)],
possible_vehicles=[[0], [1], [0], [1]]
)
solution = execute(data)
if solution is not None:
print("solution is found")
I want to make a linear regression program which visualizes the data to user. I'm using EJML for calculations and ScalaFX for front end. Everything is going fine but when I plot the data using Scatter Chart, the line drawn from the data is set to be rectangles which cover up the original data points. I would like to know how I can change the size, shape and transparency etc. of the plotted points.
Almost all of guides around JavaFX say that I should modify the CSS file (which doesn't automatically exist) in order to style my chart. I don't know how to do that in ScalaFX or even that is it possible to do that way. My result of searching every possible tutorial has been fruitless.
import scalafx.application.JFXApp
import scalafx.scene.Scene
import scalafx.scene.chart.ScatterChart
import scalafx.collections.ObservableBuffer
import scalafx.scene.chart.NumberAxis
import scalafx.scene.chart.XYChart
import scalafx.scene.shape.Line
import org.ejml.simple.SimpleMatrix
import scala.math.pow
import scala.collection.mutable.Buffer
object Plotting extends JFXApp {
/*
* Below are some arbitrary x and y values for a regression line
*/
val xValues = Array(Array(1.0, 1.0, 1.0, 1.0, 1.0, 1.0), Array(14.0, 19.0, 22.0, 26.0, 31.0, 43.0))
val yValues = Array(Array(51.0, 57.0, 66.0, 71.0, 72.0, 84.0))
val temp = yValues.flatten
val wrapper = xValues(1).zip(temp)
/*
* In the lines before stage what happens is that matrices for the x and y values are created, coefficients
* for the regression line are calculated with matrix operations and (x, y) points are calculated for the
* regression line.
*/
val X = new SimpleMatrix(xValues).transpose
val Y = new SimpleMatrix(yValues).transpose
val secondX = new SimpleMatrix(xValues(0).size, 2)
for (i <- 0 until xValues(0).size) {
secondX.set(i, 0, xValues(0)(i))
secondX.set(i, 1, xValues(1)(i))
}
val invertedSecondX = secondX.pseudoInverse()
val B = invertedSecondX.mult(Y)
val graphPoints = Buffer[(Double, Double)]()
for (i <- 0 to xValues(1).max.toInt) {
graphPoints.append((i.toDouble, B.get(0, 0) + i * B.get(1, 0)))
}
stage = new JFXApp.PrimaryStage {
title = "Demo"
scene = new Scene(400, 400) {
val xAxis = NumberAxis()
val yAxis = NumberAxis()
val pData = XYChart.Series[Number, Number](
"Data",
ObservableBuffer(wrapper.map(z => XYChart.Data[Number, Number](z._1, z._2)): _*))
val graph = XYChart.Series[Number, Number](
"RegressionLine",
ObservableBuffer(graphPoints.map(z => XYChart.Data[Number, Number](z._1, z._2)): _*))
val plot = new ScatterChart(xAxis, yAxis, ObservableBuffer(graph, pData))
root = plot
}
}
}
This certainly isn't as well documented as it might be... :-(
Stylesheets are typically placed in your project's resource directory. If you're using SBT (recommended), this would be src/main/resources.
In this example, I've added a stylesheet called MyCharts.css to this directory with the following contents:
/* Blue semi-transparent 4-pointed star, using SVG path. */
.default-color0.chart-symbol {
-fx-background-color: blue;
-fx-scale-shape: true;
-fx-shape: "M 0.0 10.0 L 3.0 3.0 L 10.0 0.0 L 3.0 -3.0 L 0.0 -10.0 L -3.0 -3.0 L -10.0 0.0 L -3.0 3.0 Z ";
-fx-opacity: 0.5;
}
/* Default shape is a rectangle. Here, we round it to become a red circle with a white
* center. Change the radius to control the size.
*/
.default-color1.chart-symbol {
-fx-background-color: red, white;
-fx-background-insets: 0, 2;
-fx-background-radius: 3px;
-fx-padding: 3px;
}
color0 will be used for the first data series (the regression line), color1 for the second (your scatter data). All other series use the default, JavaFX style.
(For more information on using scalable vector graphics (SVG) paths to define custom shapes, refer to the relevant section of the SVG specification.)
To have this stylesheet used by ScalaFX (JavaFX), you have a choice of options. To have them apply globally, add it to the main scene (which is what I've done below). Alternatively, if each chart needs a different style, you can add different stylesheets to specific charts. (BTW, I also added the standard includes import as this prevents many JavaFX-ScalaFX element conversion issues; otherwise, I've made no changes to your sources.)
import scalafx.Includes._
import scalafx.application.JFXApp
import scalafx.scene.Scene
import scalafx.scene.chart.ScatterChart
import scalafx.collections.ObservableBuffer
import scalafx.scene.chart.NumberAxis
import scalafx.scene.chart.XYChart
import scalafx.scene.shape.Line
import org.ejml.simple.SimpleMatrix
import scala.math.pow
import scala.collection.mutable.Buffer
object Plotting extends JFXApp {
/*
* Below are some arbitrary x and y values for a regression line
*/
val xValues = Array(Array(1.0, 1.0, 1.0, 1.0, 1.0, 1.0), Array(14.0, 19.0, 22.0, 26.0, 31.0, 43.0))
val yValues = Array(Array(51.0, 57.0, 66.0, 71.0, 72.0, 84.0))
val temp = yValues.flatten
val wrapper = xValues(1).zip(temp)
/*
* In the lines before stage what happens is that matrices for the x and y values are created, coefficients
* for the regression line are calculated with matrix operations and (x, y) points are calculated for the
* regression line.
*/
val X = new SimpleMatrix(xValues).transpose
val Y = new SimpleMatrix(yValues).transpose
val secondX = new SimpleMatrix(xValues(0).size, 2)
for (i <- 0 until xValues(0).size) {
secondX.set(i, 0, xValues(0)(i))
secondX.set(i, 1, xValues(1)(i))
}
val invertedSecondX = secondX.pseudoInverse()
val B = invertedSecondX.mult(Y)
val graphPoints = Buffer[(Double, Double)]()
for (i <- 0 to xValues(1).max.toInt) {
graphPoints.append((i.toDouble, B.get(0, 0) + i * B.get(1, 0)))
}
stage = new JFXApp.PrimaryStage {
title = "Demo"
scene = new Scene(400, 400) {
// Add our stylesheet.
stylesheets.add("MyCharts.css")
val xAxis = NumberAxis()
val yAxis = NumberAxis()
val pData = XYChart.Series[Number, Number](
"Data",
ObservableBuffer(wrapper.map(z => XYChart.Data[Number, Number](z._1, z._2)): _*))
val graph = XYChart.Series[Number, Number](
"RegressionLine",
ObservableBuffer(graphPoints.map(z => XYChart.Data[Number, Number](z._1, z._2)): _*))
val plot = new ScatterChart(xAxis, yAxis, ObservableBuffer(graph, pData))
root = plot
}
}
}
For further information in the CSS formatting options available (changing shapes, colors, transparency, etc.) refer to the JavaFX CSS Reference Guide.
The result looks like this:
I almost don't dare to add somethig to Mike Allens solution (wich is very good, as always), but this did not work out for me because I could not get my scala to find and/or process the .css file.
I would have done it this way if possible, but I just could not get it to work.
Here is what I came up with:
Suppose I have some data to display:
val xyExampleData: ObservableBuffer[(Double, Double)] = ObservableBuffer(Seq(
1 -> 1,
2 -> 4,
3 -> 9))
Then I convert this to a Series for the LineChart:
val DataPoints = ObservableBuffer(xyExampleData map { case (x, y) => XYChart.Data[Number, Number](x, y) })
val PointsToDisplay = XYChart.Series[Number, Number]("Points", DataPoints)
now I put this again into a Buffer, maybe with some other data from different series.
val lineChartBuffer = ObservableBuffer(PointsToDisplay, ...)
and finally I create my lineChart, wich I call (with lack of creativity) lineChart:
val lineChart = new LineChart(xAxis, yAxis, lineChartBuffer) {...}
The lines between data points can be recoloured now easily with:
lineChart.lookup(".default-color0.chart-series-line").setStyle("-fx-stroke: blue;")
This will change the Line-colour of the FIRST Dataset in the LineChartBuffer.
If you want to change the Line-Properties for the second you call
lineChart.lookup(".default-color1.chart-series-line")...
There is also "-fx-stroke-width: 3px;" to set the with of the line.
"-fx-opacity: 0.1;"
"-fx-stroke-dash-array: 10;"
-fx-fill: blue;"
are also usefull, but dont call the above line repeatedly, because the second call will override the first.
Instead concatenate all the strings into one:
lineChart.lookup(".default-color0.chart-series-line").setStyle("-fx-stroke: blue;-fx-opacity: 0.1;-fx-stroke-dash-array: 10;-fx-fill: blue;")
Now for the formatting of the Symbols at each data-Point:
unfortunately there seems to be no other way than to format each Symbol seperately:
lineChart.lookupAll(".default-color0.chart-line-symbol").asScala foreach { node => node.setStyle("-fx-background-color: blue, white;") }
for this to run you need import scala.collection.JavaConverters._
for the conversion from a java set to a scala set.
One can also make all data-poins from only one data-set invisible, for example:
lineChart.lookupAll(".default-color1.chart-line-symbol").asScala foreach { node => node.setVisible(false) }
To say this is a nice solution would be exaggerated.
And it has the big disadvantage, that you have to recolour or reformat every Symbol after adding a new Datapoint to one of the series in LineChartBuffer. If you don't, the new Symbols will have standard colours and settings.
The Lines stay, ones they are recoloured, I can't say why.
But the good side of it, one can always reformat curves in a Line Chart afterwards like this!