在本文中，我们将详细介绍Jacobian和Hessian在`scipy.optimize.minimize`中输入的各个方面，同时，我们也将为您带来关于cannotimportname''imresi

在本文中，我们将详细介绍Jacobian和Hessian在`scipy.optimize.minimize`中输入的各个方面，同时，我们也将为您带来关于cannot import name ''imresize'' from ''scipy.misc''、CISC124 Transit Optimizer Tool、Da''Quan Bowers at risk to becoming minimize simply、electron 在window 上 maximize minimize restore 三个函数无效的有用知识。

• Jacobian和Hessian在`scipy.optimize.minimize`中输入
• cannot import name ''imresize'' from ''scipy.misc''
• CISC124 Transit Optimizer Tool
• Da''Quan Bowers at risk to becoming minimize simply
• electron 在window 上 maximize minimize restore 三个函数无效

Jacobian和Hessian在`scipy.optimize.minimize`中输入

import numpy as np
from scipy.optimize import minimize
from numdifftools import Jacobian,Hessian

def fun(x,a):
    return (x[0] - 1)**2 + (x[1] - a)**2

x0 = np.array([2,0]) # initial guess
a = 2.5

res = minimize(fun,x0,args=(a),method='dogleg',jac=Jacobian(fun)([2,0]),hess=Hessian(fun)([2,0]))

print(res)

res = minimize(fun,args=a,jac=Jacobian(lambda x: fun(x,a)),hess=Hessian(lambda x: fun(x,a)))

cannot import name ''imresize'' from ''scipy.misc''

问题：执行以下python命令，出现这样的错误     cannot import name ''imresize'' from ''scipy.misc''

from scipy.misc import imresize

原因及解决办法：

1. 缺少依赖项。网上出现类似问题，给出的答案是需要安装PIL，即

   pip install pillow

   但是如果你的scipy是通过conda安装的，conda会自动安装所需的依赖项，因此不需要额外安装PIL。

2. scipy版本问题。scipy>=1.0.0不再包含函数imresize，官网给出的解释及解决办法如下：

   imresize is deprecated! imresize is deprecated in SciPy 1.0.0, and will be removed in 1.3.0. Use Pillow instead: numpy.array(Image.fromarray(arr).resize()).

   参考scipy官网

CISC124 Transit Optimizer Tool

CISC124 – Winter 2019
Lab Assignment 3: Mass Transit Optimizer Tool
Page 1
Aim – To implement a small and simple prototype tool, named MTOptimizer, to explore
rough fleet size optimizations in the hourly deployment of the vehicles used in the different
public transportation modalities (i.e., buses, subways, trains) at the city of Toronto. The tool
takes as input: 1) a representative sample of a typical workday passenger ridership
(approximately 10% of the true number of daily passengers) for all means of transportation,
2) fleet descriptions and vehicle properties for each means of transportation. The tool
outputs hourly estimates of the optimal number of vehicles, for each means of
transportation, that should be in operation, in a 24-hour period, to satisfy the projected
demand indicated by the processed sample.
MTOptimizer reads passenger and vehicle data from text files containing comma-separated
values. It writes results generated from the processing of its input data to a text file. The
problem description has been simplified in many ways to keep a very small number of scenarios
to consider and produce rough estimates of fleet size allocations. For example, each means of
transportation is represented just by one operation route (i.e., one line of subway, or GO-train,
or streetcar, or bus, or GO-bus).
The two main learning objectives in this assignment are: 1) to design and implement small class
hierarchies, and possibly use interfaces to delineate those classes, which could efficiently
support the programming required in a given application domain (e.g., public transportation),
and 2) to give you practice with allocating attributes and behaviours (i.e., methods) in class
hierarchies that facilitate the reusing of programming code and the instantiation of objects that
effectively encapsulate data and functionality in support of applications involving multiple
actors.
To attain these goals, it is advisable that, before writing any code, you clearly identify and
describe to yourself in plain language the nature, core attributes and minimum behaviours of
the root class of the hierarchy(ies) that you will use in programming the MTOptimizer tool. It is
a good idea to jot down a stylized class diagram, as shown in class. Before diagraming and
analyzing your classes, manually inspect the input data for potential patterns, nature of values
and unusual or extreme values. This will greatly help you shape the class hierarchy(ies) you will
create to support your programming and handle exceptions.
General Description
As a software developer with the Toronto Transit Commission (TTC), you have been tasked to
write a software prototype, in Java, named MTOptimizer. This tool would allow the analysts at
the TTC to process daily passenger ridership data and, as a result, create in-advance hourlybased
“in-operation-vehicle fleet sizes” for the different means of transportation supervised by
this organization.
MTOptimizer reads in the following text files:
1) File “ridership.txt” contains about 65,000 lines, which represent a 24-hour sample (00:00
hours to 24:00 hours) of the ridership for all means of transportation in the city of Toronto,
during a typical working day. Each line represents the recorded data for one passenger,
CISC124 – Winter 2019
Lab Assignment 3: Mass Transit Optimizer Tool
Page 2
riding on one of the means of transportation, timestamped at a given hour of the day (i.e.,
hours 1-24). The list of comma-separated values included on each line is the following:
Person Identification – A 7-digit card identification number for a rider using a monthly
pass, or a 16-character ticket code (whose format is: Tyyyymmddnnnnnnn, where
yyyy=year, mm=month, dd=day, nnnnnnn=unique 7-digit number) for a rider using a
10-trip discount ticket, or a “*” for a casual rider using tokens or paying cash fares.
Transportation Modality – A one-character field indicating the means of transportation
used by the rider, with values as follows: (S) Subway, (G) GO-train, (X) Streetcars, (C)
City bus, (D) GO-Bus.
Age Group – A 1-character field indicating whether the rider is a child (C), an adult (A),
or a senior citizen (S).
Hour of the day – A 1- or 2-digit number (1 – 24) indicating the hour of the day when
the rider used the specified transportation modality.
Date – The date when the rider used the specified transportation modality in the format
yyyymmdd, where yyyy is 2018 or 2019, mm is in the range 01-12, and dd is in the
range 00-31.
2) File “subways.txt” describes the fleet of trains on the Toronto subway system. Each line
represents a single train. The comma-separated values for each line are:
Unit Number – A unique 4-digit number that identifies a vehicle in the TTC’s
Computerized Information System.
Train Identification – A 3-digit number in the range 100-999.
Number of Cars– A 1-digit indicating the number of cars per train.
Passengers per Car – A 2-digit number indicating a car’s maximum passenger capacity.
Operational Status – A 1-character field indicating whether train is available or not on
the date indicated in the Operation Date field: Available (A), Unavailable (U), Unknown
(*).
Operational Date – Date when the train operational status is valid, in the format
yyyymmdd.
3) File “gotrains.txt” describes the fleet of GO-trains to Toronto’s suburban areas. Each line
represents a single train. The comma-separated values for each line are:
Unit Number – A unique 4-digit number that identifies a vehicle in the TTC’s
Computerized Information System.
Train Identification – A 4-character string with the format Gnnn, where nnn is in the
range 100-999.
Train Capacity– A 2- to 3-digit number indicating the train’s maximum passenger
capacity.
CISC124 – Winter 2019
Lab Assignment 3: Mass Transit Optimizer Tool
Page 3
4) File “streetcars.txt” describes the fleet of streetcars in Toronto’s downtown area. Each line
represents a single streetcar. The comma-separated values for each line are:
Unit Number – A unique 4-digit number that identifies a vehicle in the TTC’s
Computerized Information System.
Streetcar Identification – A 5-character string with the format SCnnn, where nnn is in
the range 100-999.
Type – A 1-character field indicating a single (S) or double (D) section streetcar. (Note:
each section of the street car can accommodate a maximum of 40 passengers).
5) File “buses.txt” describes the fleet of buses in Toronto’s metropolitan area. Each line
represents a single bus. The comma-separated values for each line are:
Unit Number – A unique 4-digit number that identifies a vehicle in the TTC’s
Computerized Information System.
Bus Identification – A 5-character string with the format MBnnn, where nnn is in the
range 100-999.
Capacity – A 2-digit number indicating the maximum number of passengers that the bus
can accommodate.
6) File “gobuses.txt” describes the fleet of buses to Toronto’s suburban areas. Each line
represents a single bus. The comma-separated values for each line are:
Unit Number – A unique 4-digit number that identifies a vehicle in the TTC’s
Computerized Information System.
Bus Identification – A 5-character string with the format GBnnn, where nnn is in the
range 100-999.
Capacity – A 2-digit number indicating the maximum number of passengers that the bus
can accommodate.

Class Hierarchies Design, Program Implementation and Others
I – Class Hierarchies Design
The analysis of the ridership and fleet description files will give many clues to design two or
more class hierarchies, which will greatly expedite the programming of the MTOptimizer
prototype. It is highly advisable that you consider at least one hierarchy centered on
passengers, and another one centered on types of transportation vehicles. The root of each
hierarchy must be a non-instantiable class (i.e., an abstract class) that captures essential
hierarchy-wide attributes and methods. The derived classes should clearly help you with
making easy the programming required to process the input files and produce optimal hourly
operational fleet sizes for an entire day.

CISC124 – Winter 2019
Lab Assignment 3: Mass Transit Optimizer Tool
Page 4
I – Program Implementation
(a) Assumptions
Your program will use the following assumptions:
Files “subways.txt”, “gotrains.txt”, “streetcars.txt, “buses.txt” and “gobuses.txt” contain
fleet sizes that will always be sufficient to satisfy the passenger ridership specified in file
“ridership.txt”. These files do not contain invalid or missing data.
Because file “ridership.txt” is assembled with data from highly heterogeneous sources (e.g.,
turnstile readers, on-board readers, manual input, etc.), it is known to contain some lines
with missing or faulty values (approximately 0.01% of the total number of lines). These bad
lines will be skipped and reported in an “errorlog.txt” files.
The optimal fleet size for each hour of the day and every means of transportation is the
minimum number of vehicles (i.e., trains, buses, etc.) that will accommodate the number of
passengers specified in the “ridership.txt” file for the respective hour of the day. When
computing the total numbers of riders for a given hour assume that an adult is one rider, a
child is a 0.75 rider, and a senior citizen is a 1.25 rider.
(b) Properties and behaviours
Your program will have the properties and behaviours described below:
Its main class will be named MTOptimizer.
It will create object instances of the classes in the hierarchies that you have designed ir
order to generate of the lines of the “InOperationFleets.txt” output file.
The contents of file “InOperationFleets.txt” is laid out in sections. Each section starts with
one of the strings [Trains], [GoTrains], [Streetcars], [Buses], [GoBuses]. Each of these
section starting lines will be followed by 24 subsections, each one starting with a line [Hour
= nn], where nn = 1 – 24. Each [Hour=nn] line will be followed by one or more lines showing
the vehicles in use during that hour. The format of these lines will be train: description, or
gotrain: description, or streetcar: description, or bus: description, or gobus: description.
Each section will be finished with a [Count=nn] line, where nn=number of vehicles in the inoperation
fleet for the current hour. The description field will match the fields provided in
the input files for the different means of transportation.
It will insert a blank line after each section, except after the last one.
It will select the vehicles, within a fleet type, that will be part of the fleet representing the
optimal fleet size for every hour of the day. It will use the riders’ age group to compute, as
explained before, the total hourly ridership for each means of transportation.
It will produce a “errorlog.txt” file listing any bad lines in the “ridership.txt” file, with an
associated error message clearly describing the nature of the error.

CISC124 – Winter 2019
Lab Assignment 3: Mass Transit Optimizer Tool
Page 5
II – Submission
Compress the source code for class MTOptimizer, the classes in the hierarchies that you have
designed, files InOperationFleets.txt and errorlog.txt, into a single zip archive file named:
[your student number]_Assignment3.zip
For example, if your student number is 12345678 then you will name your submission file as
12345678_Assignment2.zip
Submit file [your student number]_Assignment3.zip via onQ by March 28, 2019, 11:00 p.m.
III – Marking Scheme
Implementation of class MTOptimizer 8 points
Implementation of the class hierarchies 4 points
Generation of InOperationFleets.txt file 4 points
Generation of errorlog.txt file 2 points
Code style and documentation 2 points
__
WX：codehelp

Da''Quan Bowers at risk to becoming minimize simply

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electron 在window 上 maximize minimize restore 三个函数无效

electron 在window 上 maximize minimize restore 三个方法死活不生效。

原来是在调用整个三个方法前需要把窗口焦点去除，才能正确触发

   ipcMain.on(''videoWindow.toggleMaximize'', (event: any, data: string) => {
      this.mainAppWindow.blur();
      if (this.mainAppWindow.isMaximized()) {
        // true表示窗口已最大化.
        this.mainAppWindow.restore(); // 将窗口恢复为之前的状态
      } else {
        this.mainAppWindow.maximize(); // 窗口最大化
      }
      this.mainAppWindow.focus();
    });
``

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