🌐 Mailman 3 June 2020 - Tutor - python.org

Scripting Calligra sheets with Python
by Jim Byrnes Jan. 31, 2022

Jan. 31, 2022

I am trying to script Calligra Sheets (formerly KSpread) with python. I have gotten some of the included example scripts to run so I know python scripting is running. I found the following snippet on their website: import KSpread sheet = KSpread.view().sheet() # swap text of B5 and C6 t1 = sheet.text("B5") t2 = sheet.text(6,3) sheet.setText("B5", t2) sheet.setText(6, 3, t1) # swap value of D7 and E8 v1 = sheet.value("D7") v2 = sheet.value(8,5) sheet.setValue("D7", v2) sheet.setValue(8, 5, v1) Error: 'str' object has no attribute 'text' File "file:///usr/share/kde4/apps/sheets/scripts/extensions/myswap.py", line 4, in <module> This error message appeared in a dialog box. I've taught myself some Python but I don't understand what is happening here. Shouldn't t1 be a sheet object? What would cause it to be a str instead? Thanks, Jim

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Subplotting Figure Handles - Python Help
by Lakshay Verma June 29, 2020

June 29, 2020

Hello, I am currently working on a project whereby I collect data from a redshift database and do some analysis on it. I have made several functions that all generate figures, and output them as figure handles. For example, a function will take in query text, run the query in the database, and then I'll use pandas to store the data in a dataframe, and matplotlib to graph the data and do some statistical analysis on it. The function will then return a figure handle ( <Figure size 432x288> ). How do I go about subplotting these figures? Because right now, I am not able to subplot them and it's really frustrating because when I save the figures to a pdf, I'm getting 20+ page pdfs whereas I would like 5 pages whereby I can group plots that analyze similar data. I'd be happy to discuss this over a video call so you can see the code and get a better idea. Thanks! Regards, Lakshay Verma Chemical Engineering Student University of Waterloo E: l2verma(a)edu.uwaterloo.ca<mailto:l2verma@edu.uwaterloo.ca> P: (647) 721-8414

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key parameter in sorted
by Manprit Singh June 29, 2020

June 29, 2020

Sir, According to the python documentation,function sorted <https://docs.python.org/3/library/functions.html#sorted> have a *key* parameter to specify a function to be called on each element prior to making comparisons. consider a case if i am using python 3.6, and i have to remove duplicates from a list, while maintaining the order of the elements, i just need to know if my solution(which is given below ) is syntactically correct and efficient or not . The result i am getting is correct and have tested it so many times . Reason for asking this question is, here i have passed method of list data type (list.index) to the key parameter of sorted function . Can i pass a method to the key parameter of sorted function ? lx = [2, 5, 4, 6, 4, 2, 3, 6] sorted(set(lx), key=lx.index) Regards Manprit Singh

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Running Python Scripts at same time
by John Weller June 28, 2020

June 28, 2020

I have a Python program which will be running 24/7 (I hope 😊). It is generating data in a file which I want to clean up overnight. The way I am looking at doing it is to run a separate program as a Cron job at midnight – will that work? The alternative is to add it to the loop and check for the time. I have tried researching this but only got even more confused. Thanks John John Weller 01380 723235 07976 393631

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TypeError: unhashable type: 'pygame.math.Vector2'
by Cravan June 27, 2020

June 27, 2020

Hi all, I recently embarked on learning Q-learning in python, but got an error that I’ve no idea how to fix. So here’s my code in one of my .py files: ``` class Zomb(pg.sprite.Sprite): def __init__(self, game, m, n): self.groups = game.all_sprites, game.zombs pg.sprite.Sprite.__init__(self, self.groups) self.game = game self.image = game.zomb_img self.rect = self.image.get_rect() self.pos = vec(m, n) * TILESIZE self.m = m self.n = n #self.pos.x will return the x-coordinate in the grid world self.stateSpace = [i for i in range(int(GRIDWIDTH) * int(GRIDHEIGHT))] self.actionSpace = {'U': vec(0, -self.m), 'D': vec(0, self.m), 'L': vec(-1,0), 'R': vec(1,0)} self.possibleActions = ['U', 'D', 'L', 'R'] self.vel = vec(0, 0) self.acc = vec(0, 0) self.rect.center = self.pos self.rotate = 0 def setState(self, state): self.pos = state def TerminalState(self, state): if self.game.health == 0: return True else: return False def step(self, action): x, y = self.pos.x, self.pos.y resultingState = self.pos + self.actionSpace[action] self.game.reward = -1 if not self.TerminalState(resultingState) else 0 self.setState(resultingState) return resultingState,self.game.reward,\ self.TerminalState(resultingState), None def actionSpaceSample(self): return np.random.choice(self.possibleActions) def update(self): self.image = game.zomb_img self.rect = self.image.get_rect() self.rect.center = self.pos self.acc = vec(ZOMB_SPEED, 0) self.acc += self.vel * -1 self.vel += self.acc * self.game.dt self.pos += self.vel * self.game.dt + 0.5 * self.acc * self.game.dt ** 2 print(self.pos) ``` And here’s the processing part in my main .py file: ````` g = Game() def maxAction(Q, state, actions): values = np.array([Q[state,a] for a in actions]) action = np.argmax(values) return actions[action] while True: g.new() ALPHA = 0.1 GAMMA = 1.0 EPS = 1.0 Q = {} for zomb in g.zombs: for state in zomb.stateSpace: for action in zomb.possibleActions: Q[state, action] = 0 numGames = 10 totalRewards = np.zeros(numGames) for i in range(numGames): print('starting game ', i) done = False g.new() epRewards = 0 while not done: rand = np.random.random() for zomb in g.zombs: observation = zomb.pos action = maxAction(Q, observation, zomb.possibleActions) if rand < (1-EPS) \ else zomb.actionSpaceSample() observationnew, reward, done, info = zomb.step(action) epRewards += reward for zomb in g.zombs: possible_actions = zomb.possibleActions action = maxAction(Q, observationnew, possible_actions) Q[observation,action] = Q[observation,action] + ALPHA*(reward + \ GAMMA*Q[observationnew,action_] - Q[observation,action]) observation = observationnew if EPS - 2 / numGames > 0: EPS -= 2 / numGames else: EPS = 0 totalRewards[i] = epRewards g.run() ```` However when I run the second file I encounter an error: Traceback (most recent call last): File "maze.py", line 170, in <module> action = maxAction(Q, observationnew, possible_actions) File "maze.py", line 136, in maxAction values = np.array([Q[state,a] for a in actions]) File "maze.py", line 136, in <listcomp> values = np.array([Q[state,a] for a in actions]) TypeError: unhashable type: 'pygame.math.Vector2'

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Advice on working with Firefox's cookies.sqlite
by Jim June 27, 2020

June 27, 2020

I am writing a script that will delete unwanted cookies from the Firefox cookies.sqlite file based on a white list. I found that I could not connect to the file in my profile directory when Firefox is active. If I copy cookies.sqlite to a temporary location the script will connect to the db and delete unwanted cookies. The problem is when I copy the file back to my profile directory and use preferences to check for cookies, the deleted cookies have reappeared. This seems to be the result of a file called cookies.sqlite-wal in the same directory. More info on sqlite-wal here: https://sqlite.org/wal.html Based on what I read in the above link and some googling it looked like the cookies.sqlite-wal file had not yet updated the cookies.sqlite file. So I copied the cookies.sqlite file and looked at it with a db viewer and the cookies I wanted to delete were not there. So I copied the cookies.sqlite-wal file to the same location and then ran this portion of the script and the cookies appeared in the db. def sqlite3Connect(): ''' Connect to cookies database ''' conn = sqlite3.connect(WORK_AREA + "cookies.sqlite") conn.execute('PRAGMA wal_checkpoint(TRUNCATE)') return conn Checking the db the cookies I wanted to delete were now in the db. I then ran the part of the script that deleted the cookies. After running the db viewer I could see the cookies had been deleted. However when I deleted the cookies.sqlite and cookes.sqlite-wal files from my firefox profile and copied my updated cookies.sqlite file to my profile, checking with firefox still showed the cookies I tried to delete. At this point I am out of ideas and wondering if anyone knows how to successfully manipulate this file? Thanks, Jim

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column heading
by SUMAN KANTI ROY June 26, 2020

June 26, 2020

a=[1,2,3,4,5,6,7,8,9,10] arr=[[1,2],[2,23],[4,5]] dic={} for i in range(0,len(arr)): if arr[i][0] not in dic: dic[arr[i][0]]=arr[i][1] arr1=[] def find_key(val,dic): for i in dic.keys(): if dic[i]==val: return i new_arr=sorted(dic.values(),reverse=True) ans= [[0 for i in range(3)] for i in range(len(new_arr))] for i in range(len(new_arr)): ans[i][0]=a[i] ans[i][1]=find_key(new_arr[i],dic) ans[i][2]=new_arr[i] print(ans) 0/p=====[[1, 2, 23], [2, 4, 5], [3, 1, 2]] I want to make it 3 column matrix ,,,with column heading name['device',R.B.N','CQI'] please guide me...

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Review: Good Habits for Great Coding
by DL Neil June 25, 2020

June 25, 2020

Good Habits for Great Coding : Improving Programming Skills with Examples in Python, Michael Stueben, Apress, 2018 My disappointment with this book is largely a consequence of my own expectations. The title raised hopes that it would improve my own coding efforts, or that it would offer teaching techniques which might improve my approach to training in IT. The first indication of a poor 'fit' was its dedication "to the isolated CS teacher and student". Sadly, many of the errors or examples of poor teaching would not have occurred, had the author been less 'isolated' and spent time interacting with the world-wide Python community! The Introduction reveals the author to be a victim of his environment. School math and 'toy examples' lead to the conclusion that short/algebra-like variable-identification is "efficient" and that 'pretty boxes' around module docstrings add value and improve 'looks'. Yet, the later chapter on "Self-Documenting Code" reverses such advice, instead preferring descriptively-meaningful names. Whilst it is not necessary for all code to adhere to PEP-8 standards, it is difficult to imagine why one would deliberately teach differently, eg re-defining indentation. Generally, C- and Java-esque techniques do not transfer to Python. For example, camel-case for variable or function names is frowned-upon, but such, and the likes of for...in range(...) constructs, appear with alarming frequency. In eschewing the generic 'in-joke' function names such as "foo", "bar", and "baz" (which few would ever use in practice anyway), the author instead promotes the no-less meaningful "doIt()" for general-purposes, purportedly following a verb-object name-form recommendation. Similarly, but contrary to the usual EAFP/'we're all adults' ethos of the Python world; in the book's programmer-as-user example exercises there is emphasis on "testing for any pre-condition and boundary condition". In that environment, using assert to 'prove' an input or a result might be acceptable, but in 'the real world' risking such a hard-stop (decorated by a user-alarming error message and trace-back) would not. Conversely, in the "Expert Advice" chapter we read that "Robust code...heals itself...crashes gracefully". A single, solitary use of the pythonic try...except structure can be recalled, but those keywords do not appear in the book's index. The 'batteries included' advantages of the Python Standard Library don't receive coverage. Indeed the author details 'utility code' to debug-print a matrix structure, whereas most of us would reach for the pprint library. Ironically, recalling the development of such code, gives occasion to introduce the concept of "YAGNI". Stueben concludes (rightly) that the problem with students not appreciating the need for style or readability is implicit within the traditional 'work by yourself, and for yourself' methods of schooling (this is changing); and how, to demand apparently-abstract adherence to standards or convention would create an apposite relationship between teacher and student. No doubt! Confirming this admission of defeat, the chapter on "Style" is sandwiched between two entitled, "Coding Tricks" and "More Coding Tricks". However, professionals would say that coding-style cannot be ignored. Accordingly most trainers attempt to model 'best-practice' in our training materials. Sadly, the reality is that a good docstring for a toy-example may be longer than the actual code. Thus, such ideals may actually interfere with the learning process. Exigencies apply! Therefore, one of the book's (and the class's) missed-opportunities is that professionals (and students) may only ever convince themselves of such a need, after 'suffering' through reading and maintaining/improving others' code - at which point we 'discover' the struggles of wading-through a 'tangled mess'. (Perhaps a message to all of us?) There is a useful chapter on Testing, followed by four weakly-justified pages on Defensive Programming. However, the TDD (and others) approach of separating code from test modules is not mentioned. Debug-prints are favored over debuggers. Scaffolding "which will be removed eventually" is recommended, despite the virtue of test-runners remaining available for future use. There is a nod towards Numerical Analysis in coverage of the 'dangers' inherent in floating-point computation, which many would be wise to absorb! Unfortunately, this content will not encourage or extend your experience into Python's Decimal library. The math-bias shows in the strength of algorithmic coverage, and the dearth of data considerations. Even Data Scientists preface their analyses with data-collection, and data-cleaning processes! Within an environment concentrating on implementing algorithms with simple (and tailored) data formats, such a view may be justifiable. Object-Oriented Programming receives short-shrift, which is likely appropriate, given the examples' subject-matter, and the lack of (need for, teaching-approach using) re-use. Sadly, in a discussion of the 'Traveling Salesman Problem' he recommends a complex structure of mutable lists for x,y co-ordinates to which id-s (pointers) are added. Whereas, a custom class would simplify understanding and better relieve the loss of "control" (of the code-solution) he describes. Some will support the chapter "Beware of OOP", whilst others recoil in horror. Again, a divergence between ComSc and industry, and a predilection for edge-cases seem to justify the author's views. The many advantages built-in to popular Python IDEs are also a missed opportunity. Likewise, GUI and web interfaces are not discussed. That said, time is devoted to building speed-tests as means of refinement and an aid to refactoring. To be fair, it is not easy to convince high school students, or for that matter many neophyte programmers, of the virtues of 'industry standards', eg avoiding 'clever code' to enhance readability. Which implies that the title of the book is too general and requires a more descriptive (and accurate) sub-title, eg "for High School Computing". Further, to be true to the comparisons drawn in "Expert Advice", the book's sub-title should replace its current "Programming Skills" with the term "Computer Science", thus justifying the author's view that "professional software developers work in a significantly different world from the C.S. student. ...team...legacy code...end users...convenient interfaces...consistency" contrasting radically with "code up algorithms that will be run one time in front of the teacher". This book's title is a publisher's marketing-masterpiece of community appeal. However, the content fails to deliver on the promise. If you seek advice about professional practice, or to improve Python skills, look elsewhere. If you are a High School student considering IT as a career, look elsewhere. However, if you are a math-based, ComSc student (including, perhaps, first-year University Under-Graduates) and you're studying algorithms, this book includes much food-for-thought. The reviewer's first degree included a ComSc major. He has worked in IT and business for decades, and is an international consultant and vocational trainer. -- Regards, =dn

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Testing classes with class variables: How best to reset class state between tests?
by boB Stepp June 25, 2020

June 25, 2020

class MyClass: class_var1 = value1 class_var2 = value2 ... methods some which may affect class variables This is a spinoff of my earlier questions about creating an instance counter for a class. I started to implement this in my actual code and started to write tests for the counter. I quickly realized that the counter variable retained state between tests, and, in fact, already written tests would now populate the counter before I ever got around to the new counter tests. So how to best deal with this? Two ideas come immediately to mind: 1) Use setup and/or teardown methods to manually reset all class variables to their desired initial state. A downside of this is that I will have to go back to all previous tests involving this class and add these methods. 2) Forcibly reloading the import with importlib.reload(myclassmodule). This seems like using a sledge hammer to swat a fly. And it would seem that it would have to be done repeatedly and added to earlier tests as well. Does Python provide a better solution? For the record my test runner is currently pytest. TIA! -- boB

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TypeError: unhashable type: 'pygame.math.Vector2'
by Cravan June 25, 2020

June 25, 2020

Hi all, I recently began learning Q-learning in Python but encountered an error. Here’s my code for the Agent in one of my .py files: ```` class Zomb(pg.sprite.Sprite): def __init__(self, game, m, n): self.groups = game.all_sprites, game.zombs pg.sprite.Sprite.__init__(self, self.groups) self.game = game self.image = game.zomb_img self.rect = self.image.get_rect() self.pos = vec(m, n) * TILESIZE self.m = m self.n = n #self.pos.x will return the x-coordinate in the grid world self.stateSpace = [i for i in range(int(GRIDWIDTH) * int(GRIDHEIGHT))] self.actionSpace = {'U': vec(0, -self.m), 'D': vec(0, self.m), 'L': vec(-1,0), 'R': vec(1,0)} self.possibleActions = ['U', 'D', 'L', 'R'] self.vel = vec(0, 0) self.acc = vec(0, 0) self.rect.center = self.pos self.rotate = 0 def setState(self, state): self.pos = state def TerminalState(self, state): if self.game.health == 0: return True else: return False def step(self, action): x, y = self.pos.x, self.pos.y resultingState = self.pos + self.actionSpace[action] self.game.reward = -1 if not self.TerminalState(resultingState) else 0 self.setState(resultingState) return resultingState,self.game.reward,\ self.TerminalState(resultingState), None def actionSpaceSample(self): return np.random.choice(self.possibleActions) def update(self): self.image = game.zomb_img self.rect = self.image.get_rect() self.rect.center = self.pos self.acc = vec(ZOMB_SPEED, 0) self.acc += self.vel * -1 self.vel += self.acc * self.game.dt self.pos += self.vel * self.game.dt + 0.5 * self.acc * self.game.dt ** 2 print(self.pos) ```` And here’s the processing and implementation of Q-learning in my main code: ```` g = Game() def maxAction(Q, state, actions): values = np.array([Q[state,a] for a in actions]) action = np.argmax(values) return actions[action] while True: g.new() ALPHA = 0.1 GAMMA = 1.0 EPS = 1.0 Q = {} for zomb in g.zombs: for state in zomb.stateSpace: for action in zomb.possibleActions: Q[state, action] = 0 numGames = 10 totalRewards = np.zeros(numGames) for i in range(numGames): print('starting game ', i) done = False g.new() epRewards = 0 while not done: rand = np.random.random() for zomb in g.zombs: observation = zomb.pos action = maxAction(Q, observation, zomb.possibleActions) if rand < (1-EPS) \ else zomb.actionSpaceSample() observationnew, reward, done, info = zomb.step(action) epRewards += reward for zomb in g.zombs: possible_actions = zomb.possibleActions action = maxAction(Q, observationnew, possible_actions) Q[observation,action] = Q[observation,action] + ALPHA*(reward + \ GAMMA*Q[observationnew,action_] - Q[observation,action]) observation = observationnew if EPS - 2 / numGames > 0: EPS -= 2 / numGames else: EPS = 0 totalRewards[i] = epRewards g.run() ```` However, this error popped out: ###### Traceback (most recent call last): File "maze.py", line 170, in <module> action = maxAction(Q, observationnew, possible_actions) File "maze.py", line 136, in maxAction values = np.array([Q[state,a] for a in actions]) File "maze.py", line 136, in <listcomp> values = np.array([Q[state,a] for a in actions]) TypeError: unhashable type: 'pygame.math.Vector2' ###### Could someone provide me with a way to rectify this problem? (removing the vector sign in the dictionary doesn’t seem to help, another error pops up). Thanks, Cravan P.S. Also erm I made a buncha mistakes along the way, would appreciate any suggestion or help. Thanks!

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