Develop Reference

pywinauto - TimeComX Basic print_control_identifiers() doesn't show all the options

I want to have automate process for this program: TimeComX Basic.
The script i wrote:
from pywinauto.application import Application as PyWinAutoApplication
from pywinauto.timings import wait_until
from pywinauto.keyboard import send_keys
import pywinauto
import os
import sys
from pywinauto import mouse
import traceback
#Hidernate pc
app2 = PyWinAutoApplication(backend="uia").connect(found_index=0,title="TimeComX Basic")
handle = pywinauto.findwindows.find_windows(title="TimeComX Basic")[0]
window = app2.window(handle=handle)
window.maximize()
window.set_focus()
app2.TimeComxBasic.print_control_identifiers()
#mouse.click(button='left', coords=(150, 960))
Note that to run this script you have to manually install and open TimeComX Basic.
The output:
Control Identifiers:
Dialog - 'TimeComX Basic' (L-11, T-11, R1931, B1019)
['TimeComX BasicDialog', 'Dialog', 'TimeComX Basic']
child_window(title="TimeComX Basic", control_type="Window")
|
| TitleBar - '' (L24, T-8, R1920, B34)
| ['TitleBar']
| |
| | Menu - 'System' (L0, T0, R22, B22)
| | ['Menu', 'System', 'SystemMenu', 'System0', 'System1']
| | child_window(title="System", auto_id="MenuBar", control_type="MenuBar")
| | |
| | | MenuItem - 'System' (L0, T0, R22, B22)
| | | ['MenuItem', 'System2', 'SystemMenuItem']
| | | child_window(title="System", control_type="MenuItem")
| |
| | Button - 'Minimize' (L1707, T0, R1778, B33)
| | ['MinimizeButton', 'Button', 'Minimize', 'Button0', 'Button1']
| | child_window(title="Minimize", control_type="Button")
| |
| | Button - 'Restore' (L1778, T0, R1848, B33)
| | ['Restore', 'Button2', 'RestoreButton']
| | child_window(title="Restore", control_type="Button")
| |
| | Button - 'Close' (L1848, T0, R1920, B33)
| | ['Close', 'Button3', 'CloseButton']
| | child_window(title="Close", control_type="Button")
As you can see it has options only for close, minimize and maximize buttons and for main menu. There is no option to "Start" button for example.
What can I do in this situation?

How to generate report(xlsx format) and Qweb Report in odoo 12

I'm working with account-financial-reporting module (OCA).
I want to add tree structure according to Code :
if account.code = "xxx000" or "yyy000" , then change the line style to bold ,
and get the other codes in ascending order . Example:
Code | |
-------------------------
xxx000 | |
-------------------------
|xxx001 |
-------------------------
|xxx003 |
-------------------------
yyy000 | |
-------------------------
|yyy002 |
-------------------------
|yyy005 |
-------------------------
def _generate_report_content(self, workbook, report):
if not report.show_partner_details:
# Display array header for account lines
self.write_array_header()
# For each account
for account in report.account_ids.filtered(lambda a: not a.hide_line):
if not report.show_partner_details:
# Display account lines
self.write_line(account, 'account')
Any help please ?

Unable to use saved model as starting point for training Baselines' MlpPolicy?

I'm currently using code from OpenAI baselines to train a model, using the following code in my train.py:
from baselines.common import tf_util as U
import tensorflow as tf
import gym, logging
from visak_dartdeepmimic import VisakDartDeepMimicArgParse
def train(env, initial_params_path,
save_interval, out_prefix, num_timesteps, num_cpus):
from baselines.ppo1 import mlp_policy, pposgd_simple
sess = U.make_session(num_cpu=num_cpus).__enter__()
U.initialize()
def policy_fn(name, ob_space, ac_space):
print("Policy with name: ", name)
policy = mlp_policy.MlpPolicy(name=name, ob_space=ob_space, ac_space=ac_space,
hid_size=64, num_hid_layers=2)
saver = tf.train.Saver()
if initial_params_path is not None:
print("Tried to restore from ", initial_params_path)
saver.restore(tf.get_default_session(), initial_params_path)
return policy
def callback_fn(local_vars, global_vars):
iters = local_vars["iters_so_far"]
saver = tf.train.Saver()
if iters % save_interval == 0:
saver.save(sess, out_prefix + str(iters))
pposgd_simple.learn(env, policy_fn,
max_timesteps=num_timesteps,
callback=callback_fn,
timesteps_per_actorbatch=2048,
clip_param=0.2, entcoeff=0.0,
optim_epochs=10, optim_stepsize=3e-4, optim_batchsize=64,
gamma=1.0, lam=0.95, schedule='linear',
)
env.close()
Which is based off of the code that OpenAI itself provides in the baselines repository
This works fine, except that I get some pretty weird looking learning curves which I suspect are due to some hyperparameters passed to the learn function which cause performance to decay / high variance as things go on (though I don't know for certain)
Anyways, to confirm this hypothesis I'd like to retrain the model but not from scratch: I'd like to start it off from a high point: say, iteration 1600 for which I have a saved model lying around (having saved it with saver.save in callback_fn
So now I call the train function, but this time I provide it with an inital_params_path pointing to the save prefix for iteration 1600. By my understanding, the call to saver.restore in policy_fn should restore "reset" the model to where it was at 1teration 1600 (and I've confirmed that the load routine runs using the print statement)
However, in practice I find that it's almost like nothing gets loaded. For instance, if I got statistics like
----------------------------------
| EpLenMean | 74.2 |
| EpRewMean | 38.7 |
| EpThisIter | 209 |
| EpisodesSoFar | 662438 |
| TimeElapsed | 2.15e+04 |
| TimestepsSoFar | 26230266 |
| ev_tdlam_before | 0.95 |
| loss_ent | 2.7640965 |
| loss_kl | 0.09064759 |
| loss_pol_entpen | 0.0 |
| loss_pol_surr | -0.048767302 |
| loss_vf_loss | 3.8620138 |
----------------------------------
for iteration 1600, then for iteration 1 of the new trial (ostensibly using 1600's parameters as a starting point), I get something like
----------------------------------
| EpLenMean | 2.12 |
| EpRewMean | 0.486 |
| EpThisIter | 7676 |
| EpisodesSoFar | 7676 |
| TimeElapsed | 12.3 |
| TimestepsSoFar | 16381 |
| ev_tdlam_before | -4.47 |
| loss_ent | 45.355236 |
| loss_kl | 0.016298374 |
| loss_pol_entpen | 0.0 |
| loss_pol_surr | -0.039200217 |
| loss_vf_loss | 0.043219414 |
----------------------------------
which is back to square one (this is around where my models trained from scratch start)
The funny thing is I know that the model is being saved properly at least, since I can actually replay it using eval.py
from baselines.common import tf_util as U
from baselines.ppo1 import mlp_policy, pposgd_simple
import numpy as np
import tensorflow as tf
class PolicyLoaderAgent(object):
"""The world's simplest agent!"""
def __init__(self, param_path, obs_space, action_space):
self.action_space = action_space
self.actor = mlp_policy.MlpPolicy("pi", obs_space, action_space,
hid_size = 64, num_hid_layers=2)
U.initialize()
saver = tf.train.Saver()
saver.restore(tf.get_default_session(), param_path)
def act(self, observation, reward, done):
action2, unknown = self.actor.act(False, observation)
return action2
if __name__ == "__main__":
parser = VisakDartDeepMimicArgParse()
parser.add_argument("--params-prefix", required=True, type=str)
args = parser.parse_args()
env = parser.get_env()
U.make_session(num_cpu=1).__enter__()
U.initialize()
agent = PolicyLoaderAgent(args.params_prefix, env.observation_space, env.action_space)
while True:
ob = env.reset(0, pos_stdv=0, vel_stdv=0)
done = False
while not done:
action = agent.act(ob, reward, done)
ob, reward, done, _ = env.step(action)
env.render()
and I can clearly see that its learned something as compared to an untrained baseline. The loading action is the same across both files (or rather, if there's a mistake there then I can't find it), so it appears probable to me that train.py is correctly loading the model and then, due to something in the pposdg_simple.learn function's, promptly forgets about it.
Could anyone shed some light on this situation?

Not sure if this is still relevant since the baselines repository has changed quite a bit since this question was posted, but it seems that you are not actually initialising the variables before restoring them. Try moving the call of U.initialize() inside your policy_fn:
def policy_fn(name, ob_space, ac_space):
print("Policy with name: ", name)
policy = mlp_policy.MlpPolicy(name=name, ob_space=ob_space,
ac_space=ac_space, hid_size=64, num_hid_layers=2)
saver = tf.train.Saver()
if initial_params_path is not None:
print("Tried to restore from ", initial_params_path)
U.initialize()
saver.restore(tf.get_default_session(), initial_params_path)
return policy

How to click using pywinauto

I would like to use pywinauto to control an image processing software.
First, I need to click a specific area (which is used for image dragging) to pop up a windows for path input. See the first figure.
Then, I need to input a path and click the button "Select Folder". See the second figure.
I tried:
from pywinauto import Desktop, Application, mouse, findwindows
from pywinauto.keyboard import SendKeys
app = Application(backend='uia').start(r"C:\Program Files\Duplicate Photo Cleaner\DuplicatePhotoCleaner.exe")
app.connect(path="DuplicatePhotoCleaner.exe")
app.DuplicatePhotoCleaner.print_control_identifiers()
Control Identifiers:
Dialog - 'Duplicate Photo Cleaner' (L440, T126, R1480, B915)
['Duplicate Photo Cleaner', 'Duplicate Photo CleanerDialog', 'Dialog']
child_window(title="Duplicate Photo Cleaner", control_type="Window")
|
| TitleBar - '' (L464, T129, R1472, B157)
| ['', 'TitleBar']
| |
| | Menu - 'System' (L448, T134, R470, B156)
| | ['System', 'Menu', 'SystemMenu', 'System0', 'System1']
| | child_window(title="System", auto_id="MenuBar", control_type="MenuBar")
| | |
| | | MenuItem - 'System' (L448, T134, R470, B156)
| | | ['System2', 'SystemMenuItem', 'MenuItem']
| | | child_window(title="System", control_type="MenuItem")
| |
| | Button - 'Minimize' (L1333, T127, R1380, B157)
| | ['Minimize', 'Button', 'MinimizeButton', 'Button0', 'Button1']
| | child_window(title="Minimize", control_type="Button")
| |
| | Button - 'Maximize' (L1380, T127, R1426, B157)
| | ['Button2', 'Maximize', 'MaximizeButton']
| | child_window(title="Maximize", control_type="Button")
| |
| | Button - 'Close' (L1426, T127, R1473, B157)
| | ['CloseButton', 'Button3', 'Close']
| | child_window(title="Close", control_type="Button")
Can anyone help?
Thank you very much.

Looks like the + button where you need to click to get the window (shown in second figure) is ownerdrawn.
So, there is only one way to bring up the "Add folder to search" window: use click_input method by passing coordinates.
Once the window comes up, you can use the below code to set the value:
app.DuplicatePhotoCleaner.child_window(title="Folder:", auto_id="1152", control_type="Edit").set_text('Hello world') #or
app.DuplicatePhotoCleaner['Folder:Edit'].set_text('Hello world')
Application().connect(title='Add folder to search')...
Please go though pywinauto docs for further info.

How to use Fl_Tabs in pyFltk

I am trying to make a card game programs using Fl_Tabs or Fl_Wizard but can't find any instructions on how to do so in pyFltk. Is there an example or any documentation showing the basic code of creating the Fl_Tabs because I couldn't find anything on the Internet.
from fltk import *
x=Fl_Tabs(20,20,500,500)
x.show()
Fl.run()
When I run the above program that, nothing will show.
Thanks in advance.

Example of how to use Fl_Tabs. This is a python version of the one from Erco's cheat page
#!/usr/bin/python
# python version of http://seriss.com/people/erco/fltk/#Fl_Tabs
# _____ _____
# __/ Aaa \/ Bbb \______________________
# | _______ |
# | |_______| |
# | _______ |
# | |_______| |
# | _______ |
# | |_______| |
# |______________________________________|
#
from fltk import *
def GroupAAA():
aaa = Fl_Group(10,35,500-20,200-45,"Aaa")
b1 = Fl_Button(50, 60,90,25,"Button A1"); b1.color(88+1)
b2 = Fl_Button(50, 90,90,25,"Button A2"); b2.color(88+2)
b3 = Fl_Button(50,120,90,25,"Button A3"); b3.color(88+3)
aaa.end()
def GroupBBB():
bbb = Fl_Group(10,35,500-10,200-35,"Bbb")
b1 = Fl_Button( 50,60,90,25,"Button B1"); b1.color(88+1)
b2 = Fl_Button(150,60,90,25,"Button B2"); b2.color(88+3)
b3 = Fl_Button(250,60,90,25,"Button B3"); b3.color(88+5)
b4 = Fl_Button( 50,90,90,25,"Button B4"); b4.color(88+2)
b5 = Fl_Button(150,90,90,25,"Button B5"); b5.color(88+4)
b6 = Fl_Button(250,90,90,25,"Button B6"); b6.color(88+6)
bbb.end()
win = Fl_Window(500,200,"Tabs Example")
tabs = Fl_Tabs(10,10,500-20,200-20)
GroupAAA()
GroupBBB()
tabs.end()
win.end()
win.show()
Fl.run()