Develop Reference

Button Not Interacting (Custom Type) - Pywinauto

I am working on automating a process that uses our ancient HRIS system that unfortunately doesn't have API Access.
I am fairly new to Python, so I have been taking this task bit by bit. I've managed to connect to the app and input my username and password to sign in. However, I am stuck on selecting a menu item. I've tried everything that I know to do and have Googled until I've gone cross-eyed.
Dialog - 'City of Conway LIVE Springbrook V7' (L0, T0, R1032, B1039)
['Dialog', 'City of Conway LIVE Springbrook V7Dialog', 'City of Conway LIVE Springbrook V7', 'Dialog0', 'Dialog1']
child_window(title="City of Conway LIVE Springbrook V7", auto_id="MainMenu", control_type="Window")
|
| Pane - '' (L231, T87, R1024, B118)
| ['Pane', 'Pane0', 'Pane1']
| child_window(auto_id="_panelExWorkArea", control_type="Pane")
| |
| | Pane - 'Desktop' (L231, T90, R1021, B115)
| | ['DesktopPane', 'Desktop', 'Pane2']
| | child_window(title="Desktop", auto_id="_ssiGroupHeaderWorkArea", control_type="Pane")
|
| Pane - '' (L228, T87, R231, B1005)
| ['Pane3']
| child_window(auto_id="_ssiExpandableSplitter1", control_type="Pane")
|
| Pane - '' (L8, T87, R228, B1005)
| ['Pane4']
| child_window(auto_id="_panelTaskArea", control_type="Pane")
| |
| | Pane - '' (L11, T90, R228, B1002)
| | ['Pane5']
| | child_window(auto_id="328582", control_type="Pane")
| | |
| | | TreeView - '' (L11, T115, R228, B1002)
| | | ['TreeView', 'TreeView0', 'TreeView1']
| | | child_window(auto_id="1775914", control_type="Tree")
| | | |
| | | | Pane - '' (L28, T269, R194, B609)
| | | | ['Pane6']
| | | | child_window(auto_id="726924", control_type="Pane")
| | | | |
| | | | | Pane - '' (L28, T269, R194, B609)
| | | | | ['Pane7']
| | | | | child_window(auto_id="1317216", control_type="Pane")
| | | | | |
| | | | | | TreeView - '' (L28, T269, R194, B609)
| | | | | | ['TreeView2']
| | | | | | child_window(auto_id="2101028", control_type="Tree")
| | | | | | |
| | | | | | | Custom - 'Maintenance' (L0, T0, R0, B0)
| | | | | | | ['Custom', 'Maintenance', 'MaintenanceCustom', 'Custom0', 'Custom1', 'Maintenance0', 'Maintenance1', 'MaintenanceCustom0', 'MaintenanceCustom1']
| | | | | | | child_window(title="Maintenance", control_type="Custom")
I'm using a few tools to inspect the GUI, and this one specifically allows me to do the desired task by selecting "do it". It allows me to expand and collapse the section, so surely I've got to be missing something somewhere?
enter image description here
enter image description here
Here is my code:
from pywinauto import Application
app=Application(backend="uia").connect(path=r"C:\Users\skywalker\AppData\Local\Apps\2.0\C38DNYDP.PZ6\07BV1NGN.8G6\spri..ons1_b443b3e57637483a_0007.000f_52ec298e739bfebb", timeout = 30)
maintenance = app.CityofConwayLIVESpringbrookV7.WindowsForms10.Window.8.app.0.a0f91b_r8_ad1, 263022
maintenance.click()
I would also like to mention that I CAN get it to work with Click_Input, but I would like to avoid that if at all possible.

Use a plotly dropdown to select 'Time Column' and plot 'Column 2' vs 'Column 1' respecting the selected Time

Good evening,
I need to know if it is possible to use a dropdown button with plotly library and do what i mentioned in the title.
I generated these data in order to show you how my dataframe is :
+----------+----------+-----------+
| Time | Column1 | Column2 |
+----------+----------+-----------+
| 06:48:37 | -0,61447 | 0,0050662 |
| 06:48:37 | -0,30723 | 0,0045917 |
| 06:48:37 | 0 | 0,0043276 |
| 06:48:37 | 0,30723 | 0,0041332 |
| 06:48:37 | 0,61447 | 0,003965 |
| 06:48:37 | 0,9217 | 0,0038135 |
| 06:48:37 | 1,2289 | 0,0036676 |
| 06:48:37 | 1,5362 | 0,0035346 |
| 06:48:37 | 1,8434 | 0,0034031 |
| 06:48:37 | 2,1506 | 0,0032813 |
| 06:59:37 | -0,61739 | 0,0058201 |
| 06:59:37 | -0,3087 | 0,0053155 |
| 06:59:37 | 0 | 0,0050203 |
| 06:59:37 | 0,3087 | 0,0047872 |
| 06:59:37 | 0,61739 | 0,0045921 |
| 06:59:37 | 0,92609 | 0,0044152 |
| 06:59:37 | 1,2348 | 0,0042553 |
| 06:59:37 | 1,5435 | 0,004102 |
| 06:59:37 | 1,8522 | 0,0039532 |
| 06:59:37 | 2,1609 | 0,0038114 |
| 07:14:37 | -0,61799 | 0,0069837 |
| 07:14:37 | -0,309 | 0,0064459 |
| 07:14:37 | 0 | 0,0061361 |
| 07:14:37 | 0,309 | 0,0058983 |
| 07:14:37 | 0,61799 | 0,0056855 |
| 07:14:37 | 0,92699 | 0,0054978 |
| 07:14:37 | 1,236 | 0,0053299 |
| 07:14:37 | 1,545 | 0,0051582 |
| 07:14:37 | 1,854 | 0,0049979 |
| 07:14:37 | 2,163 | 0,0048426 |
+----------+----------+-----------+
Here is the link for the subject : https://plotly.com/python/dropdowns/
PS1 : I know how to create a dataframe that stock one 'Time' (for this exemple it would be a dataframe containing 06:49:37 and 06:59:37 and 07:14:37 so that i don't get many times in the dropdown list when i click on it. I don't know if it's an idea that you can use but i have that as a starting point.
PS2 : I already used plotly library but i really don't know how to start coding this problem

You can try this:
from plotly import graph_objs as go
import pandas as pd
data = {'Time':['06:48:37', '06:48:37', '06:48:37',
'06:59:37', '06:59:37', '06:59:37',
'07:14:37', '07:14:37', '07:14:37'],
'Column1':[1, 2, 3, 4, 5, 6,7,8, 9],
'Column2':[1, 2, 1, 2, 1, 2,1, 2, 1],
'Column3':[3, 4, 5, 6, 7, 8,9,10, 11],
'Column4':[1, 2, 1, 2, 1, 2, 1, 2,1]}
df = pd.DataFrame(data)
lst=[]
lst2 = []
for i,time in enumerate(df['Time'].unique()):
lst.append(
go.Scatter(name=time, # convert to str if Time column is not str
x=df.loc[df['Time']==time,'Column1'],
y=df.loc[df['Time']==time,'Column2'],
mode='lines'
)
)
lst.append(
go.Scatter(name=time, # convert to str if Time column is not str
x=df.loc[df['Time']==time,'Column3'],
y=df.loc[df['Time']==time,'Column4'],
mode='lines'
)
)
lst3 = [False]*len(df['Time'].unique()) * 2 # 2 is number of plots per timestamp
lst3[i*2:(i+1)*2]=[True,True]
lst2.append(dict(label=time,
method="update",
args=[{"visible": lst3}]))
plot = go.Figure(data=lst,
layout=dict(updatemenus=[dict(active=0, buttons=lst2)],
showlegend=False)
)
plot.show()

CoGroupByKey, emit after n records have been grouped

I'm trying to parallelize some heavier computations like so:
inputs = (p | "Read" >> beam.io.ReadFromAvro('/mypath/myavrofiles*')
| "Generate Key" >> beam.Map(lambda row: (gen_key(row), row)))
calc1_results = inputs | "perform calc1" >> beam.Pardo(Calc1())
calc2_results = inputs | "perform calc2" >> beam.Pardo(Calc2())
combined = (({"calc1": calc1_results, "calc2": calc2_results})
| beam.CoGroupByKey()
| beam.Values())
final = combined | "Use Grouped results" >> beam.ParDo(PerformFinalCalculation())
Each heavy calc emits (key, result)
Each key is unique for each input. One input, One result, one Key
Is there some way to emit from the CoGroupByKey after a single result1/result2 has been collected for each key?
Ultimately I'd like to achieve something along the lines of:
+------------+
| |
| Input |
| +-----------------+
+------------+ |
| |
v-------------------v |
+------------+ +------------+ |
| | | | |
| Heavy | | Heavy | |
| Calc 1 | | Calc 2 | |
| | | | |
+------------+ +------------+ |
| | |
| | |
| | |
+--v------------v--+ |
| Merged | |
| original dict, +<---------------+
|result 1, result2 |
| |
+------------------+

Creating a chess board for the 8 Queens puzzle

s = [0,2,6,4,7,1,5,3]
def row_top():
print("|--|--|--|--|--|--|--|--|")
def cell_left():
print("| ", end = "")
def solution(s):
for i in range(8):
row(s[i])
def cell_data(isQ):
if isQ:
print("X", end = "")
return ()
else:
print(" ", end = "")
def row_data(c):
for i in range(9):
cell_left()
cell_data(i == c)
def row(c):
row_top()
row_data(c)
print("\n")
solution(s)
My output has a space every two lines, when there shouldn't be, I'm not sure where it's creating that extra line.
The output is suppose to look like this:
|--|--|--|--|--|--|--|--|
| | | | | | X| | |
|--|--|--|--|--|--|--|--|
| | | X| | | | | |
|--|--|--|--|--|--|--|--|
| | | | | X| | | |
|--|--|--|--|--|--|--|--|
| | | | | | | | X|
|--|--|--|--|--|--|--|--|
| X| | | | | | | |
|--|--|--|--|--|--|--|--|
| | | | X| | | | |
|--|--|--|--|--|--|--|--|
| | X| | | | | | |
|--|--|--|--|--|--|--|--|
| | | | | | | X| |
|--|--|--|--|--|--|--|--|
I know this chess board isn't very square but this is only a rough draft at the moment.

Here is an alternative implementation:
def make_row(rowdata, col, empty, full):
items = [col] * (2*len(rowdata) + 1)
items[1::2] = (full if d else empty for d in rowdata)
return ''.join(items)
def make_board(queens, col="|", row="---", empty=" ", full=" X "):
size = len(queens)
bar = make_row(queens, col, row, row)
board = [bar] * (2*size + 1)
board[1::2] = (make_row([i==q for i in range(size)], col, empty, full) for q in queens)
return '\n'.join(board)
queens = [0,2,6,4,7,1,5,3]
print(make_board(queens))
which results in
|---|---|---|---|---|---|---|---|
| X | | | | | | | |
|---|---|---|---|---|---|---|---|
| | | X | | | | | |
|---|---|---|---|---|---|---|---|
| | | | | | | X | |
|---|---|---|---|---|---|---|---|
| | | | | X | | | |
|---|---|---|---|---|---|---|---|
| | | | | | | | X |
|---|---|---|---|---|---|---|---|
| | X | | | | | | |
|---|---|---|---|---|---|---|---|
| | | | | | X | | |
|---|---|---|---|---|---|---|---|
| | | | X | | | | |
|---|---|---|---|---|---|---|---|
It is now very easy to change the width of the board by changing the strings passed to row, empty, full; I added an extra char to each, resulting in a (somewhat) squarer board.

You are still printing an extra newline:
def row(c):
row_top()
row_data(c)
print("\n")
Remove the explicit ''\n'` character:
def row(c):
row_top()
row_data(c)
print()
or better still, follow my previous answer more closely and print a closing | bar:
def row(c):
row_top()
row_data(c)
print('|')

RDF/SKOS concept hierarchy as Python dictionary

In Python, how do I turn RDF/SKOS taxonomy data into a dictionary that represents the concept hierarchy only?
The dictionary must have this format:
{ 'term1': [ 'term2', 'term3'], 'term3': [{'term4' : ['term5', 'term6']}, 'term6']}
I tried using RDFLib with JSON plugins, but did not get the result I want.

I'm not much of a Python user, and I haven't worked with RDFLib, but I just pulled the SKOS and vocabulary from the SKOS vocabularies page. I wasn't sure what concepts (RDFS or OWL classes) were in the vocabulary, nor what their hierarchy was, so I ran this a SPARQL query using Jena's ARQ to select classes and their subclasses. I didn't get any results. (There were classes defined of course, but none had subclasses.) Then I decided to use both the SKOS and SKOS-XL vocabularies, and to ask for properties and subproperties as well as classes and subclasses. This is the SPARQL query I used:
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
SELECT ?property ?subproperty ?class ?subclass WHERE {
{ ?subclass rdfs:subClassOf ?class }
UNION
{ ?subproperty rdfs:subPropertyOf ?property }
}
ORDER BY ?class ?property
The results I got were
-------------------------------------------------------------------------------------------------------------------
| property | subproperty | class | subclass |
===================================================================================================================
| rdfs:label | skos:altLabel | | |
| rdfs:label | skos:hiddenLabel | | |
| rdfs:label | skos:prefLabel | | |
| skos:broader | skos:broadMatch | | |
| skos:broaderTransitive | skos:broader | | |
| skos:closeMatch | skos:exactMatch | | |
| skos:inScheme | skos:topConceptOf | | |
| skos:mappingRelation | skos:broadMatch | | |
| skos:mappingRelation | skos:closeMatch | | |
| skos:mappingRelation | skos:narrowMatch | | |
| skos:mappingRelation | skos:relatedMatch | | |
| skos:narrower | skos:narrowMatch | | |
| skos:narrowerTransitive | skos:narrower | | |
| skos:note | skos:changeNote | | |
| skos:note | skos:definition | | |
| skos:note | skos:editorialNote | | |
| skos:note | skos:example | | |
| skos:note | skos:historyNote | | |
| skos:note | skos:scopeNote | | |
| skos:related | skos:relatedMatch | | |
| skos:semanticRelation | skos:broaderTransitive | | |
| skos:semanticRelation | skos:mappingRelation | | |
| skos:semanticRelation | skos:narrowerTransitive | | |
| skos:semanticRelation | skos:related | | |
| | | _:b0 | <http://www.w3.org/2008/05/skos-xl#Label> |
| | | skos:Collection | skos:OrderedCollection |
-------------------------------------------------------------------------------------------------------------------
It looks like there's not much concept hierarchy in SKOS at all. Could that explain why you didn't get the results you wanted before?