Repetative routines for python

Summary of some repetative action one does in python

Summary of some repetative action one does in python. Updated ocasionally.

1) Take a snapshot of current enviroment from script

import subprocess

with open("requirements.txt", "w") as f:

subprocess.run(["pip", "freeze"], stdout=f)

2) Framework to load pkl files from folder.

import os

import pickle

folder_path = "path/to/your/folder"

# Version 1: List comprehension (compact)

# pkl_files = [f for f in os.listdir(folder_path) if f.endswith('.pkl')]

# Version 2: Traditional loop (more readable)

pkl_files = []

for f in os.listdir(folder_path):

if f.endswith('.pkl'):

pkl_files.append(f)

# Sort the file list for consistent indexing (optional)

pkl_files.sort()

# Full paths to the .pkl files

pkl_paths = [os.path.join(folder_path, f) for f in pkl_files]

def load_pickle_by_index(index):

if 0 <= index < len(pkl_paths):

with open(pkl_paths[index], 'rb') as f:

return pickle.load(f)

else:

raise IndexError("Index out of range.")

# Example usage

print(f"Found {len(pkl_paths)} .pkl files.")

# obj = load_pickle_by_index(0)

print(type(obj)) # Uncomment to test loading

% run_make_test_pptx_demo.m

% Creates synthetic test results, then calls make_test_pptx

% --- synthetic results you’d normally compute in your pipeline

results = struct();

results.timestamp = datestr(now, 'yyyy-mm-dd HH:MM:ss');

results.tester = 'Automated Rig 3';

N = 10000; % or N = 1e4;

results.timeSec = linspace(0, 20, N);

results.signal = 0.7*sin(2*pi*0.6*results.timeSec) + 0.05*randn(1,N);

results.latencyMs = 12 + 3*randn(1, 2000); % pretend 2k transactions

% KPIs

results.nRuns = 25;

results.metrics.meanLatency = mean(results.latencyMs);

results.metrics.stdLatency = std(results.latencyMs);

results.metrics.p95Latency = prctile(results.latencyMs,95);

results.metrics.maxOvershoot = max(results.signal) - 0.7;

results.passRate = 0.96;

results.pass = results.metrics.p95Latency < 18; % example rule

% Short log

results.log = [

"Init hardware: OK"

"Loaded config profile: /configs/prod-A.json"

"Self-test: PASS"

"Run sweep: 25 iterations"

sprintf("Anomalies detected: %d", sum(results.latencyMs>20))

];

% --- build the deck (creates a NEW .pptx; no template file required)

outfile = make_test_pptx(results, "reports"); % returns full path

fprintf("Wrote: %s\n", outfile);

function pptxPath = make_test_pptx(results, outdir)

% make_test_pptx Build a brand-new PowerPoint summary deck for a test run.

% Creates a fresh .pptx using MATLAB's built-in default presentation.

% Does NOT rely on any existing .pptx or .potx on disk.

% ---- dependency check

if ~exist('mlreportgen.ppt.Presentation','class')

error(['mlreportgen.ppt (MATLAB Report Generator) not found.\n' ...

'Install MATLAB Report Generator or use a File Exchange alternative (e.g., exportToPPTX).']);

end

import mlreportgen.ppt.*

if nargin < 2 || strlength(outdir)==0

outdir = "reports";

end

if ~exist(outdir, 'dir'), mkdir(outdir); end

pptxPath = fullfile(outdir, "test_summary.pptx");

% Create a NEW presentation using MATLAB's built-in default template in memory.

% This does NOT require any existing file on disk.

pres = Presentation(pptxPath); % no template path -> built-in default

open(pres);

% ---------- Slide 1: Title ----------

s1 = add(pres, "Title Slide");

replace(s1, "Title", "Test Summary");

replace(s1, "Subtitle", sprintf("%s • %s", results.tester, results.timestamp));

% ---------- Slide 2: KPIs ----------

s2 = add(pres, "Title and Content");

replace(s2, "Title", "Key Performance Indicators");

kpi = {

"Metric", "Value";

"Runs", num2str(results.nRuns);

"Pass rate (%)", sprintf("%.1f", 100*results.passRate);

"Mean latency (ms)", sprintf("%.2f", results.metrics.meanLatency);

"Std latency (ms)", sprintf("%.2f", results.metrics.stdLatency);

"p95 latency (ms)", sprintf("%.2f", results.metrics.p95Latency);

"Max overshoot", sprintf("%.3f", results.metrics.maxOvershoot);

"Overall status", string(results.pass * "PASS" + ~results.pass * "FAIL")

};

tbl = Table(kpi);

replace(s2, "Content", tbl);

% ---------- Prep images (export figures) ----------

latFig = figure('Visible','off');

histogram(results.latencyMs);

xlabel('Latency (ms)'); ylabel('Count'); title('Latency Distribution');

latPng = fullfile(outdir,'latency_hist.png');

exportgraphics(latFig, latPng, 'Resolution', 150); close(latFig);

sigFig = figure('Visible','off');

plot(results.timeSec, results.signal); grid on

xlabel('Time (s)'); ylabel('Signal'); title('Signal vs Time');

sigPng = fullfile(outdir,'signal_timeseries.png');

exportgraphics(sigFig, sigPng, 'Resolution', 150); close(sigFig);

% ---------- Slide 3: Latency histogram ----------

s3 = add(pres, "Title and Content");

replace(s3, "Title", "Latency Distribution");

replace(s3, "Content", Picture(latPng));

% ---------- Slide 4: Signal snapshot ----------

s4 = add(pres, "Title and Content");

replace(s4, "Title", "Signal Snapshot");

replace(s4, "Content", Picture(sigPng));

% ---------- Slide 5: Short log (bullets) ----------

s5 = add(pres, "Title and Content");

replace(s5, "Title", "Run Notes / Log (truncated)");

paras = arrayfun(@(i) Paragraph(results.log(i)), 1:numel(results.log), 'uni', false);

tbx = TextBox();

for i = 1:numel(paras), append(tbx, paras{i}); end

replace(s5, "Content", tbx);

% Finish (creates/overwrites the pptx at pptxPath)

close(pres);

end