serhii.net

Toggle touchpad (enable/disable) in Linux with xinput.:

if xinput list-props 13 | grep "Device Enabled (:digit:\+):\s*1" >/dev/null; then xinput disable 13 && notify-send -u low -i mouse "Trackpad disabled"; else xinput enable 13 && notify-send -u low -i mouse "Trackpad enabled"; fi

With 13 being the xinput id of the touchpad.

My old enable/disable oneliners have bits on how to find the ID:

'bash -c "xinput | grep TouchPad | ag -o "[0-9][0-9]"  | xargs xinput disable"'

That said, I don’t remember the ID ever being anything else than 13.

WT told me about these:

• marcotcr/lime: Lime: Explaining the predictions of any machine learning classifier
• TeamHG-Memex/eli5: A library for debugging/inspecting machine learning classifiers and explaining their predictions
• DistrictDataLabs/yellowbrick: Visual analysis and diagnostic tools to facilitate machine learning model selection.

I had this:

tm_old() {
    local DATE=$(date +'%H:%M:%S %d/%m')
    local N="$1"; shift
	  (utimer -c $N && zenity --info --title="Time's Up" --text="${*:-BING} \n\n $DATE")
}

I used it as tm 3m message and get a popup in three minutes with “message”. Used it for reminders of random stuff like “turn off the stove” or “stop doing X”.

Now utimer seems to be dead, and qtile makes the alert popup messages pop up in the wrong workspace group, usually the one wrote the command in instead of the currently active one.

Today I solved the last part by switching to notify-send. Found dunst, added to startup, now notify-send creates nice visible alerts:

It seems to support a lot of cool stuff like progress bars and images: dunst-project/dunst: Lightweight and customizable notification daemon

Dunst - The Blue Book - nice post, and woohooo a digital garden!

Useful commands:

• dunstctl close-all
• dunstctl history-pop

Added the first one as qtile shortcut:

    Key(
        [mod, ctrl],
        "h",
        lazy.spawn(cmd.dunst_clearall),
        desc="Clear notifications",
    ),

There’s also dunstify which is a notify-send with more options.

Changed the zsh command to use notify-send. Everything works nicely now.

If utimer stops working I’ll prolly write a python script that does a countdown¹ and then a configured notification/action/.., without relying on .zshrc aliases and bash functions. We’ll see.

Reading Creating and updating figures in Python.

1. All of these are equivalent (code from link):

fig.update_layout(title_text="update_layout() Syntax Example",
                  title_font_size=30)

fig.update_layout(title_text="update_layout() Syntax Example",
                  title_font=dict(size=30))


fig.update_layout(title=dict(text="update_layout() Syntax Example"),
                             font=dict(size=30))

fig.update_layout({"title": {"text": "update_layout() Syntax Example",
                             "font": {"size": 30}}})

fig.update_layout(title=go.layout.Title(text="update_layout() Syntax Example",
                                        font=go.layout.title.Font(size=30)))

2. Introducing linebreaks: <br> and <br /> work, <br/> doesn’t. ¹

3. Margins in graph: Setting graph size in Python

fig.update_layout(margin=dict(l=20, r=20, t=20, b=20))

And I just want to mention the very special design decision to have arguments named tickfont and title_font (with underscore), in the same function, getting identical arguments.

Really nice google colab showing more advanced datasets bits in addition to what’s on the label: Custom Named Entity Recognition with BERT.ipynb - Colaboratory

Pasting this example from there:

class dataset(Dataset):
	def __init__(self, dataframe, tokenizer, max_len):
		self.len = len(dataframe)
		self.data = dataframe
		self.tokenizer = tokenizer
		self.max_len = max_len
	  
	def __getitem__(self, index):
		# step 1: get the sentence and word labels
		sentence = self.data.sentence[index].strip().split()
		word_labels = self.data.word_labels[index].split(",")
		  
		# step 2: use tokenizer to encode sentence (includes padding/truncation up to max length)
		# BertTokenizerFast provides a handy "return_offsets_mapping" functionality for individual tokens
		encoding = self.tokenizer(sentence,
		is_pretokenized=True,
		return_offsets_mapping=True,
		padding='max_length',
		truncation=True,
		max_length=self.max_len)
		# step 3: create token labels only for first word pieces of each tokenized word
		labels = [labels_to_ids[label] for label in word_labels]
		# code based on https://huggingface.co/transformers/custom_datasets.html#tok-ner
		# create an empty array of -100 of length max_length
		encoded_labels = np.ones(len(encoding["offset_mapping"]), dtype=int) * -100
		# set only labels whose first offset position is 0 and the second is not 0
		i = 0
		for idx, mapping in enumerate(encoding["offset_mapping"]):
		if mapping[0] == 0 and mapping[1] != 0:
		# overwrite label
		encoded_labels[idx] = labels[i]
		i += 1
		
		# step 4: turn everything into PyTorch tensors
		item = {key: torch.as_tensor(val) for key, val in encoding.items()}
		item['labels'] = torch.as_tensor(encoded_labels)
		return item
	  
	def __len__(self):
		return self.len

For aligning tokens, there’s Code To Align Annotations With Huggingface Tokenizers. It has a repo: LightTag/sequence-labeling-with-transformers: Examples for aligning, padding and batching sequence labeling data (NER) for use with pre-trained transformer models

Also the official tutorial (Token classification) has a function to do something similar:

def tokenize_and_align_labels(examples):
    tokenized_inputs = tokenizer(examples["tokens"], truncation=True, is_split_into_words=True)

    labels = []
    for i, label in enumerate(examples[f"ner_tags"]):
        word_ids = tokenized_inputs.word_ids(batch_index=i)  # Map tokens to their respective word.
        previous_word_idx = None
        label_ids = []
        for word_idx in word_ids:  # Set the special tokens to -100.
            if word_idx is None:
                label_ids.append(-100)
            elif word_idx != previous_word_idx:  # Only label the first token of a given word.
                label_ids.append(label[word_idx])
            else:
                label_ids.append(-100)
            previous_word_idx = word_idx
        labels.append(label_ids)

    tokenized_inputs["labels"] = labels
    return tokenized_inputs

git delete commit

git rebase -i SHA_of_commit_to_delete^ drops you into the usual screen, three you can change pick to drop in the first line (or any others) to just delete that commit.

Generally, On undoing, fixing, or removing commits in git seems like The README for that.

git delete branch

• git branch -d some-branch deletes a local branch
• git push origin --delete some-branch deletes a remote branch

(as usual, remembering that branches are pointers to commits)

Changing the timeout delay for wrong logins on linux has a lot of details, in my case the TL;DR was:

1. /etc/pam.d/login change the number, in microseconds;
2. disable delays completely in /etc/pam.d/common-auth by adding nodelay to: auth [success=1 default=ignore] pam_unix.so nullok_secure nodelay

The second one works also for everything inheriting that, which is a lot.

debugging - I have a hardware detection problem, what logs do I need to look into? - Ask Ubuntu:

Then, causing the problem to happen, and listing the system’s logs in reverse order of modification time:

ls -lrt /var/log, tail -n 25 on recently modified log files (for reasonable values of 25), and dmesg.

Read, wonder, think, guess, test, repeat as needed

Causing the problem and then looking at the recently modified logs is common sense but brilliant.

And saving ls -lrt as “list by modification time”.

-t is “sort by modification time” and is easy to remember.

When debugging an issue I had with my monitor, found a mention of inxi¹, which seems to colorfully output basic system (incl. hardware) info.

The post asked for inxi -SMCGx, inxi help told me inxi -F is the fullest possible output.

Neat!

So, noisetorch says it’s potentially compromised: Release POTENTIAL COMPROMISE · noisetorch/NoiseTorch.

An improvement for the previous more dramatic formulation: Community code review? · noisetorch/NoiseTorch@b4bb8e6

This project is dead, i’ve failed you.

Thoughts and prayers (honestly! I loved it), with a heavy heart I keep looking.

Option1: werman/noise-suppression-for-voice: Noise suppression plugin based on Xiph’s RNNoise

Reading how to install it made me very sad, kept looking.

Saw EasyEffects mentioned, but it runs on Pipewire.

TIL Pipewire is a Pulseaudio replacement.

Installed via this guide: How to install PipeWire on Ubuntu Linux - Linux Tutorials - Learn Linux Configuration

Installed and ran EasyEffects using flatpak:

flatpak install easyeffects
flatpak run com.github.wwmm.easyeffects

EasyEffects’ GUI looks awesome!

Had to choose another input source in pavucontrol, then once the input is piped thorugh it - the effect “Noise Reduction” works! Removes both keyboard and random background white noise.

You can even save the config as preset and make it run automagically on startup!