alphatims

Install alphatims

!pip install alphatims --quiet

Download test data

import requests
import zipfile
import os

# # Define the URL and file name
url = 'https://github.com/MannLabs/alphatims/releases/download/0.1.210317/20201207_tims03_Evo03_PS_SA_HeLa_200ng_EvoSep_prot_DDA_21min_8cm_S1-C10_1_22476.d.zip'
file_name = '20201207_tims03_Evo03_PS_SA_HeLa_200ng_EvoSep_prot_DDA_21min_8cm_S1-C10_1_22476.d.zip'
extract_dir = './'  # Directory to extract the contents

# # Download the file
response = requests.get(url)
with open(file_name, 'wb') as file:
    file.write(response.content)

print(f'File {file_name} downloaded successfully!')

# Unzip the file
with zipfile.ZipFile(file_name, 'r') as zip_ref:
    zip_ref.extractall(extract_dir)

print(f'File extracted to {extract_dir}!')

# Optionally, remove the zip file after extraction
os.remove(file_name)
print(f'Zip file {file_name} removed.')

File 20201207_tims03_Evo03_PS_SA_HeLa_200ng_EvoSep_prot_DDA_21min_8cm_S1-C10_1_22476.d.zip downloaded successfully!
File extracted to ./!
Zip file 20201207_tims03_Evo03_PS_SA_HeLa_200ng_EvoSep_prot_DDA_21min_8cm_S1-C10_1_22476.d.zip removed.

Load .d File and Extra a section of it as a pd.DataFrame

import alphatims.bruker

bruker_dia_d_folder_name = "./20201207_tims03_Evo03_PS_SA_HeLa_200ng_EvoSep_prot_DDA_21min_8cm_S1-C10_1_22476.d"
dda_data = alphatims.bruker.TimsTOF(bruker_dia_d_folder_name)

# Slice the resulting object to return a pandas dataframe
# for full details see https://github.com/MannLabs/alphatims/blob/master/nbs/tutorial.ipynb
dda_data[1] # returns the first spectrum

/opt/hostedtoolcache/Python/3.12.8/x64/lib/python3.12/site-packages/alphatims/bruker.py:237: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  frames.Id[0] = 0
/opt/hostedtoolcache/Python/3.12.8/x64/lib/python3.12/site-packages/alphatims/bruker.py:238: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  frames.Time[0] = 0
/opt/hostedtoolcache/Python/3.12.8/x64/lib/python3.12/site-packages/alphatims/bruker.py:239: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  frames.MaxIntensity[0] = 0
/opt/hostedtoolcache/Python/3.12.8/x64/lib/python3.12/site-packages/alphatims/bruker.py:240: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  frames.SummedIntensities[0] = 0
/opt/hostedtoolcache/Python/3.12.8/x64/lib/python3.12/site-packages/alphatims/bruker.py:241: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  frames.NumPeaks[0] = 0
/opt/hostedtoolcache/Python/3.12.8/x64/lib/python3.12/site-packages/alphatims/bruker.py:242: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  frames.MsMsType[0] = 0
100%|██████████| 11886/11886 [00:03<00:00, 3014.40it/s]

	raw_indices	frame_indices	scan_indices	precursor_indices	push_indices	tof_indices	rt_values	rt_values_min	mobility_values	quad_low_mz_values	quad_high_mz_values	mz_values	intensity_values	corrected_intensity_values
0	0	1	52	0	980	255431	0.564402	0.009407	1.580692	-1.0	-1.0	913.250345	10	10
1	1	1	63	0	991	308595	0.564402	0.009407	1.568660	-1.0	-1.0	1185.318098	86	86
2	2	1	69	0	997	160184	0.564402	0.009407	1.562095	-1.0	-1.0	514.400704	159	159
3	3	1	77	0	1005	375561	0.564402	0.009407	1.553337	-1.0	-1.0	1578.430403	53	53
4	4	1	78	0	1006	248580	0.564402	0.009407	1.552242	-1.0	-1.0	880.766749	47	47
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
17036	17036	1	919	0	1847	74875	0.564402	0.009407	0.609185	-1.0	-1.0	253.681834	81	81
17037	17037	1	919	0	1847	112482	0.564402	0.009407	0.609185	-1.0	-1.0	357.374903	253	253
17038	17038	1	919	0	1847	143231	0.564402	0.009407	0.609185	-1.0	-1.0	455.328372	130	130
17039	17039	1	923	0	1851	160022	0.564402	0.009407	0.604592	-1.0	-1.0	513.819175	154	154
17040	17040	1	923	0	1851	167506	0.564402	0.009407	0.604592	-1.0	-1.0	541.027761	174	174

17041 rows × 14 columns