diff --git a/.bumpversion.cfg b/.bumpversion.cfg index f7292379..d1cf7ce1 100644 --- a/.bumpversion.cfg +++ b/.bumpversion.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 1.2.2 +current_version = 1.2.3 commit = True tag = False parse = (?P\d+)\.(?P\d+)\.(?P\d+)(\-(?P[a-z]+)(?P\d+))? diff --git a/alphabase/__init__.py b/alphabase/__init__.py index 88bddd05..72b97e27 100644 --- a/alphabase/__init__.py +++ b/alphabase/__init__.py @@ -2,7 +2,7 @@ __project__ = "alphabase" -__version__ = "1.2.2" +__version__ = "1.2.3" __license__ = "Apache" __description__ = "An infrastructure Python package of the AlphaX ecosystem" __author__ = "Mann Labs" diff --git a/alphabase/peptide/fragment.py b/alphabase/peptide/fragment.py index 0750f173..e1a0c8a6 100644 --- a/alphabase/peptide/fragment.py +++ b/alphabase/peptide/fragment.py @@ -261,7 +261,7 @@ def init_fragment_by_precursor_dataframe( def update_sliced_fragment_dataframe( fragment_df: pd.DataFrame, - fragment_mzs: np.ndarray, + fragment_df_vals: np.ndarray, values: np.ndarray, frag_start_end_list: List[Tuple[int,int]], charged_frag_types: List[str]=None, @@ -275,8 +275,8 @@ def update_sliced_fragment_dataframe( fragment_df : pd.DataFrame fragment dataframe to set the values - fragment_mzs : np.ndarray - The copy np.ndarry of fragment_df + fragment_df_vals : np.ndarray + The `fragment_df.to_numpy(copy=True)`, to prevent readonly assignment. values : np.ndarray values to set @@ -294,13 +294,13 @@ def update_sliced_fragment_dataframe( frag_slice_list = [slice(start,end) for start,end in frag_start_end_list] frag_slices = np.r_[tuple(frag_slice_list)] if charged_frag_types is None or len(charged_frag_types)==0: - fragment_mzs[frag_slices, :] = values.astype(fragment_mzs.dtype) + fragment_df_vals[frag_slices, :] = values.astype(fragment_df_vals.dtype) else: charged_frag_idxes = [fragment_df.columns.get_loc(c) for c in charged_frag_types] fragment_df.iloc[ frag_slices, charged_frag_idxes - ] = values.astype(fragment_mzs.dtype) - fragment_mzs[frag_slices] = fragment_df.values[frag_slices] + ] = values.astype(fragment_df_vals.dtype) + fragment_df_vals[frag_slices] = fragment_df.values[frag_slices] def get_sliced_fragment_dataframe( fragment_df: pd.DataFrame, diff --git a/alphabase/peptide/precursor.py b/alphabase/peptide/precursor.py index f3dcd688..f2945539 100644 --- a/alphabase/peptide/precursor.py +++ b/alphabase/peptide/precursor.py @@ -485,13 +485,13 @@ def _count_batchify_df(df_group, mp_batch_size): count += 1 return count -# `process_bar` should be replaced by more advanced tqdm wrappers created by Sander +# `progress_bar` should be replaced by more advanced tqdm wrappers created by Sander # I will leave it to alphabase.utils def calc_precursor_isotope_info_mp( precursor_df:pd.DataFrame, processes:int=8, mp_batch_size:int=10000, - process_bar=None, + progress_bar=None, min_right_most_intensity:float=0.2, min_precursor_num_to_run_mp:int=10000, )->pd.DataFrame: @@ -510,7 +510,7 @@ def calc_precursor_isotope_info_mp( mp_batch_size : int Multiprocessing batch size. Optional, by default 100000. - process_bar : Callable + progress_bar : Callable The tqdm-based callback function to check multiprocessing. Defaults to None. @@ -538,8 +538,8 @@ def calc_precursor_isotope_info_mp( min_right_most_intensity=min_right_most_intensity ), _batchify_df(df_group, mp_batch_size) ) - if process_bar: - processing = process_bar( + if progress_bar: + processing = progress_bar( processing, _count_batchify_df( df_group, mp_batch_size ) diff --git a/alphabase/quantification/quant_reader/config_dict_loader.py b/alphabase/quantification/quant_reader/config_dict_loader.py index af26fc27..d9528963 100644 --- a/alphabase/quantification/quant_reader/config_dict_loader.py +++ b/alphabase/quantification/quant_reader/config_dict_loader.py @@ -9,24 +9,15 @@ INTABLE_CONFIG = os.path.join(pathlib.Path(__file__).parent.absolute(), "../../../alphabase/constants/const_files/quant_reader_config.yaml") #the yaml config is located one directory below the python library files def get_input_type_and_config_dict(input_file, input_type_to_use = None): - config_dict = _load_config(INTABLE_CONFIG) - type2relevant_columns = _get_type2relevant_cols(config_dict) + all_config_dicts = _load_config(INTABLE_CONFIG) + type2relevant_columns = _get_type2relevant_cols(all_config_dicts) if "aq_reformat.tsv" in input_file: input_file = _get_original_file_from_aq_reformat(input_file) - filename = str(input_file) - if '.csv' in filename: - sep=',' - if '.tsv' in filename: - sep='\t' - if '.txt' in filename: - sep='\t' - - if 'sep' not in locals(): - raise TypeError(f"neither of the file extensions (.tsv, .csv, .txt) detected for file {input_file}! Your filename has to contain one of these extensions. Please modify your file name accordingly.") + sep = _get_seperator(input_file) - uploaded_data_columns = set(pd.read_csv(input_file, sep=sep, nrows=1, encoding ='latin1').columns) + uploaded_data_columns = set(pd.read_csv(input_file, sep=sep, nrows=1).columns) for input_type in type2relevant_columns.keys(): if (input_type_to_use is not None) and (input_type!=input_type_to_use): @@ -34,14 +25,29 @@ def get_input_type_and_config_dict(input_file, input_type_to_use = None): relevant_columns = type2relevant_columns.get(input_type) relevant_columns = [x for x in relevant_columns if x] #filter None values if set(relevant_columns).issubset(uploaded_data_columns): - config_dict_type = config_dict.get(input_type) - return input_type, config_dict_type, sep + config_dict = all_config_dicts.get(input_type) + return input_type, config_dict, sep + raise TypeError("format not specified in intable_config.yaml!") def _get_original_file_from_aq_reformat(input_file): matched = re.match("(.*)(\..*\.)(aq_reformat\.tsv)",input_file) return matched.group(1) +def _get_seperator(input_file): + filename = str(input_file) + if '.csv' in filename: + sep=',' + if '.tsv' in filename: + sep='\t' + if '.txt' in filename: + sep='\t' + + if 'sep' not in locals(): + raise TypeError(f"neither of the file extensions (.tsv, .csv, .txt) detected for file {input_file}! Your filename has to contain one of these extensions. Please modify your file name accordingly.") + return sep + + def _load_config(config_yaml): with open(config_yaml, 'r') as stream: diff --git a/alphabase/quantification/quant_reader/quant_reader_manager.py b/alphabase/quantification/quant_reader/quant_reader_manager.py index a99cc4e1..665161da 100644 --- a/alphabase/quantification/quant_reader/quant_reader_manager.py +++ b/alphabase/quantification/quant_reader/quant_reader_manager.py @@ -44,5 +44,8 @@ def reformat_and_save_input_file(input_file, input_type_to_use = None, use_alpha raise Exception('Format not recognized!') return outfile_name +def set_quanttable_config_location(quanttable_config_file): + config_dict_loader.INTABLE_CONFIG = quanttable_config_file + diff --git a/alphabase/spectral_library/base.py b/alphabase/spectral_library/base.py index 48ce8f61..7e4ba3e9 100644 --- a/alphabase/spectral_library/base.py +++ b/alphabase/spectral_library/base.py @@ -4,6 +4,7 @@ import logging import copy import warnings +import re import alphabase.peptide.fragment as fragment import alphabase.peptide.precursor as precursor @@ -135,9 +136,9 @@ def fragment_intensity_df(self)->pd.DataFrame: return self._fragment_intensity_df - def available_fragment_dfs(self)->list: + def available_dense_fragment_dfs(self)->list: """ - Return the available fragment dataframes + Return the available dense fragment dataframes By dynamically checking the attributes of the object. a fragment dataframe is matched with the pattern '_fragment_[attribute_name]_df' @@ -148,7 +149,7 @@ def available_fragment_dfs(self)->list: """ return [ attr for attr in dir(self) - if attr.startswith('_fragment') and attr.endswith('_df') + if re.match(r'_fragment_.*_df', attr) ] def copy(self): @@ -197,7 +198,7 @@ def append( """ if remove_unused_dfs: - current_frag_dfs = self.available_fragment_dfs() + current_frag_dfs = self.available_dense_fragment_dfs() for attr in current_frag_dfs: if attr not in dfs_to_append: delattr(self, attr) @@ -500,7 +501,7 @@ def remove_unused_fragments(self): Fragment dataframes are updated inplace and overwritten. """ - available_fragments_df = self.available_fragment_dfs() + available_fragments_df = self.available_dense_fragment_dfs() non_zero_dfs = [ df for df in available_fragments_df if len(getattr(self, df)) > 0 diff --git a/alphabase/spectral_library/flat.py b/alphabase/spectral_library/flat.py index 6df7b0df..e50e3723 100644 --- a/alphabase/spectral_library/flat.py +++ b/alphabase/spectral_library/flat.py @@ -1,5 +1,7 @@ import pandas as pd import numpy as np +import warnings + from alphabase.spectral_library.base import ( SpecLibBase ) @@ -77,10 +79,22 @@ def fragment_df(self)->pd.DataFrame: def protein_df(self)->pd.DataFrame: """ Protein dataframe """ return self._protein_df - + + def available_dense_fragment_dfs(self): + """Return the available dense fragment dataframes. + This method is inherited from :class:`SpecLibBase` and will return an empty list for a flat library. + """ + return [] + + def remove_unused_fragments(self): + """Remove unused fragments from fragment_df. + This method is inherited from :class:`SpecLibBase` and has not been implemented for a flat library. + """ + raise NotImplementedError("remove_unused_fragments is not implemented for a flat library") + def parse_base_library(self, library:SpecLibBase, - keep_original_frag_dfs:bool=True, + keep_original_frag_dfs:bool=False, copy_precursor_df:bool=False, **kwargs ): @@ -121,12 +135,16 @@ def parse_base_library(self, self._protein_df = pd.DataFrame() if keep_original_frag_dfs: + self.charged_frag_types = library.fragment_mz_df.columns.values - self._fragment_mz_df = library.fragment_mz_df - self._fragment_intensity_df = library.fragment_intensity_df - else: - self._fragment_mz_df = pd.DataFrame() - self._fragment_intensity_df = pd.DataFrame() + for dense_frag_df in library.available_dense_fragment_dfs(): + setattr(self, dense_frag_df, getattr(library, dense_frag_df)) + + warnings.warn( + "The SpecLibFlat object will have a strictly flat representation in the future. keep_original_frag_dfs=True will be deprecated.", + DeprecationWarning + ) + def save_hdf(self, hdf_file:str): """Save library dataframes into hdf_file. diff --git a/docs/conf.py b/docs/conf.py index 9af9fbbf..f62a1bb2 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -23,7 +23,7 @@ copyright = '2022, Mann Labs, MPIB' author = 'Mann Labs, MPIB' -release = "1.2.2" +release = "1.2.3" # -- General configuration --------------------------------------------------- diff --git a/nbdev_nbs/quantification/test_table_loading.ipynb b/nbdev_nbs/quantification/test_table_loading.ipynb index d76e3776..ce7040c7 100644 --- a/nbdev_nbs/quantification/test_table_loading.ipynb +++ b/nbdev_nbs/quantification/test_table_loading.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -18,472 +18,9 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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proteinquant_idFRGIONMODCHARGEMS1ISOTOPESSEQ20210407_tims03_Evo03_PaSk_SA_HeLa_100ng_Yeast_100ng_S2-A10_1_2455720210407_tims03_Evo03_PaSk_SA_HeLa_100ng_Yeast_100ng_S2-A11_1_2456020210407_tims03_Evo03_PaSk_SA_HeLa_100ng_Yeast_100ng_S2-A12_1_24563...20210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C12_1_2456520210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C1_1_2453220210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C2_1_2453520210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C3_1_2453820210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C4_1_2454120210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C5_1_2454420210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C6_1_2454720210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C7_1_2455020210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C8_1_2455320210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C9_1_24556
0O95551SEQ_AAAEEGHIIPR_MOD_AAAEEGHIIPR_CHARGE_2_FRGION_0FRGION_0MOD_AAAEEGHIIPRCHARGE_2NaNSEQ_AAAEEGHIIPR0.0896.367584.1060...2896.280714.8002985.0203675.0201933.86002910.933961.4401963.5602534.7101927.910
1O95551SEQ_AAAEEGHIIPR_MOD_AAAEEGHIIPR_CHARGE_2_FRGION_1FRGION_1MOD_AAAEEGHIIPRCHARGE_2NaNSEQ_AAAEEGHIIPR0.01179.1201303.0100...879.8233659.0202903.7904837.0401679.54002781.311194.8301009.480617.3382126.020
2O95551SEQ_AAAEEGHIIPR_MOD_AAAEEGHIIPR_CHARGE_2_FRGION_2FRGION_2MOD_AAAEEGHIIPRCHARGE_2NaNSEQ_AAAEEGHIIPR0.0596.609465.8550...2853.0201595.0101424.9902152.9502022.52002384.022195.0202385.0202146.0202022.020
3O95551SEQ_AAAEEGHIIPR_MOD_AAAEEGHIIPR_CHARGE_2_FRGION_3FRGION_3MOD_AAAEEGHIIPRCHARGE_2NaNSEQ_AAAEEGHIIPR0.01702.01058.7502...1056.070154.1481038.430787.1761142.14003036.32414.9582242.020414.917644.922
4O95551SEQ_AAAEEGHIIPR_MOD_AAAEEGHIIPR_CHARGE_2_FRGION_4FRGION_4MOD_AAAEEGHIIPRCHARGE_2NaNSEQ_AAAEEGHIIPR0.0722.1310.0000...582.225407.820356.076499.7962117.02001057.962270.020870.767915.039588.258
..................................................................
121Q9NXG2SEQ_AAPAQQTTQPGGGK_MOD_AAPAQQTTQPGGGK_CHARGE_2...FRGION_0MOD_AAPAQQTTQPGGGKCHARGE_2NaNSEQ_AAPAQQTTQPGGGK0.00.0000.0000...0.0000.0000.0000.00020447.10000.000.0000.0000.0000.000
122Q9NXG2SEQ_AAPAQQTTQPGGGK_MOD_AAPAQQTTQPGGGK_CHARGE_2...FRGION_3MOD_AAPAQQTTQPGGGKCHARGE_2NaNSEQ_AAPAQQTTQPGGGK0.00.0000.0000...0.0000.0000.0000.000454.13500.000.0000.0000.0000.000
123Q9NXG2SEQ_AAPAQQTTQPGGGK_MOD_AAPAQQTTQPGGGK_CHARGE_2...FRGION_4MOD_AAPAQQTTQPGGGKCHARGE_2NaNSEQ_AAPAQQTTQPGGGK0.00.0000.0000...0.0000.0000.0000.000310.93800.000.0000.0000.0000.000
124Q9NXG2SEQ_AAPAQQTTQPGGGK_MOD_AAPAQQTTQPGGGK_CHARGE_2...FRGION_5MOD_AAPAQQTTQPGGGKCHARGE_2NaNSEQ_AAPAQQTTQPGGGK0.00.0000.0000...0.0000.0000.0000.00043.26330.000.0000.0000.0000.000
125Q9NXG2SEQ_AAPAQQTTQPGGGK_MOD_AAPAQQTTQPGGGK_CHARGE_2...NaNMOD_AAPAQQTTQPGGGKCHARGE_2MS1ISOTOPES_2SEQ_AAPAQQTTQPGGGK0.00.0000.0000...0.0000.0000.0000.00035565.30000.000.0000.0000.0000.000
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126 rows × 43 columns

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NaN \n", - "\n", - " MOD CHARGE MS1ISOTOPES SEQ \\\n", - "0 MOD_AAAEEGHIIPR CHARGE_2 NaN SEQ_AAAEEGHIIPR \n", - "1 MOD_AAAEEGHIIPR CHARGE_2 NaN SEQ_AAAEEGHIIPR \n", - "2 MOD_AAAEEGHIIPR CHARGE_2 NaN SEQ_AAAEEGHIIPR \n", - "3 MOD_AAAEEGHIIPR CHARGE_2 NaN SEQ_AAAEEGHIIPR \n", - "4 MOD_AAAEEGHIIPR CHARGE_2 NaN SEQ_AAAEEGHIIPR \n", - ".. ... ... ... ... \n", - "121 MOD_AAPAQQTTQPGGGK CHARGE_2 NaN SEQ_AAPAQQTTQPGGGK \n", - "122 MOD_AAPAQQTTQPGGGK CHARGE_2 NaN SEQ_AAPAQQTTQPGGGK \n", - "123 MOD_AAPAQQTTQPGGGK CHARGE_2 NaN SEQ_AAPAQQTTQPGGGK \n", - "124 MOD_AAPAQQTTQPGGGK CHARGE_2 NaN SEQ_AAPAQQTTQPGGGK \n", - "125 MOD_AAPAQQTTQPGGGK CHARGE_2 MS1ISOTOPES_2 SEQ_AAPAQQTTQPGGGK \n", - "\n", - " 20210407_tims03_Evo03_PaSk_SA_HeLa_100ng_Yeast_100ng_S2-A10_1_24557 \\\n", - "0 0.0 \n", - "1 0.0 \n", - "2 0.0 \n", - "3 0.0 \n", - "4 0.0 \n", - ".. ... \n", - "121 0.0 \n", - "122 0.0 \n", - "123 0.0 \n", - "124 0.0 \n", - "125 0.0 \n", - "\n", - " 20210407_tims03_Evo03_PaSk_SA_HeLa_100ng_Yeast_100ng_S2-A11_1_24560 \\\n", - "0 896.367 \n", - "1 1179.120 \n", - "2 596.609 \n", - "3 1702.010 \n", - "4 722.131 \n", - ".. ... \n", - "121 0.000 \n", - "122 0.000 \n", - "123 0.000 \n", - "124 0.000 \n", - "125 0.000 \n", - "\n", - " 20210407_tims03_Evo03_PaSk_SA_HeLa_100ng_Yeast_100ng_S2-A12_1_24563 ... \\\n", - "0 584.1060 ... \n", - "1 1303.0100 ... \n", - "2 465.8550 ... \n", - "3 58.7502 ... \n", - "4 0.0000 ... \n", - ".. ... ... \n", - "121 0.0000 ... \n", - "122 0.0000 ... \n", - "123 0.0000 ... \n", - "124 0.0000 ... \n", - "125 0.0000 ... \n", - "\n", - " 20210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C12_1_24565 \\\n", - "0 2896.280 \n", - "1 879.823 \n", - "2 2853.020 \n", - "3 1056.070 \n", - "4 582.225 \n", - ".. ... \n", - "121 0.000 \n", - "122 0.000 \n", - "123 0.000 \n", - "124 0.000 \n", - "125 0.000 \n", - "\n", - " 20210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C1_1_24532 \\\n", - "0 714.800 \n", - "1 3659.020 \n", - "2 1595.010 \n", - "3 154.148 \n", - "4 407.820 \n", - ".. ... \n", - "121 0.000 \n", - "122 0.000 \n", - "123 0.000 \n", - "124 0.000 \n", - "125 0.000 \n", - "\n", - " 20210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C2_1_24535 \\\n", - "0 2985.020 \n", - "1 2903.790 \n", - "2 1424.990 \n", - "3 1038.430 \n", - "4 356.076 \n", - ".. ... \n", - "121 0.000 \n", - "122 0.000 \n", - "123 0.000 \n", - "124 0.000 \n", - "125 0.000 \n", - "\n", - " 20210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C3_1_24538 \\\n", - "0 3675.020 \n", - "1 4837.040 \n", - "2 2152.950 \n", - "3 787.176 \n", - "4 499.796 \n", - ".. ... \n", - "121 0.000 \n", - "122 0.000 \n", - "123 0.000 \n", - "124 0.000 \n", - "125 0.000 \n", - "\n", - " 20210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C4_1_24541 \\\n", - "0 1933.8600 \n", - "1 1679.5400 \n", - "2 2022.5200 \n", - "3 1142.1400 \n", - "4 2117.0200 \n", - ".. ... \n", - "121 20447.1000 \n", - "122 454.1350 \n", - "123 310.9380 \n", - "124 43.2633 \n", - "125 35565.3000 \n", - "\n", - " 20210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C5_1_24544 \\\n", - "0 2910.93 \n", - "1 2781.31 \n", - "2 2384.02 \n", - "3 3036.32 \n", - "4 1057.96 \n", - ".. ... \n", - "121 0.00 \n", - "122 0.00 \n", - "123 0.00 \n", - "124 0.00 \n", - "125 0.00 \n", - "\n", - " 20210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C6_1_24547 \\\n", - "0 3961.440 \n", - "1 1194.830 \n", - "2 2195.020 \n", - "3 414.958 \n", - "4 2270.020 \n", - ".. ... \n", - "121 0.000 \n", - "122 0.000 \n", - "123 0.000 \n", - "124 0.000 \n", - "125 0.000 \n", - "\n", - " 20210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C7_1_24550 \\\n", - "0 1963.560 \n", - "1 1009.480 \n", - "2 2385.020 \n", - "3 2242.020 \n", - "4 870.767 \n", - ".. ... \n", - "121 0.000 \n", - "122 0.000 \n", - "123 0.000 \n", - "124 0.000 \n", - "125 0.000 \n", - "\n", - " 20210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C8_1_24553 \\\n", - "0 2534.710 \n", - "1 617.338 \n", - "2 2146.020 \n", - "3 414.917 \n", - "4 915.039 \n", - ".. ... \n", - "121 0.000 \n", - "122 0.000 \n", - "123 0.000 \n", - "124 0.000 \n", - "125 0.000 \n", - "\n", - " 20210407_tims03_Evo03_PaSk_SA_HeLa_200ng_Yeast_2ng_S2-C9_1_24556 \n", - "0 1927.910 \n", - "1 2126.020 \n", - "2 2022.020 \n", - "3 644.922 \n", - "4 588.258 \n", - ".. ... \n", - "121 0.000 \n", - "122 0.000 \n", - "123 0.000 \n", - "124 0.000 \n", - "125 0.000 \n", - "\n", - "[126 rows x 43 columns]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "loading ran through\n", - "using input type spectronaut_precursor_v2\n" - ] - }, - { - "data": { - "text/html": [ - "
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proteinquant_idMODSEQCHARGEE_D170331_S209-S-1-240min_MHRM_R01_T0.htrms
0A0PJW6SEQ_AGGQQVTLTTHAPFGLGAHFTVPLK_MOD__AGGQQVTLTTH...MOD__AGGQQVTLTTHAPFGLGAHFTVPLK_SEQ_AGGQQVTLTTHAPFGLGAHFTVPLKCHARGE_47760.914062
1A0PJW6SEQ_DVLLFEHDR_MOD__DVLLFEHDR__CHARGE_3MOD__DVLLFEHDR_SEQ_DVLLFEHDRCHARGE_319934.281250
2A0PJW6SEQ_GEVPAMLPLK_MOD__GEVPAMLPLK__CHARGE_2MOD__GEVPAMLPLK_SEQ_GEVPAMLPLKCHARGE_2111439.398438
3A0PJW6SEQ_LFDNTVGAYR_MOD__LFDNTVGAYR__CHARGE_2MOD__LFDNTVGAYR_SEQ_LFDNTVGAYRCHARGE_2152354.437500
4A3KMH1;A3KMH1-3SEQ_DGLIVYEDSPLVK_MOD__DGLIVYEDSPLVK__CHARGE_2MOD__DGLIVYEDSPLVK_SEQ_DGLIVYEDSPLVKCHARGE_234521.593750
.....................
189H2KYI5;Q86NI2SEQ_KFPQLILDLATDLK_MOD__KFPQLILDLATDLK__CHARGE_3MOD__KFPQLILDLATDLK_SEQ_KFPQLILDLATDLKCHARGE_35939.184570
190H2KZF8;O44466SEQ_HDVDPHLLGVFLPR_MOD__HDVDPHLLGVFLPR__CHARGE_3MOD__HDVDPHLLGVFLPR_SEQ_HDVDPHLLGVFLPRCHARGE_324022.138672
191K8ESE2;O45391SEQ_AFLSALK_MOD__AFLSALK__CHARGE_2MOD__AFLSALK_SEQ_AFLSALKCHARGE_275916.906250
192K8ESE2;O45391SEQ_MVWFESPSNPLLK_MOD__MVWFESPSNPLLK__CHARGE_2MOD__MVWFESPSNPLLK_SEQ_MVWFESPSNPLLKCHARGE_227252.193359
193K8ESE2;O45391SEQ_VVDIAAVVQTAK_MOD__VVDIAAVVQTAK__CHARGE_2MOD__VVDIAAVVQTAK_SEQ_VVDIAAVVQTAKCHARGE_2167398.843750
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194 rows × 6 columns

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proteinquant_idFRGIONMODCHARGEMS1ISOTOPESSEQE_D170331_S209-S-1-240min_MHRM_R01_T0.htrms
0P00044SEQ_DRNDLITYLK_MOD__DRNDLITYLK__CHARGE_3_FRGIO...FRGION_b3_noloss_1MOD__DRNDLITYLK_CHARGE_3NaNSEQ_DRNDLITYLK2821.802734
1P00044SEQ_DRNDLITYLK_MOD__DRNDLITYLK__CHARGE_3_FRGIO...FRGION_b4_noloss_1MOD__DRNDLITYLK_CHARGE_3NaNSEQ_DRNDLITYLK27549.345703
2P00044SEQ_DRNDLITYLK_MOD__DRNDLITYLK__CHARGE_3_FRGIO...FRGION_b5_noloss_1MOD__DRNDLITYLK_CHARGE_3NaNSEQ_DRNDLITYLK15359.532227
3P00044SEQ_DRNDLITYLK_MOD__DRNDLITYLK__CHARGE_3_FRGIO...FRGION_y3_noloss_1MOD__DRNDLITYLK_CHARGE_3NaNSEQ_DRNDLITYLK6005.712891
4P00044SEQ_DRNDLITYLK_MOD__DRNDLITYLK__CHARGE_3_FRGIO...FRGION_y4_noloss_1MOD__DRNDLITYLK_CHARGE_3NaNSEQ_DRNDLITYLK8474.114258
...........................
4105P07172SEQ_SFGLAGIR_MOD__SFGLAGIR__CHARGE_2_FRGION_y4...FRGION_y4_noloss_1MOD__SFGLAGIR_CHARGE_2NaNSEQ_SFGLAGIR39904.769531
4106P07172SEQ_SFGLAGIR_MOD__SFGLAGIR__CHARGE_2_FRGION_y6...FRGION_y6_noloss_1MOD__SFGLAGIR_CHARGE_2NaNSEQ_SFGLAGIR45823.726562
4107P07172SEQ_SFGLAGIR_MOD__SFGLAGIR__CHARGE_2_MS1ISOTOP...NaNMOD__SFGLAGIR_CHARGE_2MS1ISOTOPES_0SEQ_SFGLAGIR292103.000000
4108P07172SEQ_SFGLAGIR_MOD__SFGLAGIR__CHARGE_2_MS1ISOTOP...NaNMOD__SFGLAGIR_CHARGE_2MS1ISOTOPES_1SEQ_SFGLAGIR85024.100000
4109P07172SEQ_SFGLAGIR_MOD__SFGLAGIR__CHARGE_2_MS1ISOTOP...NaNMOD__SFGLAGIR_CHARGE_2MS1ISOTOPES_3SEQ_SFGLAGIR82662.900000
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4110 rows × 8 columns

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proteinquant_idShotgun_02-01_1Shotgun_02-01_2BoxCar_12-01_3Shotgun_12-01_1Shotgun_12-01_3BoxCar_02-01_3Shotgun_02-01_3BoxCar_02-01_1BoxCar_12-01_1BoxCar_02-01_2Shotgun_12-01_2BoxCar_12-01_2
0P37108SEQ_AAAAAAAAAPAAAATAPTTAATTAATAAQ_MOD_2367.203672810000541180000527370000689370000929610000315090000562880000407110000613190000380630000920600000583740000
1Q96JP5SEQ_AAAAAAAAAVSR_MOD_999.54614000000000000
2O75822SEQ_AAAAAAAGDSDSWDADAFSVEDPVRK_MOD_2592.1728865210002687500027056000110360000130200000427150003250900081675000120600000303000003256900031004000
3P36578SEQ_AAAAAAALQAK_MOD_955.54508172230000019064000002114200000278390000022368000001678900000180560000019348000002124600000171730000021695000001858400000
4Q96P70SEQ_AAAAAAGAASGLPGPVAQGLK_MOD_1747.9581188490000164960000146210000239140000215870000112760000119320000130520000162770000193850000207500000161190000
.............................................
989P41214SEQ_ADLPLLTSTFLGSHMFSCCPEGR_MOD_2595.192001951300002618800018612000768870000000
990P31142SEQ_ADLPVEPVK_MOD_966.5386231190000181590000335950003810100033527000147540000172790000230690000343190001403200003661800039661000
991Q9BTY7SEQ_ADLQAAAVR_MOD_913.49813755260001386700008372100016023000000123880000121290000141300000116540000182870000174590000
992P77338SEQ_ADLQAQLDSLNK_MOD_1314.6779000000000000
993Q15075SEQ_ADLQNHLDTAQNALQDK_MOD_1893.91814094900096967000272710007628300000400990000409490002380500001915100093927000
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994 rows × 14 columns

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proteinquant_idFRGIONMODCHARGEMS1ISOTOPESSEQ20220730_TIMS06_MCT_SA_HeLa_whi40_M07_Ref0s4s8_c2_AID8_01_S5-A6_1_3971_(Dimethyl-n-0)20220730_TIMS06_MCT_SA_HeLa_whi40_M07_Ref0s4s8_c2_AID8_01_S5-A6_1_3971_(Dimethyl-n-4)20220730_TIMS06_MCT_SA_HeLa_whi40_M07_Ref0s4s8_c2_AID8_01_S5-A6_1_3971_(Dimethyl-n-8)...20220730_TIMS06_MCT_SA_HeLa_whi40_M07_Ref0s4s8_c2_AID8_02_S5-A7_1_3973_(Dimethyl-n-8)20220730_TIMS06_MCT_SA_HeLa_whi40_M07_Ref0s4s8_c2_AID8_03_S5-A8_1_3975_(Dimethyl-n-0)20220730_TIMS06_MCT_SA_HeLa_whi40_M07_Ref0s4s8_c2_AID8_03_S5-A8_1_3975_(Dimethyl-n-4)20220730_TIMS06_MCT_SA_HeLa_whi40_M07_Ref0s4s8_c2_AID8_03_S5-A8_1_3975_(Dimethyl-n-8)20220730_TIMS06_MCT_SA_HeLa_whi40_M07_Ref0s4s8_c2_AID8_04_S5-A9_1_3977_(Dimethyl-n-0)20220730_TIMS06_MCT_SA_HeLa_whi40_M07_Ref0s4s8_c2_AID8_04_S5-A9_1_3977_(Dimethyl-n-4)20220730_TIMS06_MCT_SA_HeLa_whi40_M07_Ref0s4s8_c2_AID8_04_S5-A9_1_3977_(Dimethyl-n-8)20220730_TIMS06_MCT_SA_HeLa_whi40_M07_Ref0s4s8_c2_AID8_05_S5-A10_1_3979_(Dimethyl-n-0)20220730_TIMS06_MCT_SA_HeLa_whi40_M07_Ref0s4s8_c2_AID8_05_S5-A10_1_3979_(Dimethyl-n-4)20220730_TIMS06_MCT_SA_HeLa_whi40_M07_Ref0s4s8_c2_AID8_05_S5-A10_1_3979_(Dimethyl-n-8)
0O14497;O14497-2SEQ_AAAGQESEGPAVGPPQPLGK_MOD_AAAGQESEGPAVGPPQP...FRGION_0MOD_(Dimethyl-n-0)AAAGQESEGPAVGPPQPLGK(Dimethy...CHARGE_2NaNSEQ_AAAGQESEGPAVGPPQPLGK0.0000000.00.0...0.00.00.00.048.0018650.00.00.00.0000000.0
1O14497;O14497-2SEQ_AAAGQESEGPAVGPPQPLGK_MOD_AAAGQESEGPAVGPPQP...FRGION_1MOD_(Dimethyl-n-4)AAAGQESEGPAVGPPQPLGK(Dimethy...CHARGE_2NaNSEQ_AAAGQESEGPAVGPPQPLGK0.0000000.00.0...0.00.00.00.00.0000000.00.00.074.0036620.0
2O14497;O14497-2SEQ_AAAGQESEGPAVGPPQPLGK_MOD_AAAGQESEGPAVGPPQP...FRGION_11MOD_(Dimethyl-n-4)AAAGQESEGPAVGPPQPLGK(Dimethy...CHARGE_2NaNSEQ_AAAGQESEGPAVGPPQPLGK0.0000000.00.0...0.00.00.00.00.0000000.00.00.00.0000000.0
3O14497;O14497-2SEQ_AAAGQESEGPAVGPPQPLGK_MOD_AAAGQESEGPAVGPPQP...FRGION_2MOD_(Dimethyl-n-0)AAAGQESEGPAVGPPQPLGK(Dimethy...CHARGE_2NaNSEQ_AAAGQESEGPAVGPPQPLGK0.0000000.00.0...0.00.00.00.069.0037230.00.00.00.0000000.0
5O14497;O14497-2SEQ_AAAGQESEGPAVGPPQPLGK_MOD_AAAGQESEGPAVGPPQP...FRGION_4MOD_(Dimethyl-n-4)AAAGQESEGPAVGPPQPLGK(Dimethy...CHARGE_2NaNSEQ_AAAGQESEGPAVGPPQPLGK0.0000000.00.0...0.00.00.00.00.0000000.00.00.041.0018690.0
..................................................................
7737Q9UNF1SEQ_SYYETSK_MOD_SYYETSK_CHARGE_2_FRGION_4FRGION_4MOD_(Dimethyl-n-0)SYYETSK(Dimethyl-K-0)CHARGE_2NaNSEQ_SYYETSK153.0042730.00.0...0.00.00.00.0423.0093690.00.00.00.0000000.0
7740Q9UNF1SEQ_SYYETSK_MOD_SYYETSK_CHARGE_2_FRGION_5FRGION_5MOD_(Dimethyl-n-0)SYYETSK(Dimethyl-K-0)CHARGE_2NaNSEQ_SYYETSK2101.0507810.00.0...0.00.00.00.01015.0228880.00.00.00.0000000.0
7743Q9UNF1SEQ_SYYETSK_MOD_SYYETSK_CHARGE_2_FRGION_6FRGION_6MOD_(Dimethyl-n-0)SYYETSK(Dimethyl-K-0)CHARGE_2NaNSEQ_SYYETSK5193.1113280.00.0...0.00.00.00.03041.0871580.00.00.00.0000000.0
7745Q9UNF1SEQ_SYYETSK_MOD_SYYETSK_CHARGE_2_FRGION_7FRGION_7MOD_(Dimethyl-n-0)SYYETSK(Dimethyl-K-0)CHARGE_2NaNSEQ_SYYETSK931.0294190.00.0...0.00.00.00.0282.0084230.00.00.00.0000000.0
7748Q9UNF1SEQ_SYYETSK_MOD_SYYETSK_CHARGE_2_MS1ISOTOPES_2NaNMOD_(Dimethyl-n-0)SYYETSK(Dimethyl-K-0)CHARGE_2MS1ISOTOPES_2SEQ_SYYETSK27286.6503900.00.0...0.00.00.00.036903.8437500.00.00.00.0000000.0
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2873 rows × 22 columns

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proteinquant_idsample_2sample_1sample_3
0sp|O94826|TOM70_HUMANSEQ_ADEMYDK_MOD_ADEMYDK0.08525616.00.0
1sp|Q9NUQ3|TXLNG_HUMANSEQ_ALLQMAEEK_MOD_ALLQMAEEK29970788.01596635.530422000.0
2sp|P26640|SYVC_HUMANSEQ_ALSPLEEWLR_MOD_ALSPLEEWLR353638048.0371597760.0312282240.0
3sp|P22087|FBRL_HUMANSEQ_ANCIDSTASAEAVFASEVK_MOD_ANC[57.0215]IDSTAS...61226836.065094176.058320496.0
4sp|P52758|RIDA_HUMANSEQ_APGAIGPYSQAVLVDR_MOD_APGAIGPYSQAVLVDR45480616.00.035133800.0
..................
109sp|Q9Y4E8|UBP15_HUMANSEQ_VEVYLTELK_MOD_VEVYLTELK43754588.026091410.00.0
110sp|Q9HCU5|PREB_HUMANSEQ_VVCFNHDNTLLATGGTDGYVR_MOD_VVC[57.0215]FNHD...33176032.037358996.00.0
111sp|P48960|AGRE5_HUMANSEQ_WACLVAGGSK_MOD_WAC[57.0215]LVAGGSK0.00.00.0
112sp|Q9H845|ACAD9_HUMANSEQ_YAPENLDEQIK_MOD_YAPENLDEQIK28436670.00.00.0
113sp|Q96RP9|EFGM_HUMANSEQ_YGEIPAELR_MOD_YGEIPAELR0.00.00.0
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proteinMODSEQCHARGE20210603_EXPL1_MCT_SA_species_100ng_100_1_02.raw20210603_EXPL1_MCT_SA_species_100ng_100_1_03.raw20210603_EXPL1_MCT_SA_species_100ng_100_1_08.raw20210603_EXPL1_MCT_SA_species_100ng_1_1_06.raw20210603_EXPL1_MCT_SA_species_100ng_1_1_07.raw
quant_id
SEQ_LTGNPELSSLDEVLAK_MOD__LTGNPELSSLDEVLAK__CHARGE_2A5Z2X5MOD__LTGNPELSSLDEVLAK_SEQ_LTGNPELSSLDEVLAKCHARGE_2NaNNaNNaN8.526810e+047.576243e+04
SEQ_ALQSINVK_MOD__ALQSINVK__CHARGE_2O13297MOD__ALQSINVK_SEQ_ALQSINVKCHARGE_2NaNNaNNaN3.606976e+043.476134e+04
SEQ_FSIIESQTR_MOD__FSIIESQTR__CHARGE_2O13297MOD__FSIIESQTR_SEQ_FSIIESQTRCHARGE_2NaNNaNNaN1.336459e+051.651850e+05
SEQ_GISEVTENTGK_MOD__GISEVTENTGK__CHARGE_2O13297MOD__GISEVTENTGK_SEQ_GISEVTENTGKCHARGE_2NaNNaNNaN5.649115e+046.469086e+04
SEQ_IAGNAVGSVVK_MOD__IAGNAVGSVVK__CHARGE_2O13297MOD__IAGNAVGSVVK_SEQ_IAGNAVGSVVKCHARGE_2NaNNaNNaN4.423597e+044.267472e+04
..............................
SEQ_TFFVGGNFK_MOD__TFFVGGNFK__CHARGE_2P00942MOD__TFFVGGNFK_SEQ_TFFVGGNFKCHARGE_2NaNNaN99103.7421881.586443e+071.547431e+07
SEQ_TLDVVER_MOD__TLDVVER__CHARGE_1P00942MOD__TLDVVER_SEQ_TLDVVERCHARGE_1NaNNaNNaN3.609795e+053.837820e+05
SEQ_TLDVVER_MOD__TLDVVER__CHARGE_2P00942MOD__TLDVVER_SEQ_TLDVVERCHARGE_2406187.343750442512.531250413241.3125003.002293e+073.105749e+07
SEQ_WVILGHSER_MOD__WVILGHSER__CHARGE_2P00942MOD__WVILGHSER_SEQ_WVILGHSERCHARGE_2NaNNaNNaN1.055499e+061.013666e+06
SEQ_WVILGHSER_MOD__WVILGHSER__CHARGE_3P00942MOD__WVILGHSER_SEQ_WVILGHSERCHARGE_37131.2036136970.0390627209.5268552.913585e+062.564057e+06
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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "correlation between both processings: 0.9999999999999999\n" - ] - } - ], + "outputs": [], "source": [ "\n", "#hide\n", @@ -2935,23 +183,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Deleted: ../../test_data/unit_tests/input_table_formats/mq_peptides.txt.maxquant_peptides_leading_razor_protein.aq_reformat.tsv\n", - "Deleted: ../../test_data/unit_tests/input_table_formats/fragpipe.tsv.fragpipe_precursors.aq_reformat.tsv\n", - "Deleted: ../../test_data/unit_tests/input_table_formats/spectronaut.tsv.spectronaut_precursor_v2.aq_reformat.tsv\n", - "Deleted: ../../test_data/unit_tests/input_table_formats/diann_test_input_mDIA.tsv.diann_fragion_isotopes_mDIA_raw.aq_reformat.tsv\n", - "Deleted: ../../test_data/unit_tests/input_table_formats/spectronaut_frgion.tsv.spectronaut_fragion_isotopes.aq_reformat.tsv\n", - "Deleted: ../../test_data/unit_tests/input_table_formats/spectronaut.frgions.large.tsv.spectronaut_precursor_v2.aq_reformat.tsv\n", - "Deleted: ../../test_data/unit_tests/input_table_formats/diann.tsv.diann_fragion_isotopes.aq_reformat.tsv\n" - ] - } - ], + "outputs": [], "source": [ "import glob\n", "import os\n", @@ -3004,15 +238,7 @@ "name": "python3" }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", "version": "3.8.16" } }, diff --git a/nbdev_nbs/spectral_library/flat_library.ipynb b/nbdev_nbs/spectral_library/flat_library.ipynb index 61558137..328394d0 100644 --- a/nbdev_nbs/spectral_library/flat_library.ipynb +++ b/nbdev_nbs/spectral_library/flat_library.ipynb @@ -874,7 +874,7 @@ } ], "source": [ - "back_to_base.available_fragment_dfs()" + "back_to_base.available_dense_fragment_dfs()" ] }, { diff --git a/nbs_tests/test_thread.ipynb b/nbs_tests/test_thread.ipynb new file mode 100644 index 00000000..5c8a7bda --- /dev/null +++ b/nbs_tests/test_thread.ipynb @@ -0,0 +1,72 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import numba\n", + "import numpy as np\n", + "\n", + "@numba.njit(nogil=True)\n", + "def fib(n):\n", + " a, b = 0.0, 1.0\n", + " for _ in range(n):\n", + " a, b = b, a + b\n", + " return a\n", + "\n", + "@numba.njit(nogil=True)\n", + "def fib_array(x):\n", + " ret = []\n", + " for n in x:\n", + " ret.append(fib(n))\n", + " return ret\n", + "\n", + "x = np.array(np.arange(1,150000))\n", + "\n", + "ret = fib_array(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from multiprocess.pool import ThreadPool\n", + "from functools import partial\n", + "with ThreadPool(10) as p:\n", + " p.map(lambda x: fib_array(*x), [(x,)]*20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/release/one_click_linux_gui/control b/release/one_click_linux_gui/control index b3422bcf..ce85681f 100644 --- a/release/one_click_linux_gui/control +++ b/release/one_click_linux_gui/control @@ -1,5 +1,5 @@ Package: AlphaBase -Version: 1.2.2 +Version: 1.2.3 Architecture: all Maintainer: Mann Labs Description: AlphaBase diff --git a/release/one_click_linux_gui/create_installer_linux.sh b/release/one_click_linux_gui/create_installer_linux.sh index 7162646b..6a1a11d3 100644 --- a/release/one_click_linux_gui/create_installer_linux.sh +++ b/release/one_click_linux_gui/create_installer_linux.sh @@ -17,7 +17,7 @@ python setup.py sdist bdist_wheel # Setting up the local package cd release/one_click_linux_gui # Make sure you include the required extra packages and always use the stable or very-stable options! -pip install "../../dist/alphabase-1.2.2-py3-none-any.whl[stable]" +pip install "../../dist/alphabase-1.2.3-py3-none-any.whl[stable]" # Creating the stand-alone pyinstaller folder pip install pyinstaller diff --git a/release/one_click_macos_gui/Info.plist b/release/one_click_macos_gui/Info.plist index 9b9976a7..6daf95d0 100644 --- a/release/one_click_macos_gui/Info.plist +++ b/release/one_click_macos_gui/Info.plist @@ -9,9 +9,9 @@ CFBundleIconFile alpha_logo.icns CFBundleIdentifier - alphabase.1.2.2 + alphabase.1.2.3 CFBundleShortVersionString - 1.2.2 + 1.2.3 CFBundleInfoDictionaryVersion 6.0 CFBundleName diff --git a/release/one_click_macos_gui/create_installer_macos.sh b/release/one_click_macos_gui/create_installer_macos.sh index a53568e3..7e98f27a 100644 --- a/release/one_click_macos_gui/create_installer_macos.sh +++ b/release/one_click_macos_gui/create_installer_macos.sh @@ -20,7 +20,7 @@ python setup.py sdist bdist_wheel # Setting up the local package cd release/one_click_macos_gui -pip install "../../dist/alphabase-1.2.2-py3-none-any.whl[stable]" +pip install "../../dist/alphabase-1.2.3-py3-none-any.whl[stable]" # Creating the stand-alone pyinstaller folder pip install pyinstaller @@ -40,5 +40,5 @@ cp ../../LICENSE.txt Resources/LICENSE.txt cp ../logos/alpha_logo.png Resources/alpha_logo.png chmod 777 scripts/* -pkgbuild --root dist/alphabase --identifier de.mpg.biochem.alphabase.app --version 1.2.2 --install-location /Applications/AlphaBase.app --scripts scripts AlphaBase.pkg +pkgbuild --root dist/alphabase --identifier de.mpg.biochem.alphabase.app --version 1.2.3 --install-location /Applications/AlphaBase.app --scripts scripts AlphaBase.pkg productbuild --distribution distribution.xml --resources Resources --package-path AlphaBase.pkg dist/alphabase_gui_installer_macos.pkg diff --git a/release/one_click_macos_gui/distribution.xml b/release/one_click_macos_gui/distribution.xml index 1ad82bd4..84b62a69 100644 --- a/release/one_click_macos_gui/distribution.xml +++ b/release/one_click_macos_gui/distribution.xml @@ -1,6 +1,6 @@ - AlphaBase 1.2.2 + AlphaBase 1.2.3 diff --git a/release/one_click_windows_gui/alphabase_innoinstaller.iss b/release/one_click_windows_gui/alphabase_innoinstaller.iss index 24b514a5..d2aed090 100644 --- a/release/one_click_windows_gui/alphabase_innoinstaller.iss +++ b/release/one_click_windows_gui/alphabase_innoinstaller.iss @@ -2,7 +2,7 @@ ; SEE THE DOCUMENTATION FOR DETAILS ON CREATING INNO SETUP SCRIPT FILES! #define MyAppName "AlphaBase" -#define MyAppVersion "1.2.2" +#define MyAppVersion "1.2.3" #define MyAppPublisher "Max Planck Institute of Biochemistry and the University of Copenhagen, Mann Labs" #define MyAppURL "https://github.com/MannLabs/alphabase" #define MyAppExeName "alphabase_gui.exe" diff --git a/release/one_click_windows_gui/create_installer_windows.sh b/release/one_click_windows_gui/create_installer_windows.sh index bfe0b420..5a0b0246 100644 --- a/release/one_click_windows_gui/create_installer_windows.sh +++ b/release/one_click_windows_gui/create_installer_windows.sh @@ -17,7 +17,7 @@ python setup.py sdist bdist_wheel # Setting up the local package cd release/one_click_windows_gui # Make sure you include the required extra packages and always use the stable or very-stable options! -pip install "../../dist/alphabase-1.2.2-py3-none-any.whl[stable]" +pip install "../../dist/alphabase-1.2.3-py3-none-any.whl[stable]" # Creating the stand-alone pyinstaller folder pip install pyinstaller diff --git a/settings.ini b/settings.ini index c5af54fb..ef0648e2 100644 --- a/settings.ini +++ b/settings.ini @@ -4,7 +4,7 @@ ### Python library ### repo = alphabase lib_name = alphabase -version = 1.2.2 +version = 1.2.3 min_python = 3.7 license = apache2