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| You can save a Transmission files using the :ref:`Save node <node_Save>` and work with the data directly in scripts, jupyter notebooks etc. You can also save them through the flowchart console (and plot consoles) through :func:`Transmission.to_hdf5 <mesmerize.Transmission.to_hdf5>`. | ||
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| Workign with Transmission files | ||
| ------------------------------- | ||
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| Load a saved Transmission instance using :func:`Transmission.from_hdf5 <mesmerize.Transmission.from_hdf5>` | ||
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| .. code-block:: python | ||
| :linenos: | ||
| >>> from mesmerize import Transmission | ||
| >>> from uuid import UUID | ||
| # load transmission file | ||
| >>> t = Transmission.from_hdf5('/share/data/temp/kushal/data.trn') | ||
| <mesmerize.analysis.data_types.Transmission at 0x7f4d42f386a0> | ||
| # The DataFrame is always the 'df' attribute | ||
| >>> t.df.head() | ||
| CurvePath ... FCLUSTER_LABELS | ||
| 0 curves/a2-_-1-_-843c2d43-75f3-421a-9fef-483d1e... ... 8 | ||
| 1 curves/brn3b_a6-_-2-_-21557a64-6868-4ff4-8db1-... ... 4 | ||
| 2 curves/brn3b_a6-_-2-_-21557a64-6868-4ff4-8db1-... ... 5 | ||
| 3 curves/brn3b_day1_3-_-2-_-ff3e95df-0e15-495c-9... ... 8 | ||
| 4 curves/brn3b_day1_3-_-2-_-ff3e95df-0e15-495c-9... ... 6 | ||
| [5 rows x 27 columns] | ||
| # the `df` is just a pandas dataframe | ||
| # View a list of samples in the current file | ||
| >>> t.df.SampleID.unique() | ||
| array(['a2-_-1', 'a5-_-1', 'brn3b_a6-_-2', 'brn3b_day1_3-_-2', | ||
| 'brn3b_day1_a1-_-2', 'brn3b_day1_a2-_-2', 'brn3b_day1_a4-_-2', | ||
| 'brn3b_day2_a1-_-2', 'brn3b_day2_a1-_-t', 'brn3b_day2_a10-_-2', | ||
| 'brn3b_day2_a2-_-1', 'brn3b_day2_a2-_-3', 'brn3b_day2_a8-_-1', | ||
| 'cesa_a1-_-1', 'cesa_a1-_-2', 'cesa_a1_jan_2019-_-1', | ||
| 'cesa_a1_jan_2019-_-2', 'cesa_a2-_-2', 'cesa_a6-_-1', | ||
| 'cesa_a7-_-1', 'cesa_a7-_-2', 'cesa_a8-_-1', 'cesa_a9-_-1', | ||
| 'cng_ch4_day1_a2-_-t1', 'cng_ch4_day1_a2-_-t2', | ||
| 'cng_ch4_day2_a4-_-t1', 'dmrt1_day1_a2-_-2', 'dmrt1_day1_a4-_-t2', | ||
| 'dmrt1_day1_a5-_-', 'dmrt1_day1_a6-_-t', 'dmrt1_day1_a6-_-t2', | ||
| 'dmrt1_day2_a1-_-t1', 'dmrt1_day2_a1-_-t2', 'dmrt1_day2_a2-_-t1', | ||
| 'dmrt1_day2_a3-_-t1', 'dmrt1_day2_a3-_-t2', 'dmrt1_day2_a4-_-t1', | ||
| 'dmrt1_day2_a4-_-t2', 'hnk1_a5-_-2', 'hnk1_a6-_-1', 'hnk1_a7-_-1', | ||
| 'hnk1_a7-_-2', 'hnk1_a8-_-1', 'pc2_a10-_-1', 'pc2_a11-_-1', | ||
| 'pc2_a13-_-1', 'pc2_a14-_-1', 'pc2_a15-_-1', 'pc2_a16-_-1', | ||
| 'pc2_a9-_-1', 'pde9_day1_a2-_-2', 'pde9_day1_a3-_-1', | ||
| 'pde9_day1_a4-_-1', 'pde9_day1_a4-_-2', 'pde9_day2_a2-_-t2', | ||
| 'pde9_day2_a2-_-t4', 'pde9_day2_a4-_-t1', 'pde9_day2_a4-_-t2', | ||
| 'pde9_day2_a4-_-t3', 'pde9_day2_a5-_-t1', 'pde9_day2_a5-_-t2', | ||
| 'pde9_day2_a6-_-t1', 'pde9_day2_a7-_-t1', 'pde9_day2_a7-_-t2'], | ||
| dtype=object) | ||
| # Show data associated with a single sample | ||
| >>> t.df[t.df['SampleID'] == 'brn3b_day1_a1-_-2'] | ||
| CurvePath ... FCLUSTER_LABELS | ||
| 6 curves/brn3b_day1_a1-_-2-_-d3c5f225-7039-4abd-... ... 6 | ||
| 7 curves/brn3b_day1_a1-_-2-_-d3c5f225-7039-4abd-... ... 6 | ||
| 8 curves/brn3b_day1_a1-_-2-_-d3c5f225-7039-4abd-... ... 5 | ||
| 9 curves/brn3b_day1_a1-_-2-_-d3c5f225-7039-4abd-... ... 7 | ||
| 10 curves/brn3b_day1_a1-_-2-_-d3c5f225-7039-4abd-... ... 5 | ||
| # View the data associated with one ROI | ||
| # the `uuid_curve` is a unique identifier for each curve/ROI | ||
| >> t.df[t.df['SampleID'] == 'brn3b_day1_a1-_-2'].iloc[0] | ||
| CurvePath curves/brn3b_day1_a1-_-2-_-d3c5f225-7039-4abd-... | ||
| ImgInfoPath images/brn3b_day1_a1-_-2-_-d3c5f225-7039-4abd-... | ||
| ImgPath images/brn3b_day1_a1-_-2-_-d3c5f225-7039-4abd-... | ||
| ImgUUID d3c5f225-7039-4abd-a7a1-5e9ef2150013 | ||
| ROI_State {'roi_xs': [554, 553, 553, 552, 552, 551, 551,... | ||
| SampleID brn3b_day1_a1-_-2 | ||
| anatomical_location palp | ||
| cell_name palp | ||
| comments untagged | ||
| date 20190425_110103 | ||
| dorso_ventral_axis untagged | ||
| misc {} | ||
| morphology untagged | ||
| promoter brn3b | ||
| rostro_caudal_axis untagged | ||
| stimulus_name [untagged] | ||
| uuid_curve f44fbd3d-6eaa-4e19-a677-496908565fde | ||
| _RAW_CURVE [81.41972198848178, 75.61356993008134, 70.0493... | ||
| meta {'origin': 'AwesomeImager', 'version': '4107ff... | ||
| stim_maps [[None]] | ||
| _BLOCK_ 3e069e2d-d012-47ee-830c-93d85197e2f4 | ||
| _SPLICE_ARRAYS [2.646593459501195, 1.8252819116136887, 1.7422... | ||
| _NORMALIZE [0.0681729940259753, 0.06533186950232853, 0.06... | ||
| _RFFT [443.19357880089615, -66.8777897472859, 55.244... | ||
| _ABSOLUTE_VALUE [443.19357880089615, 66.8777897472859, 55.2443... | ||
| _LOG_TRANSFORM [2.646593459501195, 1.8252819116136887, 1.7422... | ||
| FCLUSTER_LABELS 6 | ||
| Name: 6, dtype: object | ||
| # Show the ROI object data | ||
| >>> t.df[t.df['SampleID'] == 'brn3b_day1_a1-_-2'].iloc[0]['ROI_State'] | ||
| {'roi_xs': array([554, 553, 553, 552, 552, 551, 551, 551, 551, 550, 550, 550, 549, | ||
| 548, 547, 547, 546, 546, 545, 545, 544, 543, 543, 542, 541, 541, | ||
| 540, 540, 539, 539, 538, 537, 536, 535, 534, 533, 532, 531, 531, | ||
| 530, 529, 528, 527, 527, 526, 526, 525, 525, 525, 524, 524, 523, | ||
| 522, 522, 521, 521, 520, 521, 521, 521, 521, 521, 522, 522, 522, | ||
| 522, 522, 522, 522, 522, 521, 521, 521, 521, 521, 521, 522, 523, | ||
| 524, 524, 525, 525, 525, 526, 526, 527, 528, 528, 529, 529, 529, | ||
| 530, 530, 531, 532, 532, 533, 534, 535, 535, 536, 536, 537, 538, | ||
| 539, 540, 540, 541, 541, 542, 542, 543, 544, 545, 546, 546, 547, | ||
| 548, 548, 549, 549, 549, 549, 550, 550, 550, 550, 551, 551, 551, | ||
| 552, 552, 552, 553, 553, 553, 554, 554, 554, 553, 554, 554, 554, | ||
| 554, 554]), | ||
| 'roi_ys': array([155, 156, 156, 157, 157, 158, 159, 160, 160, 161, 162, 162, 162, | ||
| 162, 163, 163, 164, 164, 165, 165, 165, 166, 166, 166, 167, 167, | ||
| 167, 166, 167, 167, 167, 167, 167, 167, 167, 167, 167, 168, 168, | ||
| 168, 168, 168, 168, 167, 167, 166, 166, 165, 164, 164, 163, 163, | ||
| 163, 162, 162, 161, 161, 160, 160, 159, 158, 157, 156, 156, 155, | ||
| 154, 153, 152, 151, 150, 150, 149, 148, 147, 146, 145, 144, 144, | ||
| 144, 144, 143, 143, 142, 141, 141, 140, 140, 140, 139, 139, 138, | ||
| 137, 137, 136, 136, 136, 135, 135, 135, 136, 136, 137, 137, 137, | ||
| 137, 137, 138, 138, 138, 137, 137, 136, 136, 136, 136, 137, 137, | ||
| 137, 138, 138, 139, 140, 141, 141, 142, 143, 144, 144, 145, 146, | ||
| 146, 147, 148, 148, 149, 150, 150, 151, 151, 152, 152, 153, 154, | ||
| 155, 155]), | ||
| 'curve_data': (array([ 0, 1, 2, ..., 2996, 2997, 2998]), | ||
| array([ 81.41972199, 75.61356993, 70.04934883, ..., 195.4416283 , | ||
| 184.8844155 , 174.76708104])), | ||
| 'tags': {'anatomical_location': 'palp', | ||
| 'cell_name': 'palp', | ||
| 'morphology': 'untagged'}, | ||
| 'roi_type': 'CNMFROI', | ||
| 'cnmf_idx': 2} | ||
| View History Log | ||
| ---------------- | ||
| Transmissions have a `history_trace` attribute which is an instance of :class:`HistoryTrace <mesmerize.analysis.data_types.HistoryTrace>`. | ||
| Use the :func:`get_data_block_history <mesmerize.analysis.data_types.HistoryTrace.get_data_block_history>` and :func:`get_operations_list <mesmerize.analysis.data_types.HistoryTrace.get_operations_list>` methods to view the history log of a data block. | ||
| .. code-block:: python | ||
| :linenos: | ||
| # To view the history log, first get the block UUID of the dataframe row of which you want the history log | ||
| # Block UUIDs are stored in the _BLOCK_ column | ||
| >>> bid = t.df.iloc[10]._BLOCK_ | ||
| >>> bid | ||
| '248a6ece-e60e-4a09-845e-188a5199d262' | ||
| # Get the history log of this data block | ||
| # HistoryTrace.get_operations_list() returns a list of operations, without parameters | ||
| # HistoryTrace.get_data_block_history() returns the operations list with the parameters | ||
| >>> t.history_trace.get_operations_list(bid) | ||
| ['spawn_transmission', | ||
| 'splice_arrays', | ||
| 'normalize', | ||
| 'rfft', | ||
| 'absolute_value', | ||
| 'log_transform', | ||
| 'splice_arrays', | ||
| 'fcluster'] | ||
| # View the entire history log with all params | ||
| >>> t.history_trace.get_data_block_history(bid) | ||
| [{'spawn_transmission': {'sub_dataframe_name': 'neuronal', | ||
| 'dataframe_filter_history': {'dataframe_filter_history': ['df[~df["promoter"].isin([\'cesa\', \'hnk1\'])]', | ||
| 'df[~df["promoter"].isin([\'cesa\', \'hnk1\'])]', | ||
| 'df[~df["cell_name"].isin([\'not_a_neuron\', \'non_neuronal\', \'untagged\', \'ependymal\'])]']}}}, | ||
| {'splice_arrays': {'data_column': '_RAW_CURVE', | ||
| 'start_ix': 0, | ||
| 'end_ix': 2990, | ||
| 'units': 'time'}}, | ||
| {'normalize': {'data_column': '_SPLICE_ARRAYS', 'units': 'time'}}, | ||
| {'rfft': {'data_column': '_NORMALIZE', | ||
| 'frequencies': [0.0, | ||
| 0.0033444816053511705, | ||
| 0.0033444816053511705, | ||
| 0.006688963210702341, | ||
| ... | ||
| # Get the parameters for the 'fcluster' operation | ||
| >>> fp = t.history_trace.get_operation_params(bid, 'fcluster') | ||
| # remove the linkage matrix first so we can view the other params | ||
| >>> fp.pop('linkage_matrix');fp | ||
| {'threshold': 8.0, | ||
| 'criterion': 'maxclust', | ||
| 'depth': 1, | ||
| 'linkage_params': {'method': 'complete', | ||
| 'metric': 'wasserstein', | ||
| 'optimal_ordering': True}} | ||
| # Draw the analysis history as a graph | ||
| # This will open your defeault pdf viewer with the graph | ||
| >>> t.history_trace.draw_graph(bid, view=True) | ||
| # If you are using the API to perform analysis on | ||
| # transmission files, you can use the `HistoryTrace` | ||
| # to log the analysis history | ||
| # For example, add a number `3.14` to all datapoints in a curve | ||
| >>> t.df['_RAW_CURVE'] = t.df['_RAW_CURVE'].apply(lambda x: x + 3.14) | ||
| # Append the analysis log | ||
| >>> t.history_trace.add_operation(data_block_id='all', operation='addition', parameters={'value': 3.14} | ||
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