Merge pull request #55 from dandi/bf-codespell

codespell: ignore any lines which have data.*base64 - embedded binaries, fix typos introduced in recent PRs

Author: yarikoptic

.codespellrc

@@ -1,6 +1,6 @@
 [codespell]
 skip = .git,*.pdf,*.svg
-# all images embedded in .ipynb jsons
-ignore-regex = "image/png": ".*
+# all images and other binaries embedded in .ipynb jsons
+ignore-regex = "image/png": ".*|^ *".*data:\S+;base64.*
 # nd - people just like it
 ignore-words-list = nd

000005/DataJoint/DJ-NWB-Yu-Gutnisky-2016/notebooks/Yu-Gutnisky-2016-examples.ipynb

@@ -1760,10 +1760,10 @@
        "                                <span class=\"djtooltiptext\">in mm, anterior positive, posterior negative</span>\n",
        "                            </div></th><th><div class=\"djtooltip\">\n",
        "                                <p id=\"primary\">coordinate_ml</p>\n",
-       "                                <span class=\"djtooltiptext\">in mm, always postive, number larger when more lateral</span>\n",
+       "                                <span class=\"djtooltiptext\">in mm, always positive, number larger when more lateral</span>\n",
        "                            </div></th><th><div class=\"djtooltip\">\n",
        "                                <p id=\"primary\">coordinate_dv</p>\n",
-       "                                <span class=\"djtooltiptext\">in mm, always postive, number larger when more ventral (deeper)</span>\n",
+       "                                <span class=\"djtooltiptext\">in mm, always positive, number larger when more ventral (deeper)</span>\n",
        "                            </div></th><th><div class=\"djtooltip\">\n",
        "                                <p id=\"nonprimary\">spike_times</p>\n",
        "                                <span class=\"djtooltiptext\">(s) time of each spike, with respect to the start of session</span>\n",

000005/DataJoint/DJ-NWB-Yu-Gutnisky-2016/pipeline/acquisition.py

@@ -1,5 +1,5 @@
 '''
-Schema of aquisition information.
+Schema of acquisition information.
 '''
 import re
 import os

000005/DataJoint/DJ-NWB-Yu-Gutnisky-2016/pipeline/reference.py

@@ -93,8 +93,8 @@ class ActionLocation(dj.Manual):
     -> BrainLocation
     -> CoordinateReference
     coordinate_ap: decimal(4,2)    # in mm, anterior positive, posterior negative 
-    coordinate_ml: decimal(4,2)    # in mm, always postive, number larger when more lateral
-    coordinate_dv: decimal(4,2)    # in mm, always postive, number larger when more ventral (deeper)
+    coordinate_ml: decimal(4,2)    # in mm, always positive, number larger when more lateral
+    coordinate_dv: decimal(4,2)    # in mm, always positive, number larger when more ventral (deeper)
     """
 
 

000005/DataJoint/DJ-NWB-Yu-Gutnisky-2016/pipeline/virus.py

@@ -29,7 +29,7 @@ class Virus(dj.Manual):
     virus: varchar(64) # name of the virus
     ---
     -> VirusSource
-    virus_lot_number="":  varchar(128)  # lot numnber of the virus
+    virus_lot_number="":  varchar(128)  # lot number of the virus
     virus_titer=null:       float     # x10^12GC/mL
     """
 

000006/DataJoint/DJ-NWB-Economo-2018/README.md

@@ -8,15 +8,15 @@ This notebook presents data and results associated with the following papers:
 >Michael N. Economo, Sarada Viswanathan, Bosiljka Tasic, Erhan Bas, Johan Winnubst, Vilas Menon, Lucas T. Graybuck, Thuc Nghi Nguyen, Kimberly A. Smith, Zizhen Yao, Lihua Wang, Charles R. Gerfen, Jayaram Chandrashekar, Hongkui Zeng, Loren L. Looger & Karel Svoboda. "Distinct descending motor cortex
 pathways and their roles in movement" (2018) Nature (https://doi.org/10.1038/s41586-018-0642-9)
 
-The study identified two types of pyramidal track (PT) neuron in the mouse motor cortex, referred to as PT<sub>upper</sub> and PT<sub>lower</sub>. Further investigation using extracellular recordings in the anterior lateral motor cortex (ALM) during a delay-response task, the study revealed specialized roles for the two neuron types: i) PT<sub>upper</sub> neurons are preferentially involed in motor planning, ii) PT<sub>lower</sub> neurons are more involved in movement execution.  
+The study identified two types of pyramidal track (PT) neuron in the mouse motor cortex, referred to as PT<sub>upper</sub> and PT<sub>lower</sub>. Further investigation using extracellular recordings in the anterior lateral motor cortex (ALM) during a delay-response task, the study revealed specialized roles for the two neuron types: i) PT<sub>upper</sub> neurons are preferentially involved in motor planning, ii) PT<sub>lower</sub> neurons are more involved in movement execution.  
 
 A ***DataJoint*** data pipeline has been constructed for this study, with the presented data ingested into this pipeline. This notebook demonstrates the queries, processing, and reproduction of several figures from the paper. From the pipeline, export capability to ***NWB 2.0*** format is also available.
 
 ## About the data
 
 The dataset comprises of extracellular recordings and spike sorted results of the mouse's ALM during a delay-response task. The behavior data includes detailed description of the trial structure (e.g. trial timing, trial instruction, trial response, etc.) and the timing of the lick events (e.g. lick left, lick right onset). 
 
-Original data is publically available at: doi: 10.25378/janelia.7007846
+Original data is publicly available at: doi: 10.25378/janelia.7007846
 
 The data in original MATLAB format (.mat) have been ingested into a DataJoint data pipeline presented below. 
 

000006/DataJoint/DJ-NWB-Economo-2018/notebooks/Economo-2018-examples.ipynb

@@ -10,7 +10,7 @@
     ">Michael N. Economo, Sarada Viswanathan, Bosiljka Tasic, Erhan Bas, Johan Winnubst, Vilas Menon, Lucas T. Graybuck,Thuc Nghi Nguyen, Kimberly A. Smith, Zizhen Yao, Lihua Wang, Charles R. Gerfen, Jayaram Chandrashekar, Hongkui Zeng, Loren L. Looger & Karel Svoboda. \"Distinct descending motor cortex\n",
     "pathways and their roles in movement\" (2018) Nature (https://doi.org/10.1038/s41586-018-0642-9)\n",
     "\n",
-    "The study identified two types of pyramidal track (PT) neuron in the mouse motor cortex, referred to as PT<sub>upper</sub> and PT<sub>lower</sub>. Further investigation using extracellular recordings in the anterior lateral motor cortex (ALM) during a delay-response task, the study revealed specialized roles for the two neuron types: i) PT<sub>upper</sub> neurons are preferentially involed in motor planning, ii) PT<sub>lower</sub> neurons are more involved in movement execution.  \n",
+    "The study identified two types of pyramidal track (PT) neuron in the mouse motor cortex, referred to as PT<sub>upper</sub> and PT<sub>lower</sub>. Further investigation using extracellular recordings in the anterior lateral motor cortex (ALM) during a delay-response task, the study revealed specialized roles for the two neuron types: i) PT<sub>upper</sub> neurons are preferentially involved in motor planning, ii) PT<sub>lower</sub> neurons are more involved in movement execution.  \n",
     "\n",
     "A ***DataJoint*** data pipeline has been constructed for this study, with the presented data ingested into this pipeline. This notebook demonstrates the queries, processing, and reproduction of several figures from the paper. From the pipeline, export capability to ***NWB 2.0*** format is also available."
    ]
@@ -23,7 +23,7 @@
     "\n",
     "The dataset comprises of extracellular recordings and spike sorted results of the mouse's ALM during a delay-response task. The behavior data includes detailed description of the trial structure (e.g. trial timing, trial instruction, trial response, etc.) and the timing of the lick events (e.g. lick left, lick right onset). \n",
     "\n",
-    "Original data is publically available at: doi: 10.25378/janelia.7007846\n",
+    "Original data is publicly available at: doi: 10.25378/janelia.7007846\n",
     "\n",
     "The data in original MATLAB format (.mat) have been ingested into a DataJoint data pipeline presented below. \n",
     "\n",

000006/DataJoint/DJ-NWB-Economo-2018/pipeline/reference.py

@@ -73,8 +73,8 @@ class ActionLocation(dj.Manual):
     -> BrainLocation
     -> CoordinateReference
     coordinate_ap: decimal(4,2)    # in mm, anterior positive, posterior negative 
-    coordinate_ml: decimal(4,2)    # in mm, always postive, number larger when more lateral
-    coordinate_dv: decimal(4,2)    # in mm, always postive, number larger when more ventral (deeper)
+    coordinate_ml: decimal(4,2)    # in mm, always positive, number larger when more lateral
+    coordinate_dv: decimal(4,2)    # in mm, always positive, number larger when more ventral (deeper)
     """
 
 

000007/DataJoint/DJ-NWB-Gao-2018/pipeline/acquisition.py

@@ -1,5 +1,5 @@
 '''
-Schema of aquisition information.
+Schema of acquisition information.
 '''
 import datajoint as dj
 from pipeline import reference, subject
@@ -49,6 +49,6 @@ class PhotoStim(dj.Manual):
     -> reference.Hemisphere
     -> reference.CoordinateReference
     photo_stim_coordinate_ap: float    # in mm, anterior positive, posterior negative
-    photo_stim_coordinate_ml: float    # in mm, always postive, number larger when more lateral
-    photo_stim_coordinate_dv: float    # in mm, always postive, number larger when more ventral (deeper)
+    photo_stim_coordinate_ml: float    # in mm, always positive, number larger when more lateral
+    photo_stim_coordinate_dv: float    # in mm, always positive, number larger when more ventral (deeper)
     """

000007/DataJoint/DJ-NWB-Gao-2018/pipeline/reference.py

@@ -95,7 +95,7 @@ class Virus(dj.Lookup):
     virus: varchar(32) # name of the virus
     ---
     -> VirusSource
-    virus_lot_number="":  varchar(128)  # lot numnber of the virus
+    virus_lot_number="":  varchar(128)  # lot number of the virus
     """
     contents = [{
         'virus': 'AAV2-hSyn-hChR2(H134R)-EYFP',

000009/DataJoint/DJ-NWB-Guo-Inagaki-2017/notebooks/Guo-Inagaki-2017-examples.ipynb

@@ -12,7 +12,7 @@
     "\n",
     "The study sheds new insights into the persistnent neural activity of the frontal thalamocortical circuit, suggesting that reciprocal excitation between the anterior lateral motor cortex (ALM) and the thalamus is needed to maintain persistent activity in motor preparation. Intracellular and extracellular recordings were performed on mouse ALM and thalamus neurons during tectile discrimination task with and without optogenetic perturbation. The study reported near-complete collapse of ALM activity upon photoinhibition of the thalamus, and photoinhibition of the ALM resulted in diminished thalamic activity. \n",
     "\n",
-    "This notebook provides demonstrations of working with a ***DataJoint*** data pipeline in querying data, apply data conditioning and reproduce some key figures in the paper. The orignal data available in NWB 2.0 format had been ingested into a DataJoint data pipeline (data pipeline schema is given below). As a validation of complete ingestion of the original data into DataJoint, figures 3b,e, 6b,e and 4b,e,h will be reproduced in this notebook."
+    "This notebook provides demonstrations of working with a ***DataJoint*** data pipeline in querying data, apply data conditioning and reproduce some key figures in the paper. The original data available in NWB 2.0 format had been ingested into a DataJoint data pipeline (data pipeline schema is given below). As a validation of complete ingestion of the original data into DataJoint, figures 3b,e, 6b,e and 4b,e,h will be reproduced in this notebook."
    ]
   },
   {
@@ -1565,10 +1565,10 @@
        "                                <span class=\"djtooltiptext\">in mm, anterior positive, posterior negative</span>\n",
        "                            </div></th><th><div class=\"djtooltip\">\n",
        "                                <p id=\"primary\">coordinate_ml</p>\n",
-       "                                <span class=\"djtooltiptext\">in mm, always postive, number larger when more lateral</span>\n",
+       "                                <span class=\"djtooltiptext\">in mm, always positive, number larger when more lateral</span>\n",
        "                            </div></th><th><div class=\"djtooltip\">\n",
        "                                <p id=\"primary\">coordinate_dv</p>\n",
-       "                                <span class=\"djtooltiptext\">in mm, always postive, number larger when more ventral (deeper)</span>\n",
+       "                                <span class=\"djtooltiptext\">in mm, always positive, number larger when more ventral (deeper)</span>\n",
        "                            </div></th><th><div class=\"djtooltip\">\n",
        "                                <p id=\"primary\">unit_id</p>\n",
        "                                <span class=\"djtooltiptext\"></span>\n",
@@ -3110,7 +3110,7 @@
    "source": [
     "The following parts of this example relates to the extracellular recording results of this study, namely the neuronal spiking patterns in response to photostimulation.\n",
     "\n",
-    "Fairly similar to the routine layed out above, we wish to query neuronal spike times, segmented time-locked to the \"delay\" period, categorized by recording locations and photostimulation locations:\n",
+    "Fairly similar to the routine laid out above, we wish to query neuronal spike times, segmented time-locked to the \"delay\" period, categorized by recording locations and photostimulation locations:\n",
     "+ Recording at ALM, stimulation at ALM\n",
     "+ Recording at ALM, stimulation at Thalamus\n",
     "+ Recording at Thalamus, stimulation at Thalamus\n",
@@ -3309,7 +3309,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here, we define a helper function to query and fetch the trial-segmented spike times for a single unit in a specifed session: `query_unit_segmented_spiketimes()`"
+    "Here, we define a helper function to query and fetch the trial-segmented spike times for a single unit in a specified session: `query_unit_segmented_spiketimes()`"
    ]
   },
   {
@@ -3318,7 +3318,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# get trial-segmented spiketimes for a single unit in the specifed session\n",
+    "# get trial-segmented spiketimes for a single unit in the specified session\n",
     "def query_unit_segmented_spiketimes(sess_key, unit, trial_key, seg_param_key):\n",
     "    data_keys = (extracellular.TrialSegmentedUnitSpikeTimes & sess_key & {'unit_id': unit} & seg_param_key &\n",
     "                 ((acquisition.TrialSet.Trial & trial_key) & (stimulation.TrialPhotoStimInfo & trial_key))).fetch('KEY')\n",
@@ -3619,7 +3619,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# get trial-segmented spiketimes for all units in all specifed sessions\n",
+    "# get trial-segmented spiketimes for all units in all specified sessions\n",
     "def extract_segmented_spiketimes_histogram(units, trial_cond, seg_param_key, time_range=(-1.5, 3), bin_counts=1000):\n",
     "    unit_keys = units.fetch('KEY')\n",
     "    print(f'Found {len(unit_keys)} units')     \n",

000009/DataJoint/DJ-NWB-Guo-Inagaki-2017/pipeline/acquisition.py

@@ -1,5 +1,5 @@
 '''
-Schema of aquisition information.
+Schema of acquisition information.
 '''
 import re
 import os

000009/DataJoint/DJ-NWB-Guo-Inagaki-2017/pipeline/reference.py

@@ -76,8 +76,8 @@ class ActionLocation(dj.Manual):
     -> BrainLocation
     -> CoordinateReference
     coordinate_ap: decimal(4,2)    # in mm, anterior positive, posterior negative 
-    coordinate_ml: decimal(4,2)    # in mm, always postive, number larger when more lateral
-    coordinate_dv: decimal(4,2)    # in mm, always postive, number larger when more ventral (deeper)
+    coordinate_ml: decimal(4,2)    # in mm, always positive, number larger when more lateral
+    coordinate_dv: decimal(4,2)    # in mm, always positive, number larger when more ventral (deeper)
     """
 
 
@@ -133,7 +133,7 @@ class Virus(dj.Lookup):
     virus: varchar(64) # name of the virus
     ---
     -> VirusSource
-    virus_lot_number="":  varchar(128)  # lot numnber of the virus
+    virus_lot_number="":  varchar(128)  # lot number of the virus
     virus_titer=null:       float     # x10^12GC/mL
     """
 

000010/DataJoint/DJ-NWB-Li-2015b/notebooks/Li-2015b-examples.ipynb

@@ -1985,7 +1985,7 @@
       "---\n",
       "-> imaging.CellType\n",
       "roi_trace            : longblob                     # average fluorescence of roi obj-timeSeriesArrayHash-value(1, 1)-valueMatrix\n",
-      "neuropil_trace       : longblob                     # average fluorescence of neuopil surounding each ROI, obj-timeSeriesArrayHash-value(1, 1)-valueMatrix\n",
+      "neuropil_trace       : longblob                     # average fluorescence of neuopil surrounding each ROI, obj-timeSeriesArrayHash-value(1, 1)-valueMatrix\n",
       "roi_pixel_list       : longblob                     # pixel list of this roi\n",
       "neuropil_pixel_list  : longblob                     # pixel list of the neuropil surrounding the roi\n",
       "inc                  : tinyint                      # whether included (criteria - cells > 1.05 times brighter than neuropil)\n",
@@ -3023,7 +3023,7 @@
     "    {'subject_id': 216219, 'session': 5, 'roi_idx': 22}, # contra preferring, response starting from the sample period\n",
     "    {'subject_id': 216219, 'session': 5, 'roi_idx': 49}, # contra preferring, response starting from the delay period\n",
     "    {'subject_id': 221541, 'session': 13, 'roi_idx': 44}, # contra preferring, response starting from the response period\n",
-    "    {'subject_id': 216219, 'session': 14, 'roi_idx': 9}, # ipsi preferring, repsonse starting from the sample period\n",
+    "    {'subject_id': 216219, 'session': 14, 'roi_idx': 9}, # ipsi preferring, response starting from the sample period\n",
     "    {'subject_id': 216219, 'session': 5, 'roi_idx': 35}, # ipsi preferring, response starting from the delay period\n",
     "    {'subject_id': 216219, 'session': 6, 'roi_idx': 47}, # ipsi preferring, response starting from the response period\n",
     "]"
@@ -4903,10 +4903,10 @@
     "    ipsi_cells = (imaging.RoiAnalyses & 'is_responsive=1' & 'selectivity=\"Ipsi\"') & \\\n",
     "                  (imaging.Scan.Roi & f'cell_type=\"{cell_type}\"' & 'inc=1') & L5_key\n",
     "    \n",
-    "    # average df/f of contra trials, sorted by the reponse time of the half peak.\n",
+    "    # average df/f of contra trials, sorted by the response time of the half peak.\n",
     "    contra_avg_l, contra_avg_r = contra_cells.fetch('dff_m_l', 'dff_m_r', order_by='frame_rise_half')\n",
     "    \n",
-    "    # average df/f of contra trials, sorted by the reponse time of the half peak.\n",
+    "    # average df/f of contra trials, sorted by the response time of the half peak.\n",
     "    ipsi_avg_l, ipsi_avg_r = ipsi_cells.fetch('dff_m_l', 'dff_m_r', order_by='frame_rise_half')\n",
     "    \n",
     "    \n",

000010/DataJoint/DJ-NWB-Li-2015b/pipeline/experiment.py

@@ -87,7 +87,7 @@ class Photostim(dj.Manual):
     ml_location=null: float # um from ref ; right is positive; based on manipulator coordinates/reconstructed track
     ap_location=null: float # um from ref; anterior is positive; based on manipulator coordinates/reconstructed track
     dv_location=null: float # um from dura; ventral is positive; based on manipulator coordinates/reconstructed track
-    ml_angle=null: float # Angle between the manipulator/reconstructed track and the Medio-Lateral axis. A tilt towards the right hemishpere is positive.
+    ml_angle=null: float # Angle between the manipulator/reconstructed track and the Medio-Lateral axis. A tilt towards the right hemisphere is positive.
     ap_angle=null: float # Angle between the manipulator/reconstructed track and the Anterior-Posterior axis. An anterior tilt is positive.
     waveform=null:  longblob       # normalized to maximal power. The value of the maximal power is specified for each PhotostimTrialEvent individually
     frequency=null: float  # (Hz)

000010/DataJoint/DJ-NWB-Li-2015b/pipeline/export/datajoint_to_nwb.py

@@ -95,7 +95,7 @@ def export_to_nwb(session_key, nwb_output_dir=default_nwb_output_dir, save=False
         conversion=1e-6,
         unit='micrometers')
 
-    # ----- Segementation information -----
+    # ----- Segmentation information -----
     # link the imaging segmentation to the nwb file
     ophys = nwbfile.create_processing_module('ophys', 'Processing result of imaging')
     img_seg = pynwb.ophys.ImageSegmentation()

000010/DataJoint/DJ-NWB-Li-2015b/pipeline/imaging.py

@@ -43,7 +43,7 @@ class Roi(dj.Part):
         ---
         -> CellType
         roi_trace:          longblob        # average fluorescence of roi obj-timeSeriesArrayHash-value(1, 1)-valueMatrix
-        neuropil_trace:     longblob        # average fluorescence of neuopil surounding each ROI, obj-timeSeriesArrayHash-value(1, 1)-valueMatrix
+        neuropil_trace:     longblob        # average fluorescence of neuopil surrounding each ROI, obj-timeSeriesArrayHash-value(1, 1)-valueMatrix
         roi_pixel_list:     longblob        # pixel list of this roi
         neuropil_pixel_list:longblob        # pixel list of the neuropil surrounding the roi
         inc:                bool            # whether included (criteria - cells > 1.05 times brighter than neuropil)