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z1_L){1zkf)Vdt~Lpd(MqMo|l>Lv$dMMhpEPhGlntdoJHmg zgynY`^}we_J1mscIbH|$ez?(vDZMAj-G=qTvSqW z438H)?0(AIq zD~8Q}q=|pFD3!4O`cGHlX`dfVsP6VH8@U0hMDLasEVxW3BX+9!R!<#B3B=Nuv}TDB zl00O5?S(m!Ih7CJqvKSX3%mgr9XvcdxQs&DCFp?7$UzuT(5-Ot!u`z!5Jpcm+Z z{D{OE(yPy*NCkV*LAU1@D*fD?o;xv`q?!69f&!6^r1yEp``Og>%U#I2PysJUuhh6wDfWFgrvsjEQ1I z?@4b&eiVQc+6oj1(Cv=8OI2B!2!=zwyON!L5X_@R0VRNT-hyS01NfG3%_V09lDAB@ zW+r+#aFJv7x(X*;FM*-ZTNO56AQG}f8bVLK!9eShzz=WN@QE-?^|W&d^FU6D=N4wP zmU#CbY?0DF16_#7u_Gp$*UFMW|BWc6N$z0{V?`P5%A_jKS-|>MXk`V^AsG;I8g>2w z>*5nkhT`Ni+32~nVnl{HQo@5)j|jEoka@ZhsZvi7ytMYpHElPf2V4{4$UIsXcol{B zeJei1VxdPrusM77B3MUZT%3rDdu_ryUw`o)&llL>nSDzk$o~Z#lmELW`#V`Lmq-~{ W+vwHm=LO55j+>t{%R74h>VE=sE 16.213030 -0.748998 \n", - "1 16.176315 -0.511927 \n", - "2 16.546413 -0.426618 \n", - "3 16.210049 -0.508251 \n", - "4 16.473521 -0.260388 \n", "Only one unique time difference: [5000000]\n" ] } ], "source": [ "# Resample to the nominal sampling frequency\n", - "gaze_resampled_data = gaze.interpolate()\n", - "print(gaze_resampled_data.head())\n", + "gaze_resampled = gaze.interpolate()\n", "\n", - "ts = gaze_resampled_data[\"timestamp [ns]\"].values\n", - "ts_diffs = np.diff(ts)\n", - "ts_diff_unique = np.unique(ts_diffs)\n", - "print(f\"Only one unique time difference: {np.unique(ts_diffs)}\")" + "print(f\"Only one unique time difference: {np.unique(gaze_resampled.ts_diff)}\")" ] }, { @@ -211,15 +184,17 @@ "text": [ "Original gaze data length: 18769\n", "Original IMU data length: 10919\n", - "Data length after resampling to IMU: 10919\n" + "Gaze data length after resampling to IMU: 10919\n" ] } ], "source": [ "print(f\"Original gaze data length: {gaze.data.shape[0]}\")\n", "print(f\"Original IMU data length: {imu.data.shape[0]}\")\n", - "gaze_resampled_to_imu_data = gaze.interpolate(new_ts=imu.ts)\n", - "print(f\"Data length after resampling to IMU: {gaze_resampled_to_imu_data.shape[0]}\")" + "gaze_resampled_to_imu = gaze.interpolate(new_ts=imu.ts)\n", + "print(\n", + " f\"Gaze data length after resampling to IMU: {gaze_resampled_to_imu.data.shape[0]}\"\n", + ")" ] }, { @@ -235,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -249,48 +224,55 @@ "Using lowest sampling rate: 110 Hz (['imu'])\n", "Using latest start timestamp: 1725032224878547732 (['imu'])\n", "Using earliest last timestamp: 1725032319533909732 (['imu'])\n", - " timestamp [ns] time [s] gaze x [px] gaze y [px] worn fixation id \\\n", - "0 1725032224878547732 0.000000 1073.410354 611.095861 True 1 \n", - "1 1725032224887638641 0.009091 1069.801082 613.382535 True 1 \n", - "2 1725032224896729550 0.018182 1070.090109 613.439696 True 1 \n", - "3 1725032224905820459 0.027273 1069.891351 612.921757 True 1 \n", - "4 1725032224914911368 0.036364 1069.692588 612.403803 True 1 \n", + " gaze x [px] gaze y [px] worn fixation id blink id \\\n", + "1725032224878547732 1073.410354 611.095861 True 1 \n", + "1725032224887638641 1069.801082 613.382535 True 1 \n", + "1725032224896729550 1070.090109 613.439696 True 1 \n", + "1725032224905820459 1069.891351 612.921757 True 1 \n", + "1725032224914911368 1069.692588 612.403803 True 1 \n", + "\n", + " azimuth [deg] elevation [deg] pupil diameter left [mm] \\\n", + "1725032224878547732 16.591703 -0.129540 5.036588 \n", + "1725032224887638641 16.360605 -0.274666 5.093205 \n", + "1725032224896729550 16.379116 -0.278283 5.078107 \n", + "1725032224905820459 16.366379 -0.245426 5.077680 \n", + "1725032224914911368 16.353641 -0.212567 5.077253 \n", "\n", - " blink id azimuth [deg] elevation [deg] pupil diameter left [mm] ... \\\n", - "0 16.591703 -0.129540 5.036588 ... \n", - "1 16.360605 -0.274666 5.093205 ... \n", - "2 16.379116 -0.278283 5.078107 ... \n", - "3 16.366379 -0.245426 5.077680 ... \n", - "4 16.353641 -0.212567 5.077253 ... \n", + " pupil diameter right [mm] eyeball center left x [mm] \\\n", + "1725032224878547732 4.254749 -31.742046 \n", + "1725032224887638641 4.358431 -31.746807 \n", + "1725032224896729550 4.439704 -31.700505 \n", + "1725032224905820459 4.442760 -31.711353 \n", + "1725032224914911368 4.445815 -31.722201 \n", "\n", - " acceleration x [g] acceleration y [g] acceleration z [g] roll [deg] \\\n", - "0 0.063477 -0.058594 0.940430 -2.550682 \n", - "1 0.057486 -0.048916 0.942273 -2.602498 \n", - "2 0.051494 -0.039238 0.944117 -2.654314 \n", - "3 0.046721 -0.045800 0.944336 -2.699777 \n", - "4 0.042113 -0.054556 0.944336 -2.744383 \n", + " ... acceleration x [g] acceleration y [g] \\\n", + "1725032224878547732 ... 0.063477 -0.058594 \n", + "1725032224887638641 ... 0.057486 -0.048916 \n", + "1725032224896729550 ... 0.051494 -0.039238 \n", + "1725032224905820459 ... 0.046721 -0.045800 \n", + "1725032224914911368 ... 0.042113 -0.054556 \n", "\n", - " pitch [deg] yaw [deg] quaternion w quaternion x quaternion y \\\n", - "0 -4.461626 -172.335180 0.067636 -0.024792 0.037342 \n", - "1 -4.493445 -172.562393 0.065682 -0.025185 0.037639 \n", - "2 -4.525265 -172.789613 0.063728 -0.025579 0.037936 \n", - "3 -4.556964 -173.013398 0.061802 -0.025917 0.038238 \n", - "4 -4.588647 -173.236726 0.059879 -0.026247 0.038541 \n", + " acceleration z [g] roll [deg] pitch [deg] yaw [deg] \\\n", + "1725032224878547732 0.940430 -2.550682 -4.461626 -172.335180 \n", + "1725032224887638641 0.942273 -2.602498 -4.493445 -172.562393 \n", + "1725032224896729550 0.944117 -2.654314 -4.525265 -172.789613 \n", + "1725032224905820459 0.944336 -2.699777 -4.556964 -173.013398 \n", + "1725032224914911368 0.944336 -2.744383 -4.588647 -173.236726 \n", "\n", - " quaternion z \n", - "0 -0.996703 \n", - "1 -0.996810 \n", - "2 -0.996917 \n", - "3 -0.997017 \n", - "4 -0.997115 \n", + " quaternion w quaternion x quaternion y quaternion z \n", + "1725032224878547732 0.067636 -0.024792 0.037342 -0.996703 \n", + "1725032224887638641 0.065682 -0.025185 0.037639 -0.996810 \n", + "1725032224896729550 0.063728 -0.025579 0.037936 -0.996917 \n", + "1725032224905820459 0.061802 -0.025917 0.038238 -0.997017 \n", + "1725032224914911368 0.059879 -0.026247 0.038541 -0.997115 \n", "\n", - "[5 rows x 36 columns]\n" + "[5 rows x 34 columns]\n" ] } ], "source": [ - "concat_data = recording.concat_streams([\"gaze\", \"eye_states\", \"imu\"])\n", - "print(concat_data.head())" + "concat_stream = recording.concat_streams([\"gaze\", \"eye_states\", \"imu\"])\n", + "print(concat_stream.data.head())" ] }, { @@ -329,8 +311,8 @@ "source": [ "start_time = 5\n", "end_time = 5.3\n", - "start_ts = time_to_ts(start_time, concat_data)\n", - "end_ts = time_to_ts(end_time, concat_data)\n", + "start_ts = time_to_ts(start_time, concat_stream)\n", + "end_ts = time_to_ts(end_time, concat_stream)\n", "\n", "raw_gaze_data_slice = gaze.data[\n", " (gaze.data[\"timestamp [ns]\"] >= start_ts) & (gaze.data[\"timestamp [ns]\"] <= end_ts)\n", @@ -342,9 +324,9 @@ "raw_imu_data_slice = imu.data[\n", " (imu.data[\"timestamp [ns]\"] >= start_ts) & (imu.data[\"timestamp [ns]\"] <= end_ts)\n", "]\n", - "concat_data_slice = concat_data[\n", - " (concat_data[\"timestamp [ns]\"] >= start_ts)\n", - " & (concat_data[\"timestamp [ns]\"] <= end_ts)\n", + "concat_data_slice = concat_stream[\n", + " (concat_stream[\"timestamp [ns]\"] >= start_ts)\n", + " & (concat_stream[\"timestamp [ns]\"] <= end_ts)\n", "]\n", "\n", "# plot all data in the same scatter plot\n", @@ -448,7 +430,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.6" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/_sources/tutorials/read_recording.ipynb.txt b/_sources/tutorials/read_recording.ipynb.txt index 19644ff..9568f42 100644 --- a/_sources/tutorials/read_recording.ipynb.txt +++ b/_sources/tutorials/read_recording.ipynb.txt @@ -143,19 +143,19 @@ "Wearer ID: bcff2832-cfcb-4f89-abef-7bbfe91ec561\n", "Wearer name: Qian\n", "Recording start time: 2024-08-30 17:37:01.527000\n", - "Recording duration: 98.213 s\n", - " exist filename path\n", - "3d_eye_states True 3d_eye_states.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\3d_eye_states.csv\n", - "blinks True blinks.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\blinks.csv\n", - "events True events.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\events.csv\n", - "fixations True fixations.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\fixations.csv\n", - "gaze True gaze.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\gaze.csv\n", - "imu True imu.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\imu.csv\n", - "labels True labels.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\labels.csv\n", - "saccades True saccades.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\saccades.csv\n", - "world_timestamps True world_timestamps.csv C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\world_timestamps.csv\n", - "scene_video_info False None None\n", - "scene_video False None None\n", + "Recording duration: 98.213s\n", + " exist filename path\n", + "3d_eye_states True 3d_eye_states.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\3d_eye_states.csv\n", + "blinks True blinks.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\blinks.csv\n", + "events True events.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\events.csv\n", + "fixations True fixations.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\fixations.csv\n", + "gaze True gaze.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\gaze.csv\n", + "imu True imu.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\imu.csv\n", + "labels True labels.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\labels.csv\n", + "saccades True saccades.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\saccades.csv\n", + "world_timestamps True world_timestamps.csv D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\world_timestamps.csv\n", + "scene_video_info False None None\n", + "scene_video False None None\n", "\n" ] } @@ -182,7 +182,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "\n", + "\n", "None\n" ] }, @@ -190,7 +190,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\pyneon\\recording.py:275: UserWarning: Scene video not loaded because no video or video timestamps file was found.\n", + "D:\\GitHub\\pyneon\\pyneon\\recording.py:275: UserWarning: Scene video not loaded because no video or video timestamps file was found.\n", " warnings.warn(\n" ] } @@ -220,26 +220,21 @@ "name": "stdout", "output_type": "stream", "text": [ - " timestamp [ns] gaze x [px] gaze y [px] \\\n", - "2024-08-30 15:37:04.852161732 1725032224852161732 1067.486 620.856 \n", - "2024-08-30 15:37:04.857165732 1725032224857165732 1066.920 617.117 \n", - "2024-08-30 15:37:04.862161732 1725032224862161732 1072.699 615.780 \n", - "2024-08-30 15:37:04.867161732 1725032224867161732 1067.447 617.062 \n", - "2024-08-30 15:37:04.872161732 1725032224872161732 1071.564 613.158 \n", - "\n", - " worn fixation id blink id azimuth [deg] \\\n", - "2024-08-30 15:37:04.852161732 True 1 16.213030 \n", - "2024-08-30 15:37:04.857165732 True 1 16.176285 \n", - "2024-08-30 15:37:04.862161732 True 1 16.546413 \n", - "2024-08-30 15:37:04.867161732 True 1 16.210049 \n", - "2024-08-30 15:37:04.872161732 True 1 16.473521 \n", + " gaze x [px] gaze y [px] worn fixation id blink id \\\n", + "timestamp [ns] \n", + "1725032224852161732 1067.486 620.856 True 1 \n", + "1725032224857165732 1066.920 617.117 True 1 \n", + "1725032224862161732 1072.699 615.780 True 1 \n", + "1725032224867161732 1067.447 617.062 True 1 \n", + "1725032224872161732 1071.564 613.158 True 1 \n", "\n", - " elevation [deg] time [s] \n", - "2024-08-30 15:37:04.852161732 -0.748998 0.000000 \n", - "2024-08-30 15:37:04.857165732 -0.511733 0.005004 \n", - "2024-08-30 15:37:04.862161732 -0.426618 0.010000 \n", - "2024-08-30 15:37:04.867161732 -0.508251 0.015000 \n", - "2024-08-30 15:37:04.872161732 -0.260388 0.020000 \n" + " azimuth [deg] elevation [deg] \n", + "timestamp [ns] \n", + "1725032224852161732 16.213030 -0.748998 \n", + "1725032224857165732 16.176285 -0.511733 \n", + "1725032224862161732 16.546413 -0.426618 \n", + "1725032224867161732 16.210049 -0.508251 \n", + "1725032224872161732 16.473521 -0.260388 \n" ] } ], @@ -263,7 +258,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "timestamp [ns] Int64\n", "gaze x [px] float64\n", "gaze y [px] float64\n", "worn bool\n", @@ -271,7 +265,6 @@ "blink id Int32\n", "azimuth [deg] float64\n", "elevation [deg] float64\n", - "time [s] float64\n", "dtype: object\n" ] } @@ -291,22 +284,43 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "saccade id 2.0\n", + "end timestamp [ns] 1725032225347526656.0\n", + "duration [ms] 65.0\n", + "amplitude [px] 228.36139\n", + "amplitude [deg] 14.676102\n", + "mean velocity [px/s] 3685.269894\n", + "peak velocity [px/s] 5411.775481\n", + "Name: 1725032225282527732, dtype: Float64\n" + ] + }, + { + "ename": "TypeError", + "evalue": "unhashable type: 'numpy.ndarray'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[12], line 19\u001b[0m\n\u001b[0;32m 17\u001b[0m saccade \u001b[38;5;241m=\u001b[39m saccades\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(saccade)\n\u001b[1;32m---> 19\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxvspan\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43msaccade\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaccade\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mend timestamp [ns]\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlightgray\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[0;32m 21\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m 22\u001b[0m ax\u001b[38;5;241m.\u001b[39mtext(\n\u001b[0;32m 23\u001b[0m (saccade\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mto_numpy() \u001b[38;5;241m+\u001b[39m saccade[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mend timestamp [ns]\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m,\n\u001b[0;32m 24\u001b[0m \u001b[38;5;241m1050\u001b[39m,\n\u001b[0;32m 25\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSaccade\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 26\u001b[0m horizontalalignment\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m 27\u001b[0m )\n\u001b[0;32m 29\u001b[0m \u001b[38;5;66;03m# Visualize gaze x and pupil diameter left\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1087\u001b[0m, in \u001b[0;36mAxes.axvspan\u001b[1;34m(self, xmin, xmax, ymin, ymax, **kwargs)\u001b[0m\n\u001b[0;32m 1085\u001b[0m \u001b[38;5;66;03m# Strip units away.\u001b[39;00m\n\u001b[0;32m 1086\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_no_units([ymin, ymax], [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mymin\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mymax\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m-> 1087\u001b[0m (xmin, xmax), \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_unit_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mxmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxmax\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1089\u001b[0m p \u001b[38;5;241m=\u001b[39m mpatches\u001b[38;5;241m.\u001b[39mRectangle((xmin, ymin), xmax \u001b[38;5;241m-\u001b[39m xmin, ymax \u001b[38;5;241m-\u001b[39m ymin, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 1090\u001b[0m p\u001b[38;5;241m.\u001b[39mset_transform(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_xaxis_transform(which\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrid\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n", + "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2585\u001b[0m, in \u001b[0;36m_AxesBase._process_unit_info\u001b[1;34m(self, datasets, kwargs, convert)\u001b[0m\n\u001b[0;32m 2583\u001b[0m \u001b[38;5;66;03m# Update from data if axis is already set but no unit is set yet.\u001b[39;00m\n\u001b[0;32m 2584\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m axis\u001b[38;5;241m.\u001b[39mhave_units():\n\u001b[1;32m-> 2585\u001b[0m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 2586\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis_name, axis \u001b[38;5;129;01min\u001b[39;00m axis_map\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 2587\u001b[0m \u001b[38;5;66;03m# Return if no axis is set.\u001b[39;00m\n\u001b[0;32m 2588\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axis.py:1756\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 1754\u001b[0m neednew \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m!=\u001b[39m converter\n\u001b[0;32m 1755\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m=\u001b[39m converter\n\u001b[1;32m-> 1756\u001b[0m default \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdefault_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1757\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m default \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 1758\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_units(default)\n", + "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:105\u001b[0m, in \u001b[0;36mStrCategoryConverter.default_units\u001b[1;34m(data, axis)\u001b[0m\n\u001b[0;32m 103\u001b[0m \u001b[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[0;32m 104\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 105\u001b[0m axis\u001b[38;5;241m.\u001b[39mset_units(\u001b[43mUnitData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m 106\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 107\u001b[0m axis\u001b[38;5;241m.\u001b[39munits\u001b[38;5;241m.\u001b[39mupdate(data)\n", + "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:181\u001b[0m, in \u001b[0;36mUnitData.__init__\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 179\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_counter \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mcount()\n\u001b[0;32m 180\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 181\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:214\u001b[0m, in \u001b[0;36mUnitData.update\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m 212\u001b[0m \u001b[38;5;66;03m# check if convertible to number:\u001b[39;00m\n\u001b[0;32m 213\u001b[0m convertible \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 214\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m \u001b[43mOrderedDict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfromkeys\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m 215\u001b[0m \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[0;32m 216\u001b[0m _api\u001b[38;5;241m.\u001b[39mcheck_isinstance((\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m), value\u001b[38;5;241m=\u001b[39mval)\n\u001b[0;32m 217\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[0;32m 218\u001b[0m \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n", + "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'" + ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -333,11 +347,10 @@ "\n", "# Visualize the 2nd saccade\n", "saccade = saccades.data.iloc[1]\n", - "ax.axvspan(\n", - " saccade[\"start timestamp [ns]\"], saccade[\"end timestamp [ns]\"], color=\"lightgray\"\n", - ")\n", + "print(saccade)\n", + "ax.axvspan(saccade.index.values, saccade[\"end timestamp [ns]\"], color=\"lightgray\")\n", "ax.text(\n", - " (saccade[\"start timestamp [ns]\"] + saccade[\"end timestamp [ns]\"]) / 2,\n", + " (saccade.index.values + saccade[\"end timestamp [ns]\"]) / 2,\n", " 1050,\n", " \"Saccade\",\n", " horizontalalignment=\"center\",\n", @@ -347,14 +360,14 @@ "sns.scatterplot(\n", " ax=ax,\n", " data=gaze.data.head(100),\n", - " x=\"timestamp [ns]\",\n", + " x=gaze.data.index,\n", " y=\"gaze x [px]\",\n", " color=gaze_color,\n", ")\n", "sns.scatterplot(\n", " ax=ax2,\n", " data=imu.data.head(60),\n", - " x=\"timestamp [ns]\",\n", + " x=imu.data.index,\n", " y=\"gyro x [deg/s]\",\n", " color=gyro_color,\n", ")" @@ -465,7 +478,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.6" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/genindex.html b/genindex.html index 94af96f..ad8b0ea 100644 --- a/genindex.html +++ b/genindex.html @@ -366,20 +366,16 @@

C

  • (pyneon.NeonRecording method)
    • -
  • construct_event_times() (in module pyneon.preprocess) -
    • @@ -389,12 +385,16 @@

    D

    @@ -430,7 +430,7 @@

    F

    +
  • is_uniformly_sampled (pyneon.stream.NeonStream property) +

    • L

    - +
    • pyneon.export @@ -617,6 +610,8 @@

      P

    • module
    +
    • pyneon.preprocess @@ -629,6 +624,13 @@

      P

      • +
    • + pyneon.tabular + +
    • @@ -667,7 +669,7 @@

      S
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    Python Module Index

    pyneon - - -     - pyneon.data -     @@ -364,6 +359,11 @@

    Python Module Index

        pyneon.stream + + +     + pyneon.tabular +     diff --git a/reference/data.html b/reference/data.html index ae28c0a..ee2e14f 100644 --- a/reference/data.html +++ b/reference/data.html @@ -370,23 +370,39 @@
    -
    -

    Classes for individual data types#

    +
    +

    Classes for individual data types#

    -
    -class pyneon.data.NeonTabular(file: Path)#
    +
    +class pyneon.tabular.NeonTabular(file: Path)#

    Bases: object

    Base for Neon tabular data. It reads from a CSV file and stores the data as a pandas DataFrame (with section and recording IDs removed). The timestamp [ns] (for streams) or start timestamp [ns] (for events) column is set as the index.

    +
    +
    Parameters:
    +

    file (Path) – Path to the CSV file.

    +
    +
    +
    +
    +data#
    +

    The processed data with the timestamp index.

    +
    +
    Type:
    +

    pd.DataFrame

    +
    +
    +
    +
    class pyneon.stream.NeonStream(file: Path)#
    -

    Bases: NeonTabular

    +

    Bases: NeonTabular

    Base for Neon continuous data (gaze, eye states, IMU). -It must contain a timestamp [ns] column.

    +It’s indexed by timestamp [ns].

    Parameters:

    file (pathlib.Path) – Path to the CSV file containing the stream data.

    @@ -415,144 +431,161 @@
    -
    -timestamps#
    -

    Timestamps of the stream in nanoseconds.

    +
    +sampling_freq_nominal#
    +

    Nominal sampling frequency of the stream as specified by Pupil Labs +(https://pupil-labs.com/products/neon/specs).

    Type:
    -

    np.ndarray

    +

    int or None

    -
    +
    +
    +property timestamps: ndarray#
    +

    Timestamps of the stream in nanoseconds.

    +
    + +
    -ts#
    +property ts: ndarray#

    Alias for timestamps.

    -
    -
    Type:
    -

    np.ndarray

    -
    -
    -
    +
    -first_ts#
    +property first_ts: int#

    First timestamp of the stream.

    -
    -
    Type:
    -

    int

    -
    -
    -
    +
    -last_ts#
    +property last_ts: int#

    Last timestamp of the stream.

    -
    -
    Type:
    -

    int

    -
    -
    -
    +
    +
    +property ts_diff: ndarray#
    +

    Difference between consecutive timestamps.

    +
    + +
    -times#
    +property times: ndarray#

    Timestamps converted to seconds relative to stream start.

    -
    -
    Type:
    -

    np.ndarray

    -
    -
    -
    +
    -duration#
    +property duration: float#

    Duration of the stream in seconds.

    -
    -
    Type:
    -

    float

    -
    -
    -
    +
    -sampling_freq_effective#
    -

    Effective sampling frequency of the stream -(number of time points divided by duration).

    -
    -
    Type:
    -

    float

    -
    -
    +property sampling_freq_effective: float# +

    Effective sampling frequency of the stream.

    -
    -
    -sampling_freq_nominal#
    -

    Nominal sampling frequency of the stream as specified by Pupil Labs -(https://pupil-labs.com/products/neon/specs).

    -
    -
    Type:
    -

    int

    -
    -
    +
    +
    +property is_uniformly_sampled: bool#
    +

    Whether the stream is uniformly sampled.

    +
    + +
    +
    +time_to_ts(time: Number | ndarray) → ndarray#
    +

    Convert time(s) in seconds to timestamp(s) in nanoseconds.

    -crop(tmin: Number | None = None, tmax: Number | None = None, by: Literal['timestamp', 'time'] = 'timestamp', inplace: bool = False) → DataFrame#
    -

    Crop data to a specific time range.

    +crop(tmin: Number | None = None, tmax: Number | None = None, by: Literal['timestamp', 'time', 'row'] = 'timestamp', inplace: bool = False) → NeonStream | None# +

    Crop data to a specific time range based on timestamps, +relative times since start, or row numbers.

    Parameters:
      -

    • tmin (number, optional) – Start time or timestamp to crop the data to. If None, -the minimum timestamp or time in the data is used. Defaults to None.

      • -

    • tmax (number, optional) – End time or timestamp to crop the data to. If None, -the maximum timestamp or time in the data is used. Defaults to None.

      • -

    • by ("timestamp" or "time", optional) – Whether tmin and tmax are UTC timestamps in nanoseconds -or relative times in seconds. Defaults to “timestamp”.

      • +

    • tmin (number, optional) – Start timestamp/time/row to crop the data to. If None, +the minimum timestamp/time/row in the data is used. Defaults to None.

      • +

    • tmax (number, optional) – End timestamp/time/row to crop the data to. If None, +the maximum timestamp/time/row in the data is used. Defaults to None.

      • +

    • by ("timestamp" or "time" or "row", optional) – Whether tmin and tmax are UTC timestamps in nanoseconds +OR relative times in seconds OR row numbers of the stream data. +Defaults to “timestamp”.

    • inplace (bool, optional) – Whether to replace the data in the object with the cropped data. Defaults to False.

    Returns:
    -

    Cropped data.

    +

    Cropped stream if inplace=False, otherwise None.

    Return type:
    -

    pd.DataFrame

    +

    NeonStream or None

    -interpolate(new_ts: None | ndarray = None, float_kind: str = 'linear', other_kind: str = 'nearest', inplace: bool = False) → DataFrame#
    +interpolate(new_ts: ndarray | None = None, float_kind: str = 'linear', other_kind: str = 'nearest', inplace: bool = False) → NeonStream | None#

    Interpolate the stream to a new set of timestamps.

    Parameters:
      -

    • new_ts (np.ndarray, optional) – New timestamps to evaluate the interpolant at. If None, new timestamps +

    • new_ts (np.ndarray, optional) – An array of new timestamps (in nanoseconds) +at which to evaluate the interpolant. If None (default), new timestamps are generated according to the nominal sampling frequency of the stream as specified by Pupil Labs: https://pupil-labs.com/products/neon/specs.

      • -

    • data (pd.DataFrame) – Data to interpolate. Must contain a monotonically increasing -timestamp [ns] column.

    • float_kind (str, optional) – Kind of interpolation applied on columns of float type, -by default “linear”. For details see scipy.interpolate.interp1d.

      • +by default "linear". For details see scipy.interpolate.interp1d.

    • other_kind (str, optional) – Kind of interpolation applied on columns of other types, -by default “nearest”.

      • +by default "nearest". For details see scipy.interpolate.interp1d.

      +

    • inplace (bool, optional) – Whether to replace the data in the object with the interpolated data. +Defaults to False.

    Returns:
    -

    Interpolated data.

    +

    Interpolated stream if inplace=False, otherwise None.

    Return type:
    -

    pandas.DataFrame

    +

    NeonStream or None

    +
    +
    +
    + +
    +
    +window_average(new_ts: ndarray, window_size: int | None = None, inplace: bool = False) → NeonStream | None#
    +

    Take the average over a time window to obtain smoothed data at new timestamps.

    +
    +
    Parameters:
    +
      +

    • new_ts (np.ndarray) – An array of new timestamps (in nanoseconds) +at which to compute the windowed averages. +The median interval between these new timestamps must be larger than +the median interval between the original data timestamps, i.e., +np.median(np.diff(new_ts)) > np.median(np.diff(data.index)). +In other words, only downsampling is supported.

      • +

    • window_size (int, optional) – The size of the time window (in nanoseconds) +over which to compute the average around each new timestamp. +If None (default), the window size is set to the median interval +between the new timestamps, i.e., np.median(np.diff(new_ts)). +The window size must be larger than the median interval between the original data timestamps, +i.e., window_size > np.median(np.diff(data.index)).

      • +

    • inplace (bool, optional) – Whether to replace the data in the object with the window averaged data. +Defaults to False.

      • +
    +
    +
    Returns:
    +

    Stream with window average applied on data if inplace=False, otherwise None.

    +
    +
    Return type:
    +

    NeonStream or None

    @@ -590,7 +623,7 @@
    class pyneon.events.NeonEV(file)#
    -

    Bases: NeonTabular

    +

    Bases: NeonTabular

    Base for Neon event data (blinks, fixations, saccades, “events” messages).

    @@ -776,20 +809,27 @@
    -
    -
    -pyneon.preprocess.window_average(new_ts: ndarray, data: DataFrame, window_size: int | None = None) → DataFrame#
    -

    Take the average over a time window to obtain smoothed data at new timestamps.

    -
    -
    Parameters:
    -
      -

    • new_ts (np.ndarray) – New timestamps to evaluate the window average at. The median of the differences -between the new timestamps must be larger than the median of the differences -between the old timestamps. In other words, only downsampling is supported.

      • -

    • data (pd.DataFrame) – Data to apply window average to. Must contain a monotonically increasing -timestamp [ns] column.

      • -

    • window_size (int, optional) – Size of the time window in nanoseconds. If None, the window size is -set to the median of the differences between the new timestamps. -Defaults to None.

      • -
    -
    -
    Returns:
    -

    Data with window average applied.

    -
    -
    Return type:
    -

    pd.DataFrame

    -
    -
    -
    -
    pyneon.preprocess.map_gaze_to_video(rec: NeonRecording) → DataFrame#
    @@ -770,11 +750,10 @@

    Parameters:
      -
    • crop()
    • interpolate()
      • +
    • window_average()
    • concat_streams()
    • concat_events()
      • -
    • window_average()
    • map_gaze_to_video()
    • estimate_scanpath()
    • overlay_scanpath_on_video()
      • diff --git a/reference/recording.html b/reference/recording.html index 3fbe83e..e4c7166 100644 --- a/reference/recording.html +++ b/reference/recording.html @@ -530,7 +530,7 @@

      NeonRecording class
      -concat_streams(stream_names: str | list[str], sampling_freq: Number | str = 'min', resamp_float_kind: str = 'linear', resamp_other_kind: str = 'nearest', inplace: bool = False) → DataFrame#
      +concat_streams(stream_names: str | list[str], sampling_freq: Number | str = 'min', resamp_float_kind: str = 'linear', resamp_other_kind: str = 'nearest', inplace: bool = False) → CustomStream#

      Concatenate data from different streams under common timestamps. Since the streams may have different timestamps and sampling frequencies, resampling of all streams to a set of common timestamps is performed. diff --git a/searchindex.js b/searchindex.js index 559ee72..f098d54 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"1. Setup: Loading a Neon Recording": [[13, "1.-Setup:-Loading-a-Neon-Recording"]], "2. Mapping Gaze Data to Video Frames": [[13, "2.-Mapping-Gaze-Data-to-Video-Frames"]], "3. Estimating the Scanpath": [[13, "3.-Estimating-the-Scanpath"]], "4. Understanding Fixation Status": [[13, "4.-Understanding-Fixation-Status"]], "5. Overlaying Fixations on the Video": [[13, "5.-Overlaying-Fixations-on-the-Video"]], "Classes for individual data types": [[1, null]], "Concatenating different streams": [[12, "Concatenating-different-streams"]], "Conclusion": [[8, "Conclusion"]], "Data and metadata of a NeonRecording": [[11, "Data-and-metadata-of-a-NeonRecording"]], "Data format": [[0, "data-format"]], "Data streams and events": [[11, "Data-streams-and-events"]], "Export Neon data recording to BIDS formats": [[9, null]], "Exportation module": [[3, null]], "Installation": [[0, "installation"]], "License": [[0, "license"]], "Mapping Scanpath to video": [[13, null]], "NeonDataset class": [[2, null]], "NeonRecording class": [[6, null]], "Parameters:": [[5, "parameters"], [6, "parameters"], [6, "id1"], [6, "id2"]], "Preprocessing module": [[5, null]], "PyNeon API": [[4, null]], "PyNeon Tutorials": [[10, null]], "Reading a Neon dataset/recording": [[11, null]], "Reading sample data": [[11, "Reading-sample-data"]], "Resample Data and Concatenate Channels": [[12, null]], "Resampling data streams": [[12, "Resampling-data-streams"]], "Step 1: Loading Sample Data": [[8, "Step-1:-Loading-Sample-Data"]], "Step 2: Constructing Event Times": [[8, "Step-2:-Constructing-Event-Times"]], "Step 3: Verifying Event Intervals": [[8, "Step-3:-Verifying-Event-Intervals"]], "Step 4: Creating Epochs from the Data": [[8, "Step-4:-Creating-Epochs-from-the-Data"]], "Step 5: Initializing the Epoch Class": [[8, "Step-5:-Initializing-the-Epoch-Class"]], "Step 6: Converting Epochs to NumPy Array": [[8, "Step-6:-Converting-Epochs-to-NumPy-Array"]], "Step 7: Averaging Across Epochs": [[8, "Step-7:-Averaging-Across-Epochs"]], "Step 8: Averaging Over Time": [[8, "Step-8:-Averaging-Over-Time"]], "Summary": [[13, "Summary"]], "Tutorial: Processing Eye-Tracking Data with PyNeon": [[8, null]], "Unequally sampled data": [[12, "Unequally-sampled-data"]], "Visualization module": [[7, null]], "Visualizing gaze heatmap": [[11, "Visualizing-gaze-heatmap"]], "Welcome to PyNeon documentation": [[0, null]]}, "docnames": ["index", "reference/data", "reference/dataset", "reference/export", "reference/index", "reference/preprocess", "reference/recording", "reference/vis", "tutorials/epoching", "tutorials/export_to_bids", "tutorials/index", "tutorials/read_recording", "tutorials/resample_and_concat", "tutorials/video"], "envversion": {"nbsphinx": 4, "sphinx": 63, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.intersphinx": 1}, "filenames": ["index.rst", "reference/data.rst", "reference/dataset.rst", "reference/export.rst", "reference/index.rst", "reference/preprocess.rst", "reference/recording.rst", "reference/vis.rst", "tutorials/epoching.ipynb", "tutorials/export_to_bids.ipynb", 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"yy_tracksi": [3, 6], "z": 11, "zeros_lik": 11}, "titles": ["Welcome to PyNeon documentation", "Classes for individual data types", "NeonDataset class", "Exportation module", "PyNeon API", "Preprocessing module", "NeonRecording class", "Visualization module", "Tutorial: Processing Eye-Tracking Data with PyNeon", "Export Neon data recording to BIDS formats", "PyNeon Tutorials", "Interpolate Data and Concatenate Channels", "Reading a Neon dataset/recording", "Mapping Scanpath to video"], "titleterms": {"1": [8, 13], "2": [8, 13], "3": [8, 13], "4": [8, 13], "5": [8, 13], "6": 8, "7": 8, "8": 8, "across": 8, "api": 4, "arrai": 8, "averag": 8, "bid": 9, "channel": 11, "class": [1, 2, 6, 8], "concaten": 11, "conclus": 8, "construct": 8, "convert": 8, "creat": 8, "data": [0, 1, 8, 9, 11, 12, 13], "dataset": 12, "differ": 11, "document": 0, "epoch": 8, "estim": 13, "event": [8, 12], "export": [3, 9], "ey": 8, "fixat": 13, "format": [0, 9], "frame": 13, "from": 8, "gaze": [12, 13], "heatmap": 12, "individu": 1, "initi": 8, "instal": 0, "interpol": 11, "interv": 8, "licens": 0, "load": [8, 13], "map": 13, "metadata": 12, "modul": [3, 5, 7], "neon": [9, 12, 13], "neondataset": 2, "neonrecord": [6, 12], "numpi": 8, "over": 8, "overlai": 13, "paramet": [5, 6], "preprocess": 5, "process": 8, "pyneon": [0, 4, 8, 10], "read": 12, "record": [9, 12, 13], "sampl": [8, 11, 12], "scanpath": 13, "setup": 13, "statu": 13, "step": 8, "stream": [11, 12], "summari": 13, "time": 8, "track": 8, "tutori": [8, 10], "type": 1, "understand": 13, "unequ": 11, "verifi": 8, "video": 13, "visual": [7, 12], "welcom": 0}}) \ No newline at end of file diff --git a/tutorials/export_to_bids.html b/tutorials/export_to_bids.html index 5d781c6..af491dd 100644 --- a/tutorials/export_to_bids.html +++ b/tutorials/export_to_bids.html @@ -311,7 +311,7 @@

      Section Navigation

      diff --git a/tutorials/index.html b/tutorials/index.html index b8b7478..37c4362 100644 --- a/tutorials/index.html +++ b/tutorials/index.html @@ -310,7 +310,7 @@

      Section Navigation

      @@ -374,7 +374,7 @@
      diff --git a/tutorials/resample_and_concat.html b/tutorials/interpolate_and_concat.html similarity index 81% rename from tutorials/resample_and_concat.html rename to tutorials/interpolate_and_concat.html index b9f9b89..24320f0 100644 --- a/tutorials/resample_and_concat.html +++ b/tutorials/interpolate_and_concat.html @@ -8,7 +8,7 @@ - Resample Data and Concatenate Channels — PyNeon dev documentation + Interpolate Data and Concatenate Channels — PyNeon dev documentation @@ -42,7 +42,7 @@ - + @@ -311,7 +311,7 @@

      Section Navigation

      @@ -354,7 +354,7 @@
    • PyNeon Tutorials
      • -
    • Resample...
      • +
    • Interpolate...

    @@ -371,9 +371,9 @@
    -
    -

    Resample Data and Concatenate Channels#

    -

    Informative as it is, raw Neon data is not always easy to work with. Different data streams (e.g., gaze, eye states, IMU) are sampled at different rates, don’t necessarily share a common start timestamp, and within each stream data might not have been sampled at a constant rate. This tutorial demonstrates how to deal with these issues by resampling data streams and concatenating them into a single DataFrame.

    +
    +

    Interpolate Data and Concatenate Channels#

    +

    Informative as it is, raw Neon data is not always easy to work with. Different data streams (e.g., gaze, eye states, IMU) are sampled at different rates, don’t necessarily share a common start timestamp, and within each stream data might not have been sampled at a constant rate. This tutorial demonstrates how to deal with these issues by interpolating data streams and concatenating them into a single DataFrame.

    We will use the same OfficeWalk dataset as in the previous tutorial.

    [1]:
@@ -407,20 +407,10 @@ 
    Unequally sampled data
    [3]:
    -
    # Get the data points
-gaze_ts = gaze.data["timestamp [ns]"].values
-eye_states_ts = eye_states.data["timestamp [ns]"].values
-imu_ts = imu.data["timestamp [ns]"].values
-
-# Calculate the distances between subsequent data points
-gaze_diff = np.diff(gaze_ts)
-eye_states_diff = np.diff(eye_states_ts)
-imu_diff = np.diff(imu_ts)
-
-# Unique values
-gaze_diff_unique = np.unique(gaze_diff)
-eye_states_diff_unique = np.unique(eye_states_diff)
-imu_diff_unique = np.unique(imu_diff)
+
    # Unique values
+gaze_diff_unique = np.unique(gaze.ts_diff)
+eye_states_diff_unique = np.unique(eye_states.ts_diff)
+imu_diff_unique = np.unique(imu.ts_diff)
 
 print(
     f"Unique gaze time differences: {len(gaze_diff_unique)}, max: {np.max(gaze_diff_unique)/1e6}ms"
@@ -455,15 +445,15 @@ 
    Unequally sampled datafig, axs = plt.subplots(3, 1, tight_layout=True)
 
-axs[0].hist(gaze_diff, bins=50)
+axs[0].hist(gaze.ts_diff, bins=50)
 axs[0].axvline(gaze_nominal_diff, color="red", label="Nominal")
 axs[0].set_title("Gaze")
 
-axs[1].hist(eye_states_diff, bins=50)
+axs[1].hist(eye_states.ts_diff, bins=50)
 axs[1].axvline(eye_states_nominal_diff, color="red", label="Nominal")
 axs[1].set_title("Eye states")
 
-axs[2].hist(imu_diff, bins=50)
+axs[2].hist(imu.ts_diff, bins=50)
 axs[2].axvline(imu_nominal_diff, color="red", label="Nominal")
 axs[2].set_title("IMU")
 
@@ -480,25 +470,21 @@ 
    Unequally sampled data
 
    
 
    
-../_images/tutorials_resample_and_concat_7_0.png
+../_images/tutorials_interpolate_and_concat_7_0.png
    -
    -

    Resampling data streams#

    -

    Given the unequal sampling, if we want to obtain continuous data streams, interpolation is necessary. PyNeon uses the scipy.interpolate.interp1d function to interpolate data streams. We can resample each stream by simply calling resample() which returns a new DataFrame with the resampled data.

    +
    +

    Interpolating data streams#

    +

    Given the unequal sampling, if we want to obtain continuous data streams, interpolation is necessary. PyNeon uses the scipy.interpolate.interp1d function to interpolate data streams. We can interpolate each stream by simply calling interpolate() which returns a copy of the object with the interpolated data.

    [5]:
 
    # Resample to the nominal sampling frequency
-gaze_resampled_data = gaze.interpolate()
-print(gaze_resampled_data.head())
+gaze_resampled = gaze.interpolate()
 
-ts = gaze_resampled_data["timestamp [ns]"].values
-ts_diffs = np.diff(ts)
-ts_diff_unique = np.unique(ts_diffs)
-print(f"Only one unique time difference: {np.unique(ts_diffs)}")
+print(f"Only one unique time difference: {np.unique(gaze_resampled.ts_diff)}")
    @@ -507,19 +493,6 @@

    Resampling data streams 
    -        timestamp [ns]  time [s]  gaze x [px]  gaze y [px]  worn  fixation id  \
-0  1725032224852161732     0.000  1067.486000   620.856000  True            1
-1  1725032224857161732     0.005  1066.920463   617.120061  True            1
-2  1725032224862161732     0.010  1072.699000   615.780000  True            1
-3  1725032224867161732     0.015  1067.447000   617.062000  True            1
-4  1725032224872161732     0.020  1071.564000   613.158000  True            1
-
-   blink id  azimuth [deg]  elevation [deg]
-0      <NA>      16.213030        -0.748998
-1      <NA>      16.176315        -0.511927
-2      <NA>      16.546413        -0.426618
-3      <NA>      16.210049        -0.508251
-4      <NA>      16.473521        -0.260388
 Only one unique time difference: [5000000]
    @@ -531,8 +504,10 @@

    Resampling data streams 
    print(f"Original gaze data length: {gaze.data.shape[0]}")
 print(f"Original IMU data length: {imu.data.shape[0]}")
-gaze_resampled_to_imu_data = gaze.interpolate(new_ts=imu.ts)
-print(f"Data length after resampling to IMU: {gaze_resampled_to_imu_data.shape[0]}")
+gaze_resampled_to_imu = gaze.interpolate(new_ts=imu.ts)
+print(
+    f"Gaze data length after resampling to IMU: {gaze_resampled_to_imu.data.shape[0]}"
+)
    @@ -543,7 +518,7 @@

    Resampling data streams
     Original gaze data length: 18769
 Original IMU data length: 10919
-Data length after resampling to IMU: 10919
+Gaze data length after resampling to IMU: 10919

    @@ -552,11 +527,11 @@

    Concatenating different streamsconcat_streams() provides such functionality. It takes a list of stream names and resamples them to common timestamps, defined by the latest start and earliest end timestamps of the streams. The news ampling frequency can either be directly specified or taken from the lowest/highest sampling frequency of the streams.

    In the following example, we will concatenate the gaze, eye states, and IMU streams into a single DataFrame using the default parameters (e.g., using the lowest sampling frequency of the streams).

    -
    [7]:
+
    [9]:
 
    
 
    
-
    concat_data = recording.concat_streams(["gaze", "eye_states", "imu"])
-print(concat_data.head())
+
    concat_stream = recording.concat_streams(["gaze", "eye_states", "imu"])
+print(concat_stream.data.head())
 
    
 
    
 
    
@@ -572,42 +547,49 @@ 
    Concatenating different streams
    start_time = 5
 end_time = 5.3
-start_ts = time_to_ts(start_time, concat_data)
-end_ts = time_to_ts(end_time, concat_data)
+start_ts = time_to_ts(start_time, concat_stream)
+end_ts = time_to_ts(end_time, concat_stream)
 
 raw_gaze_data_slice = gaze.data[
     (gaze.data["timestamp [ns]"] >= start_ts) & (gaze.data["timestamp [ns]"] <= end_ts)
@@ -630,9 +612,9 @@ 
    Concatenating different streamsraw_imu_data_slice = imu.data[
     (imu.data["timestamp [ns]"] >= start_ts) & (imu.data["timestamp [ns]"] <= end_ts)
 ]
-concat_data_slice = concat_data[
-    (concat_data["timestamp [ns]"] >= start_ts)
-    & (concat_data["timestamp [ns]"] <= end_ts)
+concat_data_slice = concat_stream[
+    (concat_stream["timestamp [ns]"] >= start_ts)
+    & (concat_stream["timestamp [ns]"] <= end_ts)
 ]
 
 # plot all data in the same scatter plot
@@ -682,7 +664,7 @@ 
    Concatenating different streams
 
    
 
    
-../_images/tutorials_resample_and_concat_15_1.png
+../_images/tutorials_interpolate_and_concat_15_1.png
 
    
 
    
 
    A linear interpolation allows us to estimate missing values. In the end, the concatenated dataframe combines all continuous data into one central location
    
@@ -721,7 +703,7 @@ 
    Concatenating different streams
    -../_images/tutorials_resample_and_concat_17_1.png +../_images/tutorials_interpolate_and_concat_17_1.png

    @@ -774,7 +756,7 @@

    Concatenating different streams @@ -782,7 +764,7 @@

    Concatenating different streams
    - + Show Source
    diff --git a/tutorials/resample_and_concat.ipynb b/tutorials/interpolate_and_concat.ipynb similarity index 96% rename from tutorials/resample_and_concat.ipynb rename to tutorials/interpolate_and_concat.ipynb index 3507830..6dd1a44 100644 --- a/tutorials/resample_and_concat.ipynb +++ b/tutorials/interpolate_and_concat.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Resample Data and Concatenate Channels\n", + "# Interpolate Data and Concatenate Channels\n", "\n", - "Informative as it is, raw Neon data is not always easy to work with. Different data streams (e.g., gaze, eye states, IMU) are sampled at different rates, don't necessarily share a common start timestamp, and within each stream data might not have been sampled at a constant rate. This tutorial demonstrates how to deal with these issues by resampling data streams and concatenating them into a single DataFrame.\n", + "Informative as it is, raw Neon data is not always easy to work with. Different data streams (e.g., gaze, eye states, IMU) are sampled at different rates, don't necessarily share a common start timestamp, and within each stream data might not have been sampled at a constant rate. This tutorial demonstrates how to deal with these issues by interpolating data streams and concatenating them into a single DataFrame.\n", "\n", "We will use the same ``OfficeWalk`` dataset as in the [previous tutorial](read_recording.ipynb)." ] @@ -69,20 +69,10 @@ } ], "source": [ - "# Get the data points\n", - "gaze_ts = gaze.data[\"timestamp [ns]\"].values\n", - "eye_states_ts = eye_states.data[\"timestamp [ns]\"].values\n", - "imu_ts = imu.data[\"timestamp [ns]\"].values\n", - "\n", - "# Calculate the distances between subsequent data points\n", - "gaze_diff = np.diff(gaze_ts)\n", - "eye_states_diff = np.diff(eye_states_ts)\n", - "imu_diff = np.diff(imu_ts)\n", - "\n", "# Unique values\n", - "gaze_diff_unique = np.unique(gaze_diff)\n", - "eye_states_diff_unique = np.unique(eye_states_diff)\n", - "imu_diff_unique = np.unique(imu_diff)\n", + "gaze_diff_unique = np.unique(gaze.ts_diff)\n", + "eye_states_diff_unique = np.unique(eye_states.ts_diff)\n", + "imu_diff_unique = np.unique(imu.ts_diff)\n", "\n", "print(\n", " f\"Unique gaze time differences: {len(gaze_diff_unique)}, max: {np.max(gaze_diff_unique)/1e6}ms\"\n", @@ -125,15 +115,15 @@ "\n", "fig, axs = plt.subplots(3, 1, tight_layout=True)\n", "\n", - "axs[0].hist(gaze_diff, bins=50)\n", + "axs[0].hist(gaze.ts_diff, bins=50)\n", "axs[0].axvline(gaze_nominal_diff, color=\"red\", label=\"Nominal\")\n", "axs[0].set_title(\"Gaze\")\n", "\n", - "axs[1].hist(eye_states_diff, bins=50)\n", + "axs[1].hist(eye_states.ts_diff, bins=50)\n", "axs[1].axvline(eye_states_nominal_diff, color=\"red\", label=\"Nominal\")\n", "axs[1].set_title(\"Eye states\")\n", "\n", - "axs[2].hist(imu_diff, bins=50)\n", + "axs[2].hist(imu.ts_diff, bins=50)\n", "axs[2].axvline(imu_nominal_diff, color=\"red\", label=\"Nominal\")\n", "axs[2].set_title(\"IMU\")\n", "\n", @@ -149,9 +139,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Resampling data streams\n", + "## Interpolating data streams\n", "\n", - "Given the unequal sampling, if we want to obtain continuous data streams, interpolation is necessary. PyNeon uses the `scipy.interpolate.interp1d` function to interpolate data streams. We can resample each stream by simply calling `resample()` which returns a new DataFrame with the resampled data." + "Given the unequal sampling, if we want to obtain continuous data streams, interpolation is necessary. PyNeon uses the `scipy.interpolate.interp1d` function to interpolate data streams. We can interpolate each stream by simply calling `interpolate()` which returns a copy of the object with the interpolated data." ] }, { @@ -163,32 +153,15 @@ "name": "stdout", "output_type": "stream", "text": [ - " timestamp [ns] time [s] gaze x [px] gaze y [px] worn fixation id \\\n", - "0 1725032224852161732 0.000 1067.486000 620.856000 True 1 \n", - "1 1725032224857161732 0.005 1066.920463 617.120061 True 1 \n", - "2 1725032224862161732 0.010 1072.699000 615.780000 True 1 \n", - "3 1725032224867161732 0.015 1067.447000 617.062000 True 1 \n", - "4 1725032224872161732 0.020 1071.564000 613.158000 True 1 \n", - "\n", - " blink id azimuth [deg] elevation [deg] \n", - "0 16.213030 -0.748998 \n", - "1 16.176315 -0.511927 \n", - "2 16.546413 -0.426618 \n", - "3 16.210049 -0.508251 \n", - "4 16.473521 -0.260388 \n", "Only one unique time difference: [5000000]\n" ] } ], "source": [ "# Resample to the nominal sampling frequency\n", - "gaze_resampled_data = gaze.interpolate()\n", - "print(gaze_resampled_data.head())\n", + "gaze_resampled = gaze.interpolate()\n", "\n", - "ts = gaze_resampled_data[\"timestamp [ns]\"].values\n", - "ts_diffs = np.diff(ts)\n", - "ts_diff_unique = np.unique(ts_diffs)\n", - "print(f\"Only one unique time difference: {np.unique(ts_diffs)}\")" + "print(f\"Only one unique time difference: {np.unique(gaze_resampled.ts_diff)}\")" ] }, { @@ -211,15 +184,17 @@ "text": [ "Original gaze data length: 18769\n", "Original IMU data length: 10919\n", - "Data length after resampling to IMU: 10919\n" + "Gaze data length after resampling to IMU: 10919\n" ] } ], "source": [ "print(f\"Original gaze data length: {gaze.data.shape[0]}\")\n", "print(f\"Original IMU data length: {imu.data.shape[0]}\")\n", - "gaze_resampled_to_imu_data = gaze.interpolate(new_ts=imu.ts)\n", - "print(f\"Data length after resampling to IMU: {gaze_resampled_to_imu_data.shape[0]}\")" + "gaze_resampled_to_imu = gaze.interpolate(new_ts=imu.ts)\n", + "print(\n", + " f\"Gaze data length after resampling to IMU: {gaze_resampled_to_imu.data.shape[0]}\"\n", + ")" ] }, { @@ -235,7 +210,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -249,48 +224,55 @@ "Using lowest sampling rate: 110 Hz (['imu'])\n", "Using latest start timestamp: 1725032224878547732 (['imu'])\n", "Using earliest last timestamp: 1725032319533909732 (['imu'])\n", - " timestamp [ns] time [s] gaze x [px] gaze y [px] worn fixation id \\\n", - "0 1725032224878547732 0.000000 1073.410354 611.095861 True 1 \n", - "1 1725032224887638641 0.009091 1069.801082 613.382535 True 1 \n", - "2 1725032224896729550 0.018182 1070.090109 613.439696 True 1 \n", - "3 1725032224905820459 0.027273 1069.891351 612.921757 True 1 \n", - "4 1725032224914911368 0.036364 1069.692588 612.403803 True 1 \n", + " gaze x [px] gaze y [px] worn fixation id blink id \\\n", + "1725032224878547732 1073.410354 611.095861 True 1 \n", + "1725032224887638641 1069.801082 613.382535 True 1 \n", + "1725032224896729550 1070.090109 613.439696 True 1 \n", + "1725032224905820459 1069.891351 612.921757 True 1 \n", + "1725032224914911368 1069.692588 612.403803 True 1 \n", + "\n", + " azimuth [deg] elevation [deg] pupil diameter left [mm] \\\n", + "1725032224878547732 16.591703 -0.129540 5.036588 \n", + "1725032224887638641 16.360605 -0.274666 5.093205 \n", + "1725032224896729550 16.379116 -0.278283 5.078107 \n", + "1725032224905820459 16.366379 -0.245426 5.077680 \n", + "1725032224914911368 16.353641 -0.212567 5.077253 \n", "\n", - " blink id azimuth [deg] elevation [deg] pupil diameter left [mm] ... \\\n", - "0 16.591703 -0.129540 5.036588 ... \n", - "1 16.360605 -0.274666 5.093205 ... \n", - "2 16.379116 -0.278283 5.078107 ... \n", - "3 16.366379 -0.245426 5.077680 ... \n", - "4 16.353641 -0.212567 5.077253 ... \n", + " pupil diameter right [mm] eyeball center left x [mm] \\\n", + "1725032224878547732 4.254749 -31.742046 \n", + "1725032224887638641 4.358431 -31.746807 \n", + "1725032224896729550 4.439704 -31.700505 \n", + "1725032224905820459 4.442760 -31.711353 \n", + "1725032224914911368 4.445815 -31.722201 \n", "\n", - " acceleration x [g] acceleration y [g] acceleration z [g] roll [deg] \\\n", - "0 0.063477 -0.058594 0.940430 -2.550682 \n", - "1 0.057486 -0.048916 0.942273 -2.602498 \n", - "2 0.051494 -0.039238 0.944117 -2.654314 \n", - "3 0.046721 -0.045800 0.944336 -2.699777 \n", - "4 0.042113 -0.054556 0.944336 -2.744383 \n", + " ... acceleration x [g] acceleration y [g] \\\n", + "1725032224878547732 ... 0.063477 -0.058594 \n", + "1725032224887638641 ... 0.057486 -0.048916 \n", + "1725032224896729550 ... 0.051494 -0.039238 \n", + "1725032224905820459 ... 0.046721 -0.045800 \n", + "1725032224914911368 ... 0.042113 -0.054556 \n", "\n", - " pitch [deg] yaw [deg] quaternion w quaternion x quaternion y \\\n", - "0 -4.461626 -172.335180 0.067636 -0.024792 0.037342 \n", - "1 -4.493445 -172.562393 0.065682 -0.025185 0.037639 \n", - "2 -4.525265 -172.789613 0.063728 -0.025579 0.037936 \n", - "3 -4.556964 -173.013398 0.061802 -0.025917 0.038238 \n", - "4 -4.588647 -173.236726 0.059879 -0.026247 0.038541 \n", + " acceleration z [g] roll [deg] pitch [deg] yaw [deg] \\\n", + "1725032224878547732 0.940430 -2.550682 -4.461626 -172.335180 \n", + "1725032224887638641 0.942273 -2.602498 -4.493445 -172.562393 \n", + "1725032224896729550 0.944117 -2.654314 -4.525265 -172.789613 \n", + "1725032224905820459 0.944336 -2.699777 -4.556964 -173.013398 \n", + "1725032224914911368 0.944336 -2.744383 -4.588647 -173.236726 \n", "\n", - " quaternion z \n", - "0 -0.996703 \n", - "1 -0.996810 \n", - "2 -0.996917 \n", - "3 -0.997017 \n", - "4 -0.997115 \n", + " quaternion w quaternion x quaternion y quaternion z \n", + "1725032224878547732 0.067636 -0.024792 0.037342 -0.996703 \n", + "1725032224887638641 0.065682 -0.025185 0.037639 -0.996810 \n", + "1725032224896729550 0.063728 -0.025579 0.037936 -0.996917 \n", + "1725032224905820459 0.061802 -0.025917 0.038238 -0.997017 \n", + "1725032224914911368 0.059879 -0.026247 0.038541 -0.997115 \n", "\n", - "[5 rows x 36 columns]\n" + "[5 rows x 34 columns]\n" ] } ], "source": [ - "concat_data = recording.concat_streams([\"gaze\", \"eye_states\", \"imu\"])\n", - "print(concat_data.head())" + "concat_stream = recording.concat_streams([\"gaze\", \"eye_states\", \"imu\"])\n", + "print(concat_stream.data.head())" ] }, { @@ -329,8 +311,8 @@ "source": [ "start_time = 5\n", "end_time = 5.3\n", - "start_ts = time_to_ts(start_time, concat_data)\n", - "end_ts = time_to_ts(end_time, concat_data)\n", + "start_ts = time_to_ts(start_time, concat_stream)\n", + "end_ts = time_to_ts(end_time, concat_stream)\n", "\n", "raw_gaze_data_slice = gaze.data[\n", " (gaze.data[\"timestamp [ns]\"] >= start_ts) & (gaze.data[\"timestamp [ns]\"] <= end_ts)\n", @@ -342,9 +324,9 @@ "raw_imu_data_slice = imu.data[\n", " (imu.data[\"timestamp [ns]\"] >= start_ts) & (imu.data[\"timestamp [ns]\"] <= end_ts)\n", "]\n", - "concat_data_slice = concat_data[\n", - " (concat_data[\"timestamp [ns]\"] >= start_ts)\n", - " & (concat_data[\"timestamp [ns]\"] <= end_ts)\n", + "concat_data_slice = concat_stream[\n", + " (concat_stream[\"timestamp [ns]\"] >= start_ts)\n", + " & (concat_stream[\"timestamp [ns]\"] <= end_ts)\n", "]\n", "\n", "# plot all data in the same scatter plot\n", @@ -448,7 +430,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.6" + "version": "3.12.4" } }, "nbformat": 4, diff --git a/tutorials/read_recording.html b/tutorials/read_recording.html index 862fa6e..b7a33cb 100644 --- a/tutorials/read_recording.html +++ b/tutorials/read_recording.html @@ -45,7 +45,7 @@ - + @@ -311,7 +311,7 @@

    Section Navigation

    @@ -484,19 +484,19 @@

    Data and metadata of a NeonRecording 
    -<pyneon.stream.NeonGaze object at 0x000002248D3B6240>
+<pyneon.stream.NeonGaze object at 0x00000216FFB5B800>
 None

    @@ -527,7 +527,7 @@

    Data and metadata of a NeonRecording 
    -C:\Users\qian.chu\Documents\GitHub\pyneon\pyneon\recording.py:275: UserWarning: Scene video not loaded because no video or video timestamps file was found.
+D:\GitHub\pyneon\pyneon\recording.py:275: UserWarning: Scene video not loaded because no video or video timestamps file was found.
   warnings.warn(
    @@ -546,26 +546,21 @@

    Data and metadata of a NeonRecording 
    -                                    timestamp [ns]  gaze x [px]  gaze y [px]  \
-2024-08-30 15:37:04.852161732  1725032224852161732     1067.486      620.856
-2024-08-30 15:37:04.857165732  1725032224857165732     1066.920      617.117
-2024-08-30 15:37:04.862161732  1725032224862161732     1072.699      615.780
-2024-08-30 15:37:04.867161732  1725032224867161732     1067.447      617.062
-2024-08-30 15:37:04.872161732  1725032224872161732     1071.564      613.158
-
-                               worn  fixation id  blink id  azimuth [deg]  \
-2024-08-30 15:37:04.852161732  True            1      <NA>      16.213030
-2024-08-30 15:37:04.857165732  True            1      <NA>      16.176285
-2024-08-30 15:37:04.862161732  True            1      <NA>      16.546413
-2024-08-30 15:37:04.867161732  True            1      <NA>      16.210049
-2024-08-30 15:37:04.872161732  True            1      <NA>      16.473521
-
-                               elevation [deg]  time [s]
-2024-08-30 15:37:04.852161732        -0.748998  0.000000
-2024-08-30 15:37:04.857165732        -0.511733  0.005004
-2024-08-30 15:37:04.862161732        -0.426618  0.010000
-2024-08-30 15:37:04.867161732        -0.508251  0.015000
-2024-08-30 15:37:04.872161732        -0.260388  0.020000
+                     gaze x [px]  gaze y [px]  worn  fixation id  blink id  \
+timestamp [ns]
+1725032224852161732     1067.486      620.856  True            1      <NA>
+1725032224857165732     1066.920      617.117  True            1      <NA>
+1725032224862161732     1072.699      615.780  True            1      <NA>
+1725032224867161732     1067.447      617.062  True            1      <NA>
+1725032224872161732     1071.564      613.158  True            1      <NA>
+
+                     azimuth [deg]  elevation [deg]
+timestamp [ns]
+1725032224852161732      16.213030        -0.748998
+1725032224857165732      16.176285        -0.511733
+1725032224862161732      16.546413        -0.426618
+1725032224867161732      16.210049        -0.508251
+1725032224872161732      16.473521        -0.260388

    PyNeon also automatically sets the column datatype to appropriate types, such as Int64 for timestamps, Int32 for event IDs, and float64 for float data.

    @@ -582,7 +577,6 @@

    Data and metadata of a NeonRecording 
    -timestamp [ns]       Int64
 gaze x [px]        float64
 gaze y [px]        float64
 worn                  bool
@@ -590,7 +584,6 @@ 
    Data and metadata of a NeonRecording#
    
 
    Up to this point, PyNeon simply reads and re-organizes the raw .csv files. Let’s plot some samples from the gaze and eye_states streams and a saccade from the saccades events.
    
 
    
-
    [9]:
+
    [12]:
 
    
 
    
 
    import matplotlib.pyplot as plt
@@ -619,11 +612,10 @@ 
    Data streams and events# Visualize the 2nd saccade
 saccade = saccades.data.iloc[1]
-ax.axvspan(
-    saccade["start timestamp [ns]"], saccade["end timestamp [ns]"], color="lightgray"
-)
+print(saccade)
+ax.axvspan(saccade.index.values, saccade["end timestamp [ns]"], color="lightgray")
 ax.text(
-    (saccade["start timestamp [ns]"] + saccade["end timestamp [ns]"]) / 2,
+    (saccade.index.values + saccade["end timestamp [ns]"]) / 2,
     1050,
     "Saccade",
     horizontalalignment="center",
@@ -633,14 +625,14 @@ 
    Data streams and eventssns.scatterplot(
     ax=ax,
     data=gaze.data.head(100),
-    x="timestamp [ns]",
+    x=gaze.data.index,
     y="gaze x [px]",
     color=gaze_color,
 )
 sns.scatterplot(
     ax=ax2,
     data=imu.data.head(60),
-    x="timestamp [ns]",
+    x=imu.data.index,
     y="gyro x [deg/s]",
     color=gyro_color,
 )
@@ -648,19 +640,92 @@ 
    Data streams and events
 
    
 
    
-
    [9]:
-
    
+
    
+
    
+
    
+
    +saccade id                                2.0
+end timestamp [ns]      1725032225347526656.0
+duration [ms]                            65.0
+amplitude [px]                      228.36139
+amplitude [deg]                     14.676102
+mean velocity [px/s]              3685.269894
+peak velocity [px/s]              5411.775481
+Name: 1725032225282527732, dtype: Float64
+
    
+
    
+
    
+
    
 
    
 
    
 
    -<Axes: xlabel='timestamp [ns]', ylabel='gyro x [deg/s]'>
+---------------------------------------------------------------------------
+TypeError                                 Traceback (most recent call last)
+Cell In[12], line 19
+     17 saccade = saccades.data.iloc[1]
+     18 print(saccade)
+---> 19 ax.axvspan(
+     20     saccade.index.to_numpy(), saccade["end timestamp [ns]"], color="lightgray"
+     21 )
+     22 ax.text(
+     23     (saccade.index.to_numpy() + saccade["end timestamp [ns]"]) / 2,
+     24     1050,
+     25     "Saccade",
+     26     horizontalalignment="center",
+     27 )
+     29 # Visualize gaze x and pupil diameter left
+
+File c:\Users\QianC\.conda\envs\pyneon\Lib\site-packages\matplotlib\axes\_axes.py:1087, in Axes.axvspan(self, xmin, xmax, ymin, ymax, **kwargs)
+   1085 # Strip units away.
+   1086 self._check_no_units([ymin, ymax], ['ymin', 'ymax'])
+-> 1087 (xmin, xmax), = self._process_unit_info([("x", [xmin, xmax])], kwargs)
+   1089 p = mpatches.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, **kwargs)
+   1090 p.set_transform(self.get_xaxis_transform(which="grid"))
+
+File c:\Users\QianC\.conda\envs\pyneon\Lib\site-packages\matplotlib\axes\_base.py:2585, in _AxesBase._process_unit_info(self, datasets, kwargs, convert)
+   2583     # Update from data if axis is already set but no unit is set yet.
+   2584     if axis is not None and data is not None and not axis.have_units():
+-> 2585         axis.update_units(data)
+   2586 for axis_name, axis in axis_map.items():
+   2587     # Return if no axis is set.
+   2588     if axis is None:
+
+File c:\Users\QianC\.conda\envs\pyneon\Lib\site-packages\matplotlib\axis.py:1756, in Axis.update_units(self, data)
+   1754 neednew = self.converter != converter
+   1755 self.converter = converter
+-> 1756 default = self.converter.default_units(data, self)
+   1757 if default is not None and self.units is None:
+   1758     self.set_units(default)
+
+File c:\Users\QianC\.conda\envs\pyneon\Lib\site-packages\matplotlib\category.py:105, in StrCategoryConverter.default_units(data, axis)
+    103 # the conversion call stack is default_units -> axis_info -> convert
+    104 if axis.units is None:
+--> 105     axis.set_units(UnitData(data))
+    106 else:
+    107     axis.units.update(data)
+
+File c:\Users\QianC\.conda\envs\pyneon\Lib\site-packages\matplotlib\category.py:181, in UnitData.__init__(self, data)
+    179 self._counter = itertools.count()
+    180 if data is not None:
+--> 181     self.update(data)
+
+File c:\Users\QianC\.conda\envs\pyneon\Lib\site-packages\matplotlib\category.py:214, in UnitData.update(self, data)
+    212 # check if convertible to number:
+    213 convertible = True
+--> 214 for val in OrderedDict.fromkeys(data):
+    215     # OrderedDict just iterates over unique values in data.
+    216     _api.check_isinstance((str, bytes), value=val)
+    217     if convertible:
+    218         # this will only be called so long as convertible is True.
+
+TypeError: unhashable type: 'numpy.ndarray'
 
    
 
    
 
    
 
    
 
    
 
    
-../_images/tutorials_read_recording_17_1.png
+../_images/tutorials_read_recording_17_2.png
 
    
 
    
 
    It’s apparent that at the beginning of the recording, there are some missing data points in both the gaze and imu streams. This is presumably due to the time it takes for the sensors to start up and stabilize. We will show how to handle missing data using resampling in the next tutorial. For now, it’s important to be aware of these gaps and that it will require great caution to assume the data is continuously and equally sampled.
    
@@ -751,11 +816,11 @@ 
    Visualizing gaze heatmap
       
    
         
    next
    
-        
    Resample Data and Concatenate Channels
    
+        
    Interpolate Data and Concatenate Channels
    
       
    
       
         
diff --git a/tutorials/read_recording.ipynb b/tutorials/read_recording.ipynb
index 19644ff..9568f42 100644
--- a/tutorials/read_recording.ipynb
+++ b/tutorials/read_recording.ipynb
@@ -143,19 +143,19 @@
       "Wearer ID: bcff2832-cfcb-4f89-abef-7bbfe91ec561\n",
       "Wearer name: Qian\n",
       "Recording start time: 2024-08-30 17:37:01.527000\n",
-      "Recording duration: 98.213 s\n",
-      "                  exist              filename                                                                                                           path\n",
-      "3d_eye_states      True     3d_eye_states.csv     C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\3d_eye_states.csv\n",
-      "blinks             True            blinks.csv            C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\blinks.csv\n",
-      "events             True            events.csv            C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\events.csv\n",
-      "fixations          True         fixations.csv         C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\fixations.csv\n",
-      "gaze               True              gaze.csv              C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\gaze.csv\n",
-      "imu                True               imu.csv               C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\imu.csv\n",
-      "labels             True            labels.csv            C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\labels.csv\n",
-      "saccades           True          saccades.csv          C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\saccades.csv\n",
-      "world_timestamps   True  world_timestamps.csv  C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\world_timestamps.csv\n",
-      "scene_video_info  False                  None                                                                                                           None\n",
-      "scene_video       False                  None                                                                                                           None\n",
+      "Recording duration: 98.213s\n",
+      "                  exist              filename                                                                                  path\n",
+      "3d_eye_states      True     3d_eye_states.csv     D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\3d_eye_states.csv\n",
+      "blinks             True            blinks.csv            D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\blinks.csv\n",
+      "events             True            events.csv            D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\events.csv\n",
+      "fixations          True         fixations.csv         D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\fixations.csv\n",
+      "gaze               True              gaze.csv              D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\gaze.csv\n",
+      "imu                True               imu.csv               D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\imu.csv\n",
+      "labels             True            labels.csv            D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\labels.csv\n",
+      "saccades           True          saccades.csv          D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\saccades.csv\n",
+      "world_timestamps   True  world_timestamps.csv  D:\\GitHub\\pyneon\\data\\OfficeWalk\\Timeseries Data\\walk1-e116e606\\world_timestamps.csv\n",
+      "scene_video_info  False                  None                                                                                  None\n",
+      "scene_video       False                  None                                                                                  None\n",
       "\n"
      ]
     }
@@ -182,7 +182,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
+      "\n",
       "None\n"
      ]
     },
@@ -190,7 +190,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\qian.chu\\Documents\\GitHub\\pyneon\\pyneon\\recording.py:275: UserWarning: Scene video not loaded because no video or video timestamps file was found.\n",
+      "D:\\GitHub\\pyneon\\pyneon\\recording.py:275: UserWarning: Scene video not loaded because no video or video timestamps file was found.\n",
       "  warnings.warn(\n"
      ]
     }
@@ -220,26 +220,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                                    timestamp [ns]  gaze x [px]  gaze y [px]  \\\n",
-      "2024-08-30 15:37:04.852161732  1725032224852161732     1067.486      620.856   \n",
-      "2024-08-30 15:37:04.857165732  1725032224857165732     1066.920      617.117   \n",
-      "2024-08-30 15:37:04.862161732  1725032224862161732     1072.699      615.780   \n",
-      "2024-08-30 15:37:04.867161732  1725032224867161732     1067.447      617.062   \n",
-      "2024-08-30 15:37:04.872161732  1725032224872161732     1071.564      613.158   \n",
-      "\n",
-      "                               worn  fixation id  blink id  azimuth [deg]  \\\n",
-      "2024-08-30 15:37:04.852161732  True            1            16.213030   \n",
-      "2024-08-30 15:37:04.857165732  True            1            16.176285   \n",
-      "2024-08-30 15:37:04.862161732  True            1            16.546413   \n",
-      "2024-08-30 15:37:04.867161732  True            1            16.210049   \n",
-      "2024-08-30 15:37:04.872161732  True            1            16.473521   \n",
+      "                     gaze x [px]  gaze y [px]  worn  fixation id  blink id  \\\n",
+      "timestamp [ns]                                                               \n",
+      "1725032224852161732     1067.486      620.856  True            1         \n",
+      "1725032224857165732     1066.920      617.117  True            1         \n",
+      "1725032224862161732     1072.699      615.780  True            1         \n",
+      "1725032224867161732     1067.447      617.062  True            1         \n",
+      "1725032224872161732     1071.564      613.158  True            1         \n",
       "\n",
-      "                               elevation [deg]  time [s]  \n",
-      "2024-08-30 15:37:04.852161732        -0.748998  0.000000  \n",
-      "2024-08-30 15:37:04.857165732        -0.511733  0.005004  \n",
-      "2024-08-30 15:37:04.862161732        -0.426618  0.010000  \n",
-      "2024-08-30 15:37:04.867161732        -0.508251  0.015000  \n",
-      "2024-08-30 15:37:04.872161732        -0.260388  0.020000  \n"
+      "                     azimuth [deg]  elevation [deg]  \n",
+      "timestamp [ns]                                       \n",
+      "1725032224852161732      16.213030        -0.748998  \n",
+      "1725032224857165732      16.176285        -0.511733  \n",
+      "1725032224862161732      16.546413        -0.426618  \n",
+      "1725032224867161732      16.210049        -0.508251  \n",
+      "1725032224872161732      16.473521        -0.260388  \n"
      ]
     }
    ],
@@ -263,7 +258,6 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "timestamp [ns]       Int64\n",
       "gaze x [px]        float64\n",
       "gaze y [px]        float64\n",
       "worn                  bool\n",
@@ -271,7 +265,6 @@
       "blink id             Int32\n",
       "azimuth [deg]      float64\n",
       "elevation [deg]    float64\n",
-      "time [s]           float64\n",
       "dtype: object\n"
      ]
     }
@@ -291,22 +284,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       ""
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "saccade id                                2.0\n",
+      "end timestamp [ns]      1725032225347526656.0\n",
+      "duration [ms]                            65.0\n",
+      "amplitude [px]                      228.36139\n",
+      "amplitude [deg]                     14.676102\n",
+      "mean velocity [px/s]              3685.269894\n",
+      "peak velocity [px/s]              5411.775481\n",
+      "Name: 1725032225282527732, dtype: Float64\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "unhashable type: 'numpy.ndarray'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[12], line 19\u001b[0m\n\u001b[0;32m     17\u001b[0m saccade \u001b[38;5;241m=\u001b[39m saccades\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39miloc[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m     18\u001b[0m \u001b[38;5;28mprint\u001b[39m(saccade)\n\u001b[1;32m---> 19\u001b[0m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maxvspan\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m     20\u001b[0m \u001b[43m    \u001b[49m\u001b[43msaccade\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_numpy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msaccade\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mend timestamp [ns]\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mlightgray\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\n\u001b[0;32m     21\u001b[0m \u001b[43m)\u001b[49m\n\u001b[0;32m     22\u001b[0m ax\u001b[38;5;241m.\u001b[39mtext(\n\u001b[0;32m     23\u001b[0m     (saccade\u001b[38;5;241m.\u001b[39mindex\u001b[38;5;241m.\u001b[39mto_numpy() \u001b[38;5;241m+\u001b[39m saccade[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mend timestamp [ns]\u001b[39m\u001b[38;5;124m\"\u001b[39m]) \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m,\n\u001b[0;32m     24\u001b[0m     \u001b[38;5;241m1050\u001b[39m,\n\u001b[0;32m     25\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSaccade\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     26\u001b[0m     horizontalalignment\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m     27\u001b[0m )\n\u001b[0;32m     29\u001b[0m \u001b[38;5;66;03m# Visualize gaze x and pupil diameter left\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axes\\_axes.py:1087\u001b[0m, in \u001b[0;36mAxes.axvspan\u001b[1;34m(self, xmin, xmax, ymin, ymax, **kwargs)\u001b[0m\n\u001b[0;32m   1085\u001b[0m \u001b[38;5;66;03m# Strip units away.\u001b[39;00m\n\u001b[0;32m   1086\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_no_units([ymin, ymax], [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mymin\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mymax\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m-> 1087\u001b[0m (xmin, xmax), \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_unit_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mx\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43mxmin\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mxmax\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1089\u001b[0m p \u001b[38;5;241m=\u001b[39m mpatches\u001b[38;5;241m.\u001b[39mRectangle((xmin, ymin), xmax \u001b[38;5;241m-\u001b[39m xmin, ymax \u001b[38;5;241m-\u001b[39m ymin, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1090\u001b[0m p\u001b[38;5;241m.\u001b[39mset_transform(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_xaxis_transform(which\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgrid\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n",
+      "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axes\\_base.py:2585\u001b[0m, in \u001b[0;36m_AxesBase._process_unit_info\u001b[1;34m(self, datasets, kwargs, convert)\u001b[0m\n\u001b[0;32m   2583\u001b[0m     \u001b[38;5;66;03m# Update from data if axis is already set but no unit is set yet.\u001b[39;00m\n\u001b[0;32m   2584\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m axis\u001b[38;5;241m.\u001b[39mhave_units():\n\u001b[1;32m-> 2585\u001b[0m         \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2586\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis_name, axis \u001b[38;5;129;01min\u001b[39;00m axis_map\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m   2587\u001b[0m     \u001b[38;5;66;03m# Return if no axis is set.\u001b[39;00m\n\u001b[0;32m   2588\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m axis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\axis.py:1756\u001b[0m, in \u001b[0;36mAxis.update_units\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m   1754\u001b[0m neednew \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m!=\u001b[39m converter\n\u001b[0;32m   1755\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter \u001b[38;5;241m=\u001b[39m converter\n\u001b[1;32m-> 1756\u001b[0m default \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdefault_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1757\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m default \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1758\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_units(default)\n",
+      "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:105\u001b[0m, in \u001b[0;36mStrCategoryConverter.default_units\u001b[1;34m(data, axis)\u001b[0m\n\u001b[0;32m    103\u001b[0m \u001b[38;5;66;03m# the conversion call stack is default_units -> axis_info -> convert\u001b[39;00m\n\u001b[0;32m    104\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m axis\u001b[38;5;241m.\u001b[39munits \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 105\u001b[0m     axis\u001b[38;5;241m.\u001b[39mset_units(\u001b[43mUnitData\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[0;32m    106\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    107\u001b[0m     axis\u001b[38;5;241m.\u001b[39munits\u001b[38;5;241m.\u001b[39mupdate(data)\n",
+      "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:181\u001b[0m, in \u001b[0;36mUnitData.__init__\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    179\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_counter \u001b[38;5;241m=\u001b[39m itertools\u001b[38;5;241m.\u001b[39mcount()\n\u001b[0;32m    180\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m--> 181\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\QianC\\.conda\\envs\\pyneon\\Lib\\site-packages\\matplotlib\\category.py:214\u001b[0m, in \u001b[0;36mUnitData.update\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    212\u001b[0m \u001b[38;5;66;03m# check if convertible to number:\u001b[39;00m\n\u001b[0;32m    213\u001b[0m convertible \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m--> 214\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m val \u001b[38;5;129;01min\u001b[39;00m \u001b[43mOrderedDict\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfromkeys\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m    215\u001b[0m     \u001b[38;5;66;03m# OrderedDict just iterates over unique values in data.\u001b[39;00m\n\u001b[0;32m    216\u001b[0m     _api\u001b[38;5;241m.\u001b[39mcheck_isinstance((\u001b[38;5;28mstr\u001b[39m, \u001b[38;5;28mbytes\u001b[39m), value\u001b[38;5;241m=\u001b[39mval)\n\u001b[0;32m    217\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m convertible:\n\u001b[0;32m    218\u001b[0m         \u001b[38;5;66;03m# this will only be called so long as convertible is True.\u001b[39;00m\n",
+      "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'numpy.ndarray'"
+     ]
     },
     {
      "data": {
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",
+      "image/png": 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",
       "text/plain": [
        "
    "
       ]
@@ -333,11 +347,10 @@
     "\n",
     "# Visualize the 2nd saccade\n",
     "saccade = saccades.data.iloc[1]\n",
-    "ax.axvspan(\n",
-    "    saccade[\"start timestamp [ns]\"], saccade[\"end timestamp [ns]\"], color=\"lightgray\"\n",
-    ")\n",
+    "print(saccade)\n",
+    "ax.axvspan(saccade.index.values, saccade[\"end timestamp [ns]\"], color=\"lightgray\")\n",
     "ax.text(\n",
-    "    (saccade[\"start timestamp [ns]\"] + saccade[\"end timestamp [ns]\"]) / 2,\n",
+    "    (saccade.index.values + saccade[\"end timestamp [ns]\"]) / 2,\n",
     "    1050,\n",
     "    \"Saccade\",\n",
     "    horizontalalignment=\"center\",\n",
@@ -347,14 +360,14 @@
     "sns.scatterplot(\n",
     "    ax=ax,\n",
     "    data=gaze.data.head(100),\n",
-    "    x=\"timestamp [ns]\",\n",
+    "    x=gaze.data.index,\n",
     "    y=\"gaze x [px]\",\n",
     "    color=gaze_color,\n",
     ")\n",
     "sns.scatterplot(\n",
     "    ax=ax2,\n",
     "    data=imu.data.head(60),\n",
-    "    x=\"timestamp [ns]\",\n",
+    "    x=imu.data.index,\n",
     "    y=\"gyro x [deg/s]\",\n",
     "    color=gyro_color,\n",
     ")"
@@ -465,7 +478,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.6"
+   "version": "3.12.4"
   }
  },
  "nbformat": 4,
diff --git a/tutorials/video.html b/tutorials/video.html
index caa7cea..72987c7 100644
--- a/tutorials/video.html
+++ b/tutorials/video.html
@@ -46,7 +46,7 @@
     
     
     
-    
+    
   
   
   
@@ -311,7 +311,7 @@
   
    Section Navigation
    
   
    
@@ -611,12 +611,12 @@ 
    Summary#<
                   
 
    
     
       
       
    
         
    previous
    
-        
    Resample Data and Concatenate Channels
    
+        
    Interpolate Data and Concatenate Channels