Parameters description for [PHONE_LOCATIONS][PROVIDERS][BARNETT]:
Parameters description for [PHONE_LOCATIONS][PROVIDERS][DORYAB]:
| movingtostaticratio | - | -Ratio between the number of rows labeled Moving versus Static | +Ratio between stationary time and total location sensed time. A lat/long coordinate pair is labelled as stationary if it’s speed (distance/time) to the next coordinate pair is less than 1km/hr. A higher value represents a more stationary routine. These times are computed by multiplying the number of rows by [SAMPLING_FREQUENCY] |
||||||
| outlierstimepercent | - | -Ratio between the number of rows that belong to non-significant clusters divided by the total number of rows in a time segment. | +Ratio between the time spent in non-significant clusters divided by the time spent in all clusters (total location sensed time). A higher value represents more time spent in non-significant clusters. These times are computed by multiplying the number of rows by [SAMPLING_FREQUENCY] |
||||||
| maxlengthstayatclusters | diff --git a/0.3/search/search_index.json b/0.3/search/search_index.json index 603a13a9..b16ee4db 100644 --- a/0.3/search/search_index.json +++ b/0.3/search/search_index.json @@ -1 +1 @@ -{"config":{"lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Welcome to RAPIDS documentation \u00b6 Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to extract and create behavioral features (a.k.a. digital biomarkers), visualize mobile sensor data and structure your analysis into reproducible workflows. RAPIDS is open source, documented, modular, tested, and reproducible. At the moment we support smartphone data collected with AWARE and wearable data from Fitbit devices. Tip Questions or feedback can be posted on the #rapids channel in AWARE Framework's slack . Bugs and feature requests should be posted on Github . Join our discussions on our algorithms and assumptions for feature processing . Ready to start? Go to Installation , then to Configuration , and then to Execution Are you upgrading from RAPIDS beta ? Follow this guide How does it work? \u00b6 RAPIDS is formed by R and Python scripts orchestrated by Snakemake . We suggest you read Snakemake\u2019s docs but in short: every link in the analysis chain is atomic and has files as input and output. Behavioral features are processed per sensor and per participant. What are the benefits of using RAPIDS? \u00b6 Consistent analysis . Every participant sensor dataset is analyzed in the exact same way and isolated from each other. Efficient analysis . Every analysis step is executed only once. Whenever your data or configuration changes only the affected files are updated. Parallel execution . Thanks to Snakemake, your analysis can be executed over multiple cores without changing your code. Code-free features . Extract any of the behavioral features offered by RAPIDS without writing any code. Extensible code . You can easily add your own behavioral features in R or Python, share them with the community, and keep authorship and citations. Timezone aware . Your data is adjusted to the specified timezone (multiple timezones suport coming soon ). Flexible time segments . You can extract behavioral features on time windows of any length (e.g. 5 minutes, 3 hours, 2 days), on every day or particular days (e.g. weekends, Mondays, the 1 st of each month, etc.) or around events of interest (e.g. surveys or clinical relapses). Tested code . We are constantly adding tests to make sure our behavioral features are correct. Reproducible code . If you structure your analysis within RAPIDS, you can be sure your code will run in other computers as intended thanks to R and Python virtual environments. You can share your analysis code along your publications without any overhead. Private . All your data is processed locally. How is it organized? \u00b6 In broad terms the config.yaml , .env file , participants files , and time segment files are the only ones that you will have to modify. All data is stored in data/ and all scripts are stored in src/ . For more information see RAPIDS\u2019 File Structure .","title":"Home"},{"location":"#welcome-to-rapids-documentation","text":"Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to extract and create behavioral features (a.k.a. digital biomarkers), visualize mobile sensor data and structure your analysis into reproducible workflows. RAPIDS is open source, documented, modular, tested, and reproducible. At the moment we support smartphone data collected with AWARE and wearable data from Fitbit devices. Tip Questions or feedback can be posted on the #rapids channel in AWARE Framework's slack . Bugs and feature requests should be posted on Github . Join our discussions on our algorithms and assumptions for feature processing . Ready to start? Go to Installation , then to Configuration , and then to Execution Are you upgrading from RAPIDS beta ? Follow this guide","title":"Welcome to RAPIDS documentation"},{"location":"#how-does-it-work","text":"RAPIDS is formed by R and Python scripts orchestrated by Snakemake . We suggest you read Snakemake\u2019s docs but in short: every link in the analysis chain is atomic and has files as input and output. Behavioral features are processed per sensor and per participant.","title":"How does it work?"},{"location":"#what-are-the-benefits-of-using-rapids","text":"Consistent analysis . Every participant sensor dataset is analyzed in the exact same way and isolated from each other. Efficient analysis . Every analysis step is executed only once. Whenever your data or configuration changes only the affected files are updated. Parallel execution . Thanks to Snakemake, your analysis can be executed over multiple cores without changing your code. Code-free features . Extract any of the behavioral features offered by RAPIDS without writing any code. Extensible code . You can easily add your own behavioral features in R or Python, share them with the community, and keep authorship and citations. Timezone aware . Your data is adjusted to the specified timezone (multiple timezones suport coming soon ). Flexible time segments . You can extract behavioral features on time windows of any length (e.g. 5 minutes, 3 hours, 2 days), on every day or particular days (e.g. weekends, Mondays, the 1 st of each month, etc.) or around events of interest (e.g. surveys or clinical relapses). Tested code . We are constantly adding tests to make sure our behavioral features are correct. Reproducible code . If you structure your analysis within RAPIDS, you can be sure your code will run in other computers as intended thanks to R and Python virtual environments. You can share your analysis code along your publications without any overhead. Private . All your data is processed locally.","title":"What are the benefits of using RAPIDS?"},{"location":"#how-is-it-organized","text":"In broad terms the config.yaml , .env file , participants files , and time segment files are the only ones that you will have to modify. All data is stored in data/ and all scripts are stored in src/ . For more information see RAPIDS\u2019 File Structure .","title":"How is it organized?"},{"location":"change-log/","text":"Change Log \u00b6 v0.3.1 \u00b6 Update installation docs for RAPIDS\u2019 docker container Fix example analysis use of accelerometer data in a plot Update FAQ Update minimal example documentation Minor doc updates v0.3.0 \u00b6 Update R and Python virtual environments Add GH actions CI support for tests and docker Add release and test badges to README v0.2.6 \u00b6 Fix old versions banner on nested pages v0.2.5 \u00b6 Fix docs deploy typo v0.2.4 \u00b6 Fix broken links in landing page and docs deploy v0.2.3 \u00b6 Fix participant IDS in the example analysis workflow v0.2.2 \u00b6 Fix readme link to docs v0.2.1 \u00b6 FIx link to the most recent version in the old version banner v0.2.0 \u00b6 Add new PHONE_BLUETOOTH DORYAB provider Deprecate PHONE_BLUETOOTH RAPIDS provider Fix bug in filter_data_by_segment for Python when dataset was empty Minor doc updates New FAQ item v0.1.0 \u00b6 New and more consistent docs (this website). The previous docs are marked as beta Consolidate configuration instructions Flexible time segments Simplify Fitbit behavioral feature extraction and documentation Sensor\u2019s configuration and output is more consistent Update visualizations to handle flexible day segments Create a RAPIDS execution script that allows re-computation of the pipeline after configuration changes Add citation guide Update virtual environment guide Update analysis workflow example Add a Code of Conduct Update Team page","title":"Change Log"},{"location":"change-log/#change-log","text":"","title":"Change Log"},{"location":"change-log/#v031","text":"Update installation docs for RAPIDS\u2019 docker container Fix example analysis use of accelerometer data in a plot Update FAQ Update minimal example documentation Minor doc updates","title":"v0.3.1"},{"location":"change-log/#v030","text":"Update R and Python virtual environments Add GH actions CI support for tests and docker Add release and test badges to README","title":"v0.3.0"},{"location":"change-log/#v026","text":"Fix old versions banner on nested pages","title":"v0.2.6"},{"location":"change-log/#v025","text":"Fix docs deploy typo","title":"v0.2.5"},{"location":"change-log/#v024","text":"Fix broken links in landing page and docs deploy","title":"v0.2.4"},{"location":"change-log/#v023","text":"Fix participant IDS in the example analysis workflow","title":"v0.2.3"},{"location":"change-log/#v022","text":"Fix readme link to docs","title":"v0.2.2"},{"location":"change-log/#v021","text":"FIx link to the most recent version in the old version banner","title":"v0.2.1"},{"location":"change-log/#v020","text":"Add new PHONE_BLUETOOTH DORYAB provider Deprecate PHONE_BLUETOOTH RAPIDS provider Fix bug in filter_data_by_segment for Python when dataset was empty Minor doc updates New FAQ item","title":"v0.2.0"},{"location":"change-log/#v010","text":"New and more consistent docs (this website). The previous docs are marked as beta Consolidate configuration instructions Flexible time segments Simplify Fitbit behavioral feature extraction and documentation Sensor\u2019s configuration and output is more consistent Update visualizations to handle flexible day segments Create a RAPIDS execution script that allows re-computation of the pipeline after configuration changes Add citation guide Update virtual environment guide Update analysis workflow example Add a Code of Conduct Update Team page","title":"v0.1.0"},{"location":"citation/","text":"Cite RAPIDS and providers \u00b6 RAPIDS and the community RAPIDS is a community effort and as such we want to continue recognizing the contributions from other researchers. Besides citing RAPIDS, we ask you to cite any of the authors listed below if you used those sensor providers in your analysis, thank you! RAPIDS \u00b6 If you used RAPIDS, please cite this paper . RAPIDS et al. citation Vega J, Li M, Aguillera K, Goel N, Joshi E, Durica KC, Kunta AR, Low CA RAPIDS: Reproducible Analysis Pipeline for Data Streams Collected with Mobile Devices JMIR Preprints. 18/08/2020:23246 DOI: 10.2196/preprints.23246 URL: https://preprints.jmir.org/preprint/23246 Panda (accelerometer) \u00b6 If you computed accelerometer features using the provider [PHONE_ACCLEROMETER][PANDA] cite this paper in addition to RAPIDS. Panda et al. citation Panda N, Solsky I, Huang EJ, Lipsitz S, Pradarelli JC, Delisle M, Cusack JC, Gadd MA, Lubitz CC, Mullen JT, Qadan M, Smith BL, Specht M, Stephen AE, Tanabe KK, Gawande AA, Onnela JP, Haynes AB. Using Smartphones to Capture Novel Recovery Metrics After Cancer Surgery. JAMA Surg. 2020 Feb 1;155(2):123-129. doi: 10.1001/jamasurg.2019.4702. PMID: 31657854; PMCID: PMC6820047. Stachl (applications foreground) \u00b6 If you computed applications foreground features using the app category (genre) catalogue in [PHONE_APPLICATIONS_FOREGROUND][RAPIDS] cite this paper in addition to RAPIDS. Stachl et al. citation Clemens Stachl, Quay Au, Ramona Schoedel, Samuel D. Gosling, Gabriella M. Harari, Daniel Buschek, Sarah Theres V\u00f6lkel, Tobias Schuwerk, Michelle Oldemeier, Theresa Ullmann, Heinrich Hussmann, Bernd Bischl, Markus B\u00fchner. Proceedings of the National Academy of Sciences Jul 2020, 117 (30) 17680-17687; DOI: 10.1073/pnas.1920484117 Doryab (bluetooth) \u00b6 If you computed bluetooth features using the provider [PHONE_BLUETOOTH][DORYAB] cite this paper in addition to RAPIDS. Doryab et al. citation Doryab, A., Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral Features from Smartphone and Wearable Data. ArXiv:1812.10394 [Cs, Stat]. http://arxiv.org/abs/1812.10394 Barnett (locations) \u00b6 If you computed locations features using the provider [PHONE_LOCATIONS][BARNETT] cite this paper and this paper in addition to RAPIDS. Barnett et al. citation Ian Barnett, Jukka-Pekka Onnela, Inferring mobility measures from GPS traces with missing data, Biostatistics, Volume 21, Issue 2, April 2020, Pages e98\u2013e112, https://doi.org/10.1093/biostatistics/kxy059 Canzian et al. citation Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp \u201815). Association for Computing Machinery, New York, NY, USA, 1293\u20131304. DOI: https://doi.org/10.1145/2750858.2805845 Doryab (locations) \u00b6 If you computed locations features using the provider [PHONE_LOCATIONS][DORYAB] cite this paper and this paper in addition to RAPIDS. Doryab et al. citation Doryab, A., Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral Features from Smartphone and Wearable Data. ArXiv:1812.10394 [Cs, Stat]. http://arxiv.org/abs/1812.10394 Canzian et al. citation Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp \u201815). Association for Computing Machinery, New York, NY, USA, 1293\u20131304. DOI: https://doi.org/10.1145/2750858.2805845","title":"Citation"},{"location":"citation/#cite-rapids-and-providers","text":"RAPIDS and the community RAPIDS is a community effort and as such we want to continue recognizing the contributions from other researchers. Besides citing RAPIDS, we ask you to cite any of the authors listed below if you used those sensor providers in your analysis, thank you!","title":"Cite RAPIDS and providers"},{"location":"citation/#rapids","text":"If you used RAPIDS, please cite this paper . RAPIDS et al. citation Vega J, Li M, Aguillera K, Goel N, Joshi E, Durica KC, Kunta AR, Low CA RAPIDS: Reproducible Analysis Pipeline for Data Streams Collected with Mobile Devices JMIR Preprints. 18/08/2020:23246 DOI: 10.2196/preprints.23246 URL: https://preprints.jmir.org/preprint/23246","title":"RAPIDS"},{"location":"citation/#panda-accelerometer","text":"If you computed accelerometer features using the provider [PHONE_ACCLEROMETER][PANDA] cite this paper in addition to RAPIDS. Panda et al. citation Panda N, Solsky I, Huang EJ, Lipsitz S, Pradarelli JC, Delisle M, Cusack JC, Gadd MA, Lubitz CC, Mullen JT, Qadan M, Smith BL, Specht M, Stephen AE, Tanabe KK, Gawande AA, Onnela JP, Haynes AB. Using Smartphones to Capture Novel Recovery Metrics After Cancer Surgery. JAMA Surg. 2020 Feb 1;155(2):123-129. doi: 10.1001/jamasurg.2019.4702. PMID: 31657854; PMCID: PMC6820047.","title":"Panda (accelerometer)"},{"location":"citation/#stachl-applications-foreground","text":"If you computed applications foreground features using the app category (genre) catalogue in [PHONE_APPLICATIONS_FOREGROUND][RAPIDS] cite this paper in addition to RAPIDS. Stachl et al. citation Clemens Stachl, Quay Au, Ramona Schoedel, Samuel D. Gosling, Gabriella M. Harari, Daniel Buschek, Sarah Theres V\u00f6lkel, Tobias Schuwerk, Michelle Oldemeier, Theresa Ullmann, Heinrich Hussmann, Bernd Bischl, Markus B\u00fchner. Proceedings of the National Academy of Sciences Jul 2020, 117 (30) 17680-17687; DOI: 10.1073/pnas.1920484117","title":"Stachl (applications foreground)"},{"location":"citation/#doryab-bluetooth","text":"If you computed bluetooth features using the provider [PHONE_BLUETOOTH][DORYAB] cite this paper in addition to RAPIDS. Doryab et al. citation Doryab, A., Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral Features from Smartphone and Wearable Data. ArXiv:1812.10394 [Cs, Stat]. http://arxiv.org/abs/1812.10394","title":"Doryab (bluetooth)"},{"location":"citation/#barnett-locations","text":"If you computed locations features using the provider [PHONE_LOCATIONS][BARNETT] cite this paper and this paper in addition to RAPIDS. Barnett et al. citation Ian Barnett, Jukka-Pekka Onnela, Inferring mobility measures from GPS traces with missing data, Biostatistics, Volume 21, Issue 2, April 2020, Pages e98\u2013e112, https://doi.org/10.1093/biostatistics/kxy059 Canzian et al. citation Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp \u201815). Association for Computing Machinery, New York, NY, USA, 1293\u20131304. DOI: https://doi.org/10.1145/2750858.2805845","title":"Barnett (locations)"},{"location":"citation/#doryab-locations","text":"If you computed locations features using the provider [PHONE_LOCATIONS][DORYAB] cite this paper and this paper in addition to RAPIDS. Doryab et al. citation Doryab, A., Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral Features from Smartphone and Wearable Data. ArXiv:1812.10394 [Cs, Stat]. http://arxiv.org/abs/1812.10394 Canzian et al. citation Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp \u201815). Association for Computing Machinery, New York, NY, USA, 1293\u20131304. DOI: https://doi.org/10.1145/2750858.2805845","title":"Doryab (locations)"},{"location":"code_of_conduct/","text":"Contributor Covenant Code of Conduct \u00b6 Our Pledge \u00b6 We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community. Our Standards \u00b6 Examples of behavior that contributes to a positive environment for our community include: Demonstrating empathy and kindness toward other people Being respectful of differing opinions, viewpoints, and experiences Giving and gracefully accepting constructive feedback Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience Focusing on what is best not just for us as individuals, but for the overall community Examples of unacceptable behavior include: The use of sexualized language or imagery, and sexual attention or advances of any kind Trolling, insulting or derogatory comments, and personal or political attacks Public or private harassment Publishing others\u2019 private information, such as a physical or email address, without their explicit permission Other conduct which could reasonably be considered inappropriate in a professional setting Enforcement Responsibilities \u00b6 Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful. Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate. Scope \u00b6 This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Enforcement \u00b6 Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at moshi@pitt.edu . All complaints will be reviewed and investigated promptly and fairly. All community leaders are obligated to respect the privacy and security of the reporter of any incident. Enforcement Guidelines \u00b6 Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct: 1. Correction \u00b6 Community Impact : Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community. Consequence : A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested. 2. Warning \u00b6 Community Impact : A violation through a single incident or series of actions. Consequence : A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban. 3. Temporary Ban \u00b6 Community Impact : A serious violation of community standards, including sustained inappropriate behavior. Consequence : A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban. 4. Permanent Ban \u00b6 Community Impact : Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals. Consequence : A permanent ban from any sort of public interaction within the community. Attribution \u00b6 This Code of Conduct is adapted from the Contributor Covenant , version 2.0, available at https://www.contributor-covenant.org/version/2/0/code_of_conduct.html . Community Impact Guidelines were inspired by Mozilla\u2019s code of conduct enforcement ladder . For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq . Translations are available at https://www.contributor-covenant.org/translations .","title":"Code of Conduct"},{"location":"code_of_conduct/#contributor-covenant-code-of-conduct","text":"","title":"Contributor Covenant Code of Conduct"},{"location":"code_of_conduct/#our-pledge","text":"We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.","title":"Our Pledge"},{"location":"code_of_conduct/#our-standards","text":"Examples of behavior that contributes to a positive environment for our community include: Demonstrating empathy and kindness toward other people Being respectful of differing opinions, viewpoints, and experiences Giving and gracefully accepting constructive feedback Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience Focusing on what is best not just for us as individuals, but for the overall community Examples of unacceptable behavior include: The use of sexualized language or imagery, and sexual attention or advances of any kind Trolling, insulting or derogatory comments, and personal or political attacks Public or private harassment Publishing others\u2019 private information, such as a physical or email address, without their explicit permission Other conduct which could reasonably be considered inappropriate in a professional setting","title":"Our Standards"},{"location":"code_of_conduct/#enforcement-responsibilities","text":"Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful. Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate.","title":"Enforcement Responsibilities"},{"location":"code_of_conduct/#scope","text":"This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event.","title":"Scope"},{"location":"code_of_conduct/#enforcement","text":"Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at moshi@pitt.edu . All complaints will be reviewed and investigated promptly and fairly. All community leaders are obligated to respect the privacy and security of the reporter of any incident.","title":"Enforcement"},{"location":"code_of_conduct/#enforcement-guidelines","text":"Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:","title":"Enforcement Guidelines"},{"location":"code_of_conduct/#1-correction","text":"Community Impact : Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community. Consequence : A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested.","title":"1. Correction"},{"location":"code_of_conduct/#2-warning","text":"Community Impact : A violation through a single incident or series of actions. Consequence : A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban.","title":"2. Warning"},{"location":"code_of_conduct/#3-temporary-ban","text":"Community Impact : A serious violation of community standards, including sustained inappropriate behavior. Consequence : A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.","title":"3. Temporary Ban"},{"location":"code_of_conduct/#4-permanent-ban","text":"Community Impact : Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals. Consequence : A permanent ban from any sort of public interaction within the community.","title":"4. Permanent Ban"},{"location":"code_of_conduct/#attribution","text":"This Code of Conduct is adapted from the Contributor Covenant , version 2.0, available at https://www.contributor-covenant.org/version/2/0/code_of_conduct.html . Community Impact Guidelines were inspired by Mozilla\u2019s code of conduct enforcement ladder . For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq . Translations are available at https://www.contributor-covenant.org/translations .","title":"Attribution"},{"location":"faq/","text":"Frequently Asked Questions \u00b6 Cannot connect to your MySQL server \u00b6 Problem **Error in .local ( drv, \\. .. ) :** **Failed to connect to database: Error: Can \\' t initialize character set unknown ( path: compiled \\_ in ) ** : Calls: dbConnect -> dbConnect -> .local -> .Call Execution halted [ Tue Mar 10 19 :40:15 2020 ] Error in rule download_dataset: jobid: 531 output: data/raw/p60/locations_raw.csv RuleException: CalledProcessError in line 20 of /home/ubuntu/rapids/rules/preprocessing.snakefile: Command 'set -euo pipefail; Rscript --vanilla /home/ubuntu/rapids/.snakemake/scripts/tmp_2jnvqs7.download_dataset.R' returned non-zero exit status 1 . File \"/home/ubuntu/rapids/rules/preprocessing.snakefile\" , line 20 , in __rule_download_dataset File \"/home/ubuntu/anaconda3/envs/moshi-env/lib/python3.7/concurrent/futures/thread.py\" , line 57 , in run Shutting down, this might take some time. Exiting because a job execution failed. Look above for error message Solution Please make sure the DATABASE_GROUP in config.yaml matches your DB credentials group in .env . Cannot start mysql in linux via brew services start mysql \u00b6 Problem Cannot start mysql in linux via brew services start mysql Solution Use mysql.server start Every time I run force the download_dataset rule all rules are executed \u00b6 Problem When running snakemake -j1 -R download_phone_data or ./rapids -j1 -R download_phone_data all the rules and files are re-computed Solution This is expected behavior. The advantage of using snakemake under the hood is that every time a file containing data is modified every rule that depends on that file will be re-executed to update their results. In this case, since download_dataset updates all the raw data, and you are forcing the rule with the flag -R every single rule that depends on those raw files will be executed. Error Table XXX doesn't exist while running the download_phone_data or download_fitbit_data rule. \u00b6 Problem Error in .local ( conn, statement, ... ) : could not run statement: Table 'db_name.table_name' doesn ' t exist Calls: colnames ... .local -> dbSendQuery -> dbSendQuery -> .local -> .Call Execution halted Solution Please make sure the sensors listed in [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] and the [TABLE] of each sensor you activated in config.yaml match your database tables. How do I install RAPIDS on Ubuntu 16.04 \u00b6 Solution Install dependencies (Homebrew - if not installed): sudo apt-get install libmariadb-client-lgpl-dev libxml2-dev libssl-dev Install brew for linux and add the following line to ~/.bashrc : export PATH=$HOME/.linuxbrew/bin:$PATH source ~/.bashrc Install MySQL brew install mysql brew services start mysql Install R, pandoc and rmarkdown: brew install r brew install gcc@6 (needed due to this bug ) HOMEBREW_CC=gcc-6 brew install pandoc Install miniconda using these instructions Clone our repo: git clone https://github.com/carissalow/rapids Create a python virtual environment: cd rapids conda env create -f environment.yml -n MY_ENV_NAME conda activate MY_ENV_NAME Install R packages and virtual environment: snakemake renv_install snakemake renv_init snakemake renv_restore This step could take several minutes to complete. Please be patient and let it run until completion. mysql.h cannot be found \u00b6 Problem -------------------------- [ ERROR MESSAGE ] ----------------------------
| movingtostaticratio | - | -Ratio between the number of rows labeled Moving versus Static | +Ratio between stationary time and total location sensed time. A lat/long coordinate pair is labelled as stationary if it’s speed (distance/time) to the next coordinate pair is less than 1km/hr. A higher value represents a more stationary routine. These times are computed by multiplying the number of rows by [SAMPLING_FREQUENCY] |
| outlierstimepercent | - | -Ratio between the number of rows that belong to non-significant clusters divided by the total number of rows in a time segment. | +Ratio between the time spent in non-significant clusters divided by the time spent in all clusters (total location sensed time). A higher value represents more time spent in non-significant clusters. These times are computed by multiplying the number of rows by [SAMPLING_FREQUENCY] |
| maxlengthstayatclusters | diff --git a/latest/search/search_index.json b/latest/search/search_index.json index 603a13a9..b16ee4db 100644 --- a/latest/search/search_index.json +++ b/latest/search/search_index.json @@ -1 +1 @@ -{"config":{"lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Welcome to RAPIDS documentation \u00b6 Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to extract and create behavioral features (a.k.a. digital biomarkers), visualize mobile sensor data and structure your analysis into reproducible workflows. RAPIDS is open source, documented, modular, tested, and reproducible. At the moment we support smartphone data collected with AWARE and wearable data from Fitbit devices. Tip Questions or feedback can be posted on the #rapids channel in AWARE Framework's slack . Bugs and feature requests should be posted on Github . Join our discussions on our algorithms and assumptions for feature processing . Ready to start? Go to Installation , then to Configuration , and then to Execution Are you upgrading from RAPIDS beta ? Follow this guide How does it work? \u00b6 RAPIDS is formed by R and Python scripts orchestrated by Snakemake . We suggest you read Snakemake\u2019s docs but in short: every link in the analysis chain is atomic and has files as input and output. Behavioral features are processed per sensor and per participant. What are the benefits of using RAPIDS? \u00b6 Consistent analysis . Every participant sensor dataset is analyzed in the exact same way and isolated from each other. Efficient analysis . Every analysis step is executed only once. Whenever your data or configuration changes only the affected files are updated. Parallel execution . Thanks to Snakemake, your analysis can be executed over multiple cores without changing your code. Code-free features . Extract any of the behavioral features offered by RAPIDS without writing any code. Extensible code . You can easily add your own behavioral features in R or Python, share them with the community, and keep authorship and citations. Timezone aware . Your data is adjusted to the specified timezone (multiple timezones suport coming soon ). Flexible time segments . You can extract behavioral features on time windows of any length (e.g. 5 minutes, 3 hours, 2 days), on every day or particular days (e.g. weekends, Mondays, the 1 st of each month, etc.) or around events of interest (e.g. surveys or clinical relapses). Tested code . We are constantly adding tests to make sure our behavioral features are correct. Reproducible code . If you structure your analysis within RAPIDS, you can be sure your code will run in other computers as intended thanks to R and Python virtual environments. You can share your analysis code along your publications without any overhead. Private . All your data is processed locally. How is it organized? \u00b6 In broad terms the config.yaml , .env file , participants files , and time segment files are the only ones that you will have to modify. All data is stored in data/ and all scripts are stored in src/ . For more information see RAPIDS\u2019 File Structure .","title":"Home"},{"location":"#welcome-to-rapids-documentation","text":"Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to extract and create behavioral features (a.k.a. digital biomarkers), visualize mobile sensor data and structure your analysis into reproducible workflows. RAPIDS is open source, documented, modular, tested, and reproducible. At the moment we support smartphone data collected with AWARE and wearable data from Fitbit devices. Tip Questions or feedback can be posted on the #rapids channel in AWARE Framework's slack . Bugs and feature requests should be posted on Github . Join our discussions on our algorithms and assumptions for feature processing . Ready to start? Go to Installation , then to Configuration , and then to Execution Are you upgrading from RAPIDS beta ? Follow this guide","title":"Welcome to RAPIDS documentation"},{"location":"#how-does-it-work","text":"RAPIDS is formed by R and Python scripts orchestrated by Snakemake . We suggest you read Snakemake\u2019s docs but in short: every link in the analysis chain is atomic and has files as input and output. Behavioral features are processed per sensor and per participant.","title":"How does it work?"},{"location":"#what-are-the-benefits-of-using-rapids","text":"Consistent analysis . Every participant sensor dataset is analyzed in the exact same way and isolated from each other. Efficient analysis . Every analysis step is executed only once. Whenever your data or configuration changes only the affected files are updated. Parallel execution . Thanks to Snakemake, your analysis can be executed over multiple cores without changing your code. Code-free features . Extract any of the behavioral features offered by RAPIDS without writing any code. Extensible code . You can easily add your own behavioral features in R or Python, share them with the community, and keep authorship and citations. Timezone aware . Your data is adjusted to the specified timezone (multiple timezones suport coming soon ). Flexible time segments . You can extract behavioral features on time windows of any length (e.g. 5 minutes, 3 hours, 2 days), on every day or particular days (e.g. weekends, Mondays, the 1 st of each month, etc.) or around events of interest (e.g. surveys or clinical relapses). Tested code . We are constantly adding tests to make sure our behavioral features are correct. Reproducible code . If you structure your analysis within RAPIDS, you can be sure your code will run in other computers as intended thanks to R and Python virtual environments. You can share your analysis code along your publications without any overhead. Private . All your data is processed locally.","title":"What are the benefits of using RAPIDS?"},{"location":"#how-is-it-organized","text":"In broad terms the config.yaml , .env file , participants files , and time segment files are the only ones that you will have to modify. All data is stored in data/ and all scripts are stored in src/ . For more information see RAPIDS\u2019 File Structure .","title":"How is it organized?"},{"location":"change-log/","text":"Change Log \u00b6 v0.3.1 \u00b6 Update installation docs for RAPIDS\u2019 docker container Fix example analysis use of accelerometer data in a plot Update FAQ Update minimal example documentation Minor doc updates v0.3.0 \u00b6 Update R and Python virtual environments Add GH actions CI support for tests and docker Add release and test badges to README v0.2.6 \u00b6 Fix old versions banner on nested pages v0.2.5 \u00b6 Fix docs deploy typo v0.2.4 \u00b6 Fix broken links in landing page and docs deploy v0.2.3 \u00b6 Fix participant IDS in the example analysis workflow v0.2.2 \u00b6 Fix readme link to docs v0.2.1 \u00b6 FIx link to the most recent version in the old version banner v0.2.0 \u00b6 Add new PHONE_BLUETOOTH DORYAB provider Deprecate PHONE_BLUETOOTH RAPIDS provider Fix bug in filter_data_by_segment for Python when dataset was empty Minor doc updates New FAQ item v0.1.0 \u00b6 New and more consistent docs (this website). The previous docs are marked as beta Consolidate configuration instructions Flexible time segments Simplify Fitbit behavioral feature extraction and documentation Sensor\u2019s configuration and output is more consistent Update visualizations to handle flexible day segments Create a RAPIDS execution script that allows re-computation of the pipeline after configuration changes Add citation guide Update virtual environment guide Update analysis workflow example Add a Code of Conduct Update Team page","title":"Change Log"},{"location":"change-log/#change-log","text":"","title":"Change Log"},{"location":"change-log/#v031","text":"Update installation docs for RAPIDS\u2019 docker container Fix example analysis use of accelerometer data in a plot Update FAQ Update minimal example documentation Minor doc updates","title":"v0.3.1"},{"location":"change-log/#v030","text":"Update R and Python virtual environments Add GH actions CI support for tests and docker Add release and test badges to README","title":"v0.3.0"},{"location":"change-log/#v026","text":"Fix old versions banner on nested pages","title":"v0.2.6"},{"location":"change-log/#v025","text":"Fix docs deploy typo","title":"v0.2.5"},{"location":"change-log/#v024","text":"Fix broken links in landing page and docs deploy","title":"v0.2.4"},{"location":"change-log/#v023","text":"Fix participant IDS in the example analysis workflow","title":"v0.2.3"},{"location":"change-log/#v022","text":"Fix readme link to docs","title":"v0.2.2"},{"location":"change-log/#v021","text":"FIx link to the most recent version in the old version banner","title":"v0.2.1"},{"location":"change-log/#v020","text":"Add new PHONE_BLUETOOTH DORYAB provider Deprecate PHONE_BLUETOOTH RAPIDS provider Fix bug in filter_data_by_segment for Python when dataset was empty Minor doc updates New FAQ item","title":"v0.2.0"},{"location":"change-log/#v010","text":"New and more consistent docs (this website). The previous docs are marked as beta Consolidate configuration instructions Flexible time segments Simplify Fitbit behavioral feature extraction and documentation Sensor\u2019s configuration and output is more consistent Update visualizations to handle flexible day segments Create a RAPIDS execution script that allows re-computation of the pipeline after configuration changes Add citation guide Update virtual environment guide Update analysis workflow example Add a Code of Conduct Update Team page","title":"v0.1.0"},{"location":"citation/","text":"Cite RAPIDS and providers \u00b6 RAPIDS and the community RAPIDS is a community effort and as such we want to continue recognizing the contributions from other researchers. Besides citing RAPIDS, we ask you to cite any of the authors listed below if you used those sensor providers in your analysis, thank you! RAPIDS \u00b6 If you used RAPIDS, please cite this paper . RAPIDS et al. citation Vega J, Li M, Aguillera K, Goel N, Joshi E, Durica KC, Kunta AR, Low CA RAPIDS: Reproducible Analysis Pipeline for Data Streams Collected with Mobile Devices JMIR Preprints. 18/08/2020:23246 DOI: 10.2196/preprints.23246 URL: https://preprints.jmir.org/preprint/23246 Panda (accelerometer) \u00b6 If you computed accelerometer features using the provider [PHONE_ACCLEROMETER][PANDA] cite this paper in addition to RAPIDS. Panda et al. citation Panda N, Solsky I, Huang EJ, Lipsitz S, Pradarelli JC, Delisle M, Cusack JC, Gadd MA, Lubitz CC, Mullen JT, Qadan M, Smith BL, Specht M, Stephen AE, Tanabe KK, Gawande AA, Onnela JP, Haynes AB. Using Smartphones to Capture Novel Recovery Metrics After Cancer Surgery. JAMA Surg. 2020 Feb 1;155(2):123-129. doi: 10.1001/jamasurg.2019.4702. PMID: 31657854; PMCID: PMC6820047. Stachl (applications foreground) \u00b6 If you computed applications foreground features using the app category (genre) catalogue in [PHONE_APPLICATIONS_FOREGROUND][RAPIDS] cite this paper in addition to RAPIDS. Stachl et al. citation Clemens Stachl, Quay Au, Ramona Schoedel, Samuel D. Gosling, Gabriella M. Harari, Daniel Buschek, Sarah Theres V\u00f6lkel, Tobias Schuwerk, Michelle Oldemeier, Theresa Ullmann, Heinrich Hussmann, Bernd Bischl, Markus B\u00fchner. Proceedings of the National Academy of Sciences Jul 2020, 117 (30) 17680-17687; DOI: 10.1073/pnas.1920484117 Doryab (bluetooth) \u00b6 If you computed bluetooth features using the provider [PHONE_BLUETOOTH][DORYAB] cite this paper in addition to RAPIDS. Doryab et al. citation Doryab, A., Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral Features from Smartphone and Wearable Data. ArXiv:1812.10394 [Cs, Stat]. http://arxiv.org/abs/1812.10394 Barnett (locations) \u00b6 If you computed locations features using the provider [PHONE_LOCATIONS][BARNETT] cite this paper and this paper in addition to RAPIDS. Barnett et al. citation Ian Barnett, Jukka-Pekka Onnela, Inferring mobility measures from GPS traces with missing data, Biostatistics, Volume 21, Issue 2, April 2020, Pages e98\u2013e112, https://doi.org/10.1093/biostatistics/kxy059 Canzian et al. citation Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp \u201815). Association for Computing Machinery, New York, NY, USA, 1293\u20131304. DOI: https://doi.org/10.1145/2750858.2805845 Doryab (locations) \u00b6 If you computed locations features using the provider [PHONE_LOCATIONS][DORYAB] cite this paper and this paper in addition to RAPIDS. Doryab et al. citation Doryab, A., Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral Features from Smartphone and Wearable Data. ArXiv:1812.10394 [Cs, Stat]. http://arxiv.org/abs/1812.10394 Canzian et al. citation Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp \u201815). Association for Computing Machinery, New York, NY, USA, 1293\u20131304. DOI: https://doi.org/10.1145/2750858.2805845","title":"Citation"},{"location":"citation/#cite-rapids-and-providers","text":"RAPIDS and the community RAPIDS is a community effort and as such we want to continue recognizing the contributions from other researchers. Besides citing RAPIDS, we ask you to cite any of the authors listed below if you used those sensor providers in your analysis, thank you!","title":"Cite RAPIDS and providers"},{"location":"citation/#rapids","text":"If you used RAPIDS, please cite this paper . RAPIDS et al. citation Vega J, Li M, Aguillera K, Goel N, Joshi E, Durica KC, Kunta AR, Low CA RAPIDS: Reproducible Analysis Pipeline for Data Streams Collected with Mobile Devices JMIR Preprints. 18/08/2020:23246 DOI: 10.2196/preprints.23246 URL: https://preprints.jmir.org/preprint/23246","title":"RAPIDS"},{"location":"citation/#panda-accelerometer","text":"If you computed accelerometer features using the provider [PHONE_ACCLEROMETER][PANDA] cite this paper in addition to RAPIDS. Panda et al. citation Panda N, Solsky I, Huang EJ, Lipsitz S, Pradarelli JC, Delisle M, Cusack JC, Gadd MA, Lubitz CC, Mullen JT, Qadan M, Smith BL, Specht M, Stephen AE, Tanabe KK, Gawande AA, Onnela JP, Haynes AB. Using Smartphones to Capture Novel Recovery Metrics After Cancer Surgery. JAMA Surg. 2020 Feb 1;155(2):123-129. doi: 10.1001/jamasurg.2019.4702. PMID: 31657854; PMCID: PMC6820047.","title":"Panda (accelerometer)"},{"location":"citation/#stachl-applications-foreground","text":"If you computed applications foreground features using the app category (genre) catalogue in [PHONE_APPLICATIONS_FOREGROUND][RAPIDS] cite this paper in addition to RAPIDS. Stachl et al. citation Clemens Stachl, Quay Au, Ramona Schoedel, Samuel D. Gosling, Gabriella M. Harari, Daniel Buschek, Sarah Theres V\u00f6lkel, Tobias Schuwerk, Michelle Oldemeier, Theresa Ullmann, Heinrich Hussmann, Bernd Bischl, Markus B\u00fchner. Proceedings of the National Academy of Sciences Jul 2020, 117 (30) 17680-17687; DOI: 10.1073/pnas.1920484117","title":"Stachl (applications foreground)"},{"location":"citation/#doryab-bluetooth","text":"If you computed bluetooth features using the provider [PHONE_BLUETOOTH][DORYAB] cite this paper in addition to RAPIDS. Doryab et al. citation Doryab, A., Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral Features from Smartphone and Wearable Data. ArXiv:1812.10394 [Cs, Stat]. http://arxiv.org/abs/1812.10394","title":"Doryab (bluetooth)"},{"location":"citation/#barnett-locations","text":"If you computed locations features using the provider [PHONE_LOCATIONS][BARNETT] cite this paper and this paper in addition to RAPIDS. Barnett et al. citation Ian Barnett, Jukka-Pekka Onnela, Inferring mobility measures from GPS traces with missing data, Biostatistics, Volume 21, Issue 2, April 2020, Pages e98\u2013e112, https://doi.org/10.1093/biostatistics/kxy059 Canzian et al. citation Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp \u201815). Association for Computing Machinery, New York, NY, USA, 1293\u20131304. DOI: https://doi.org/10.1145/2750858.2805845","title":"Barnett (locations)"},{"location":"citation/#doryab-locations","text":"If you computed locations features using the provider [PHONE_LOCATIONS][DORYAB] cite this paper and this paper in addition to RAPIDS. Doryab et al. citation Doryab, A., Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral Features from Smartphone and Wearable Data. ArXiv:1812.10394 [Cs, Stat]. http://arxiv.org/abs/1812.10394 Canzian et al. citation Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp \u201815). Association for Computing Machinery, New York, NY, USA, 1293\u20131304. DOI: https://doi.org/10.1145/2750858.2805845","title":"Doryab (locations)"},{"location":"code_of_conduct/","text":"Contributor Covenant Code of Conduct \u00b6 Our Pledge \u00b6 We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community. Our Standards \u00b6 Examples of behavior that contributes to a positive environment for our community include: Demonstrating empathy and kindness toward other people Being respectful of differing opinions, viewpoints, and experiences Giving and gracefully accepting constructive feedback Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience Focusing on what is best not just for us as individuals, but for the overall community Examples of unacceptable behavior include: The use of sexualized language or imagery, and sexual attention or advances of any kind Trolling, insulting or derogatory comments, and personal or political attacks Public or private harassment Publishing others\u2019 private information, such as a physical or email address, without their explicit permission Other conduct which could reasonably be considered inappropriate in a professional setting Enforcement Responsibilities \u00b6 Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful. Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate. Scope \u00b6 This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Enforcement \u00b6 Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at moshi@pitt.edu . All complaints will be reviewed and investigated promptly and fairly. All community leaders are obligated to respect the privacy and security of the reporter of any incident. Enforcement Guidelines \u00b6 Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct: 1. Correction \u00b6 Community Impact : Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community. Consequence : A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested. 2. Warning \u00b6 Community Impact : A violation through a single incident or series of actions. Consequence : A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban. 3. Temporary Ban \u00b6 Community Impact : A serious violation of community standards, including sustained inappropriate behavior. Consequence : A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban. 4. Permanent Ban \u00b6 Community Impact : Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals. Consequence : A permanent ban from any sort of public interaction within the community. Attribution \u00b6 This Code of Conduct is adapted from the Contributor Covenant , version 2.0, available at https://www.contributor-covenant.org/version/2/0/code_of_conduct.html . Community Impact Guidelines were inspired by Mozilla\u2019s code of conduct enforcement ladder . For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq . Translations are available at https://www.contributor-covenant.org/translations .","title":"Code of Conduct"},{"location":"code_of_conduct/#contributor-covenant-code-of-conduct","text":"","title":"Contributor Covenant Code of Conduct"},{"location":"code_of_conduct/#our-pledge","text":"We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.","title":"Our Pledge"},{"location":"code_of_conduct/#our-standards","text":"Examples of behavior that contributes to a positive environment for our community include: Demonstrating empathy and kindness toward other people Being respectful of differing opinions, viewpoints, and experiences Giving and gracefully accepting constructive feedback Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience Focusing on what is best not just for us as individuals, but for the overall community Examples of unacceptable behavior include: The use of sexualized language or imagery, and sexual attention or advances of any kind Trolling, insulting or derogatory comments, and personal or political attacks Public or private harassment Publishing others\u2019 private information, such as a physical or email address, without their explicit permission Other conduct which could reasonably be considered inappropriate in a professional setting","title":"Our Standards"},{"location":"code_of_conduct/#enforcement-responsibilities","text":"Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful. Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate.","title":"Enforcement Responsibilities"},{"location":"code_of_conduct/#scope","text":"This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event.","title":"Scope"},{"location":"code_of_conduct/#enforcement","text":"Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at moshi@pitt.edu . All complaints will be reviewed and investigated promptly and fairly. All community leaders are obligated to respect the privacy and security of the reporter of any incident.","title":"Enforcement"},{"location":"code_of_conduct/#enforcement-guidelines","text":"Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:","title":"Enforcement Guidelines"},{"location":"code_of_conduct/#1-correction","text":"Community Impact : Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community. Consequence : A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested.","title":"1. Correction"},{"location":"code_of_conduct/#2-warning","text":"Community Impact : A violation through a single incident or series of actions. Consequence : A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban.","title":"2. Warning"},{"location":"code_of_conduct/#3-temporary-ban","text":"Community Impact : A serious violation of community standards, including sustained inappropriate behavior. Consequence : A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.","title":"3. Temporary Ban"},{"location":"code_of_conduct/#4-permanent-ban","text":"Community Impact : Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals. Consequence : A permanent ban from any sort of public interaction within the community.","title":"4. Permanent Ban"},{"location":"code_of_conduct/#attribution","text":"This Code of Conduct is adapted from the Contributor Covenant , version 2.0, available at https://www.contributor-covenant.org/version/2/0/code_of_conduct.html . Community Impact Guidelines were inspired by Mozilla\u2019s code of conduct enforcement ladder . For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq . Translations are available at https://www.contributor-covenant.org/translations .","title":"Attribution"},{"location":"faq/","text":"Frequently Asked Questions \u00b6 Cannot connect to your MySQL server \u00b6 Problem **Error in .local ( drv, \\. .. ) :** **Failed to connect to database: Error: Can \\' t initialize character set unknown ( path: compiled \\_ in ) ** : Calls: dbConnect -> dbConnect -> .local -> .Call Execution halted [ Tue Mar 10 19 :40:15 2020 ] Error in rule download_dataset: jobid: 531 output: data/raw/p60/locations_raw.csv RuleException: CalledProcessError in line 20 of /home/ubuntu/rapids/rules/preprocessing.snakefile: Command 'set -euo pipefail; Rscript --vanilla /home/ubuntu/rapids/.snakemake/scripts/tmp_2jnvqs7.download_dataset.R' returned non-zero exit status 1 . File \"/home/ubuntu/rapids/rules/preprocessing.snakefile\" , line 20 , in __rule_download_dataset File \"/home/ubuntu/anaconda3/envs/moshi-env/lib/python3.7/concurrent/futures/thread.py\" , line 57 , in run Shutting down, this might take some time. Exiting because a job execution failed. Look above for error message Solution Please make sure the DATABASE_GROUP in config.yaml matches your DB credentials group in .env . Cannot start mysql in linux via brew services start mysql \u00b6 Problem Cannot start mysql in linux via brew services start mysql Solution Use mysql.server start Every time I run force the download_dataset rule all rules are executed \u00b6 Problem When running snakemake -j1 -R download_phone_data or ./rapids -j1 -R download_phone_data all the rules and files are re-computed Solution This is expected behavior. The advantage of using snakemake under the hood is that every time a file containing data is modified every rule that depends on that file will be re-executed to update their results. In this case, since download_dataset updates all the raw data, and you are forcing the rule with the flag -R every single rule that depends on those raw files will be executed. Error Table XXX doesn't exist while running the download_phone_data or download_fitbit_data rule. \u00b6 Problem Error in .local ( conn, statement, ... ) : could not run statement: Table 'db_name.table_name' doesn ' t exist Calls: colnames ... .local -> dbSendQuery -> dbSendQuery -> .local -> .Call Execution halted Solution Please make sure the sensors listed in [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] and the [TABLE] of each sensor you activated in config.yaml match your database tables. How do I install RAPIDS on Ubuntu 16.04 \u00b6 Solution Install dependencies (Homebrew - if not installed): sudo apt-get install libmariadb-client-lgpl-dev libxml2-dev libssl-dev Install brew for linux and add the following line to ~/.bashrc : export PATH=$HOME/.linuxbrew/bin:$PATH source ~/.bashrc Install MySQL brew install mysql brew services start mysql Install R, pandoc and rmarkdown: brew install r brew install gcc@6 (needed due to this bug ) HOMEBREW_CC=gcc-6 brew install pandoc Install miniconda using these instructions Clone our repo: git clone https://github.com/carissalow/rapids Create a python virtual environment: cd rapids conda env create -f environment.yml -n MY_ENV_NAME conda activate MY_ENV_NAME Install R packages and virtual environment: snakemake renv_install snakemake renv_init snakemake renv_restore This step could take several minutes to complete. Please be patient and let it run until completion. mysql.h cannot be found \u00b6 Problem -------------------------- [ ERROR MESSAGE ] ----------------------------