diff --git a/content/en/case-studies/blackhole-image.md b/content/en/case-studies/blackhole-image.md index f7fd5ec219..adfe4ca201 100644 --- a/content/en/case-studies/blackhole-image.md +++ b/content/en/case-studies/blackhole-image.md @@ -3,13 +3,13 @@ title: "Case Study: First Image of a Black Hole" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/blackhole.jpg' -title = 'Black Hole M87' -alt = 'black hole image' -attribution = '(Image Credits: Event Horizon Telescope Collaboration)' -attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/blackhole.jpg" + title="Black Hole M87" + alt="black hole image" + attribution="(Image Credits: Event Horizon Telescope Collaboration)" + attributionlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" +>}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" @@ -70,14 +70,14 @@ from a sidewalk café in Paris! When the goal is to see something never before seen, how can scientists be confident the image is correct? -{{< figure >}} -src = '/images/content_images/cs/dataprocessbh.png' -title = 'EHT Data Processing Pipeline' -alt = 'data pipeline' -align = 'center' -attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' -attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/dataprocessbh.png" + title="EHT Data Processing Pipeline" + alt="data pipeline" + align="center" + attribution="(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)" + attributionlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" +>}} ## NumPy’s Role @@ -93,11 +93,11 @@ first-of-a-kind image of the black hole. Their work illustrates the role the scientific Python ecosystem plays in advancing science through collaborative data analysis. -{{< figure >}} -src = '/images/content_images/cs/bh_numpy_role.png' -alt = 'role of numpy' -title = 'The role of NumPy in Black Hole imaging' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/bh_numpy_role.png" + alt="role of numpy" + title="The role of NumPy in Black Hole imaging" +>}} For example, the [`eht-imaging`][ehtim] Python package provides tools for simulating and performing image reconstruction on VLBI data. @@ -105,11 +105,11 @@ NumPy is at the core of array data processing used in this package, as illustrated by the partial software dependency chart below. -{{< figure >}} -src = '/images/content_images/cs/ehtim_numpy.png' -alt = 'ehtim dependency map highlighting numpy' -title = 'Software dependency chart of ehtim package highlighting NumPy' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/ehtim_numpy.png" + alt="ehtim dependency map highlighting numpy" + title="Software dependency chart of ehtim package highlighting NumPy" +>}} [ehtim]: https://github.com/achael/eht-imaging @@ -136,8 +136,8 @@ best radio observatories. Innovative algorithms and data processing techniques, improving upon existing astronomical models, helped unfold a mystery of the universe. -{{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_bh_benefits.png" + alt="numpy benefits" + title="Key NumPy Capabilities utilized" +>}} diff --git a/content/en/case-studies/cricket-analytics.md b/content/en/case-studies/cricket-analytics.md index 6abd3b0291..766380fb25 100644 --- a/content/en/case-studies/cricket-analytics.md +++ b/content/en/case-studies/cricket-analytics.md @@ -3,13 +3,13 @@ title: "Case Study: Cricket Analytics, the game changer!" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/ipl-stadium.png' -title = 'IPLT20, the biggest Cricket Festival in India' -alt = 'Indian Premier League Cricket cup and stadium' -attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' -attributionlink = 'https://unsplash.com/@aksh1802' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/ipl-stadium.png" + title="IPLT20, the biggest Cricket Festival in India" + alt="Indian Premier League Cricket cup and stadium" + attribution="(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))" + attributionlink="https://unsplash.com/@aksh1802" +>}} {{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" @@ -59,14 +59,14 @@ metrics for improving match winning chances: * gaining insights into fitness and performance of a player against different opposition, * player contribution to wins and losses for making strategic decisions on team composition -{{< figure >}} -src = '/images/content_images/cs/cricket-pitch.png' -title = 'Cricket Pitch, the focal point in the field' -alt = 'A cricket pitch with bowler and batsmen' -align = 'center' -attribution = '(Image credit: Debarghya Das)' -attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/cricket-pitch.png" + title="Cricket Pitch, the focal point in the field" + alt="A cricket pitch with bowler and batsmen" + align="center" + attribution="(Image credit: Debarghya Das)" + attributionlink="http://debarghyadas.com/files/IPLpaper.pdf" +>}} ### Key Data Analytics Objectives @@ -81,13 +81,13 @@ attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' number crunching and data science know-how, visualization tools and capability to include newer observations in the analysis. -{{< figure >}} -src = '/images/content_images/cs/player-pose-estimator.png' -alt = 'pose estimator' -title = 'Cricket Pose Estimator' -attribution = '(Image credit: connect.vin)' -attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/player-pose-estimator.png" + alt="pose estimator" + title="Cricket Pose Estimator" + attribution="(Image credit: connect.vin)" + attributionlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" +>}} ### The Challenges @@ -157,8 +157,8 @@ hidden parameters, patterns, and attributes that lead to the outcome of a cricket match helps the stakeholders to take notice of game insights that are otherwise hidden in numbers and statistics. -{{< figure >}} -src = '/images/content_images/cs/numpy_ca_benefits.png' -alt = 'Diagram showing benefits of using NumPy for cricket analytics' -title = 'Key NumPy Capabilities utilized' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_ca_benefits.png" + alt="Diagram showing benefits of using NumPy for cricket analytics" + title="Key NumPy Capabilities utilized" +>}} diff --git a/content/en/case-studies/deeplabcut-dnn.md b/content/en/case-studies/deeplabcut-dnn.md index c499bd3a25..fabf621991 100644 --- a/content/en/case-studies/deeplabcut-dnn.md +++ b/content/en/case-studies/deeplabcut-dnn.md @@ -3,13 +3,13 @@ title: "Case Study: DeepLabCut 3D Pose Estimation" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/mice-hand.gif' -title = 'Analyzing mice hand-movement using DeepLapCut' -alt = 'micehandanim' -attribution = '(Source: www.deeplabcut.org )' -attributionlink = 'http://www.mousemotorlab.org/deeplabcut' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/mice-hand.gif" + title="Analyzing mice hand-movement using DeepLapCut" + alt="micehandanim" + attribution="(Source: www.deeplabcut.org )" + attributionlink="http://www.mousemotorlab.org/deeplabcut" +>}} {{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" @@ -24,12 +24,12 @@ Open Source Software is accelerating Biomedicine. DeepLabCut enables automated v Several areas of research, including neuroscience, medicine, and biomechanics, use data from tracking animal movement. DeepLabCut helps in understanding what humans and other animals are doing by parsing actions that have been recorded on film. Using automation for laborious tasks of tagging and monitoring, along with deep neural network based data analysis, DeepLabCut makes scientific studies involving observing animals, such as primates, mice, fish, flies etc., much faster and more accurate. -{{< figure >}} -src = '/images/content_images/cs/race-horse.gif' -title = 'Colored dots track the positions of a racehorse’s body part' -alt = 'horserideranim' -attribution = '(Source: Mackenzie Mathis)' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/race-horse.gif" + title="Colored dots track the positions of a racehorse’s body part" + alt="horserideranim" + attribution="(Source: Mackenzie Mathis)" +>}} DeepLabCut's non-invasive behavioral tracking of animals by extracting the poses of animals is crucial for scientific pursuits in domains such as biomechanics, genetics, ethology & neuroscience. Measuring animal poses non-invasively from video - without markers - in dynamically changing backgrounds is computationally challenging, both technically as well as in terms of resource needs and training data required. @@ -72,14 +72,14 @@ Recently, the [DeepLabCut model zoo](https://deeplabcut.github.io/DeepLabCut/doc - code for large-scale inference on videos - draw inferences using integrated visualization tools -{{< figure >}} -src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' -title = 'Pose estimation steps with DeepLabCut' -alt = 'dlcsteps' -align = 'center' -attribution = '(Source: DeepLabCut)' -attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/deeplabcut-toolkit-steps.png" + title="Pose estimation steps with DeepLabCut" + alt="dlcsteps" + align="center" + attribution="(Source: DeepLabCut)" + attributionlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" +>}} [DLCToolkit]: https://github.com/DeepLabCut/DeepLabCut @@ -110,14 +110,14 @@ attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/pho arrays corresponding to various images, target tensors and keypoints is fairly challenging. -{{< figure >}} -src = '/images/content_images/cs/pose-estimation.png' -title = 'Pose estimation variety and complexity' -alt = 'challengesfig' -align = 'center' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/pose-estimation.png" + title="Pose estimation variety and complexity" + alt="challengesfig" + align="center" + attribution="(Source: Mackenzie Mathis)" + attributionlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" +>}} ## NumPy's Role in meeting Pose Estimation Challenges @@ -155,13 +155,13 @@ training fast, NumPy’s vectorization capabilities are leveraged. For inference the most likely predictions from target scoremaps need to extracted and one needs to efficiently “link predictions to assemble individual animals”. -{{< figure >}} -src = '/images/content_images/cs/deeplabcut-workflow.png' -title = 'DeepLabCut Workflow' -alt = 'workflow' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/deeplabcut-workflow.png" + title="DeepLabCut Workflow" + alt="workflow" + attribution="(Source: Mackenzie Mathis)" + attributionlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962" +>}} ## Summary @@ -177,8 +177,8 @@ medicine and rehabilitation studies. Complex combinatorics, data processing challenges faced by DeepLabCut algorithms are addressed through the use of NumPy's array manipulation capabilities. -{{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_dlc_benefits.png" + alt="numpy benefits" + title="Key NumPy Capabilities utilized" +>}} diff --git a/content/en/case-studies/gw-discov.md b/content/en/case-studies/gw-discov.md index ead650f14c..2a6b719514 100644 --- a/content/en/case-studies/gw-discov.md +++ b/content/en/case-studies/gw-discov.md @@ -3,13 +3,13 @@ title: "Case Study: Discovery of Gravitational Waves" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/gw_sxs_image.png' -title = 'Gravitational Waves' -alt = 'binary coalesce black hole generating gravitational waves' -attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' -attributionlink = 'https://youtu.be/Zt8Z_uzG71o' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/gw_sxs_image.png" + title="Gravitational Waves" + alt="binary coalesce black hole generating gravitational waves" + attribution="(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)" + attributionlink="https://youtu.be/Zt8Z_uzG71o" +>}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" @@ -94,13 +94,13 @@ made from warped spacetime. simulations using latest experimental inputs and insights can be a time consuming activity that challenges researchers in this domain. -{{< figure >}} -src = '/images/content_images/cs/gw_strain_amplitude.png' -alt = 'gravitational waves strain amplitude' -title = 'Estimated gravitational-wave strain amplitude from GW150914' -attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' -attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/gw_strain_amplitude.png" + alt="gravitational waves strain amplitude" + title="Estimated gravitational-wave strain amplitude from GW150914" + attribution="(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)" + attributionlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger" +>}} ## NumPy’s Role in the Detection of Gravitational Waves @@ -132,19 +132,19 @@ speed. Here are some examples: providing object based interfaces to utilities, tools, and methods for studying data from gravitational-wave detectors. -{{< figure >}} -src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' -alt = 'gwpy-numpy depgraph' -title = 'Dependency graph showing how GwPy package depends on NumPy' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/gwpy-numpy-dep-graph.png" + alt="gwpy-numpy depgraph" + title="Dependency graph showing how GwPy package depends on NumPy" +>}} ---- -{{< figure >}} -src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' -alt = 'PyCBC-numpy depgraph' -title = 'Dependency graph showing how PyCBC package depends on NumPy' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/PyCBC-numpy-dep-graph.png" + alt="PyCBC-numpy depgraph" + title="Dependency graph showing how PyCBC package depends on NumPy" +>}} ## Summary @@ -160,8 +160,8 @@ is [enabling researchers](https://www.gw-openscience.org/events/GW150914/) to answer complex questions and discover new horizons in our understanding of the universe. -{{< figure >}} -src = '/images/content_images/cs/numpy_gw_benefits.png' -alt = 'numpy benefits' -title = 'Key NumPy Capabilities utilized' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_gw_benefits.png" + alt="numpy benefits" + title="Key NumPy Capabilities utilized" +>}} diff --git a/content/en/teams/docs-team.toml b/content/en/teams/docs-team.toml index 09abff8aa8..7d453fbffc 100644 --- a/content/en/teams/docs-team.toml +++ b/content/en/teams/docs-team.toml @@ -1,69 +1,69 @@ [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&v=4"' -alt = 'Avatar of Rohit Goswami' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/4336207?u=564d623a8c9d710c3520841b83458b0bf1eae010&v=4\"" + alt="Avatar of Rohit Goswami" +>}} Rohit Goswami''' link = 'https://github.com/HaoZeke' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4"' -alt = 'Avatar of Inessa Pawson' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/43481325?u=8c0c0adbf3f2efd2cce72951d3554064c7bbfce3&v=4\"" + alt="Avatar of Inessa Pawson" +>}} Inessa Pawson''' link = 'https://github.com/InessaPawson' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&v=4"' -alt = 'Avatar of Mars Lee' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/46167686?u=b5ca05a767012822d06b8bc16e3cd5ca0d1cafe9&v=4\"" + alt="Avatar of Mars Lee" +>}} Mars Lee''' link = 'https://github.com/MarsBarLee' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&v=4"' -alt = 'Avatar of Matti Picus' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/823911?u=1dd52e6dcca6a7a35b6644935cdd33a6e166a596&v=4\"" + alt="Avatar of Matti Picus" +>}} Matti Picus''' link = 'https://github.com/mattip' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&v=4"' -alt = 'Avatar of Melissa Weber Mendonça' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/3949932?u=aacac68df60d2cf64c17c7e5aa17adf8b738aa7b&v=4\"" + alt="Avatar of Melissa Weber Mendonça" +>}} Melissa Weber Mendonça''' link = 'https://github.com/melissawm' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&v=4"' -alt = 'Avatar of Mukulika' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/60316606?u=229ba03253068b0a4f206b0be08f7a9e76c832f1&v=4\"" + alt="Avatar of Mukulika" +>}} Mukulika''' link = 'https://github.com/Mukulikaa' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4"' -alt = 'Avatar of Ross Barnowski' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/1268991?u=974707b96081a9705f3a239c0773320f353ee02f&v=4\"" + alt="Avatar of Ross Barnowski" +>}} Ross Barnowski''' link = 'https://github.com/rossbar' diff --git a/content/en/teams/emeritus-maintainers.toml b/content/en/teams/emeritus-maintainers.toml index b61eef1f2f..d030da984a 100644 --- a/content/en/teams/emeritus-maintainers.toml +++ b/content/en/teams/emeritus-maintainers.toml @@ -1,89 +1,89 @@ [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/9040124?v=4"' -alt = 'Avatar of Allan Haldane' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/9040124?v=4\"" + alt="Avatar of Allan Haldane" +>}} Allan Haldane''' link = 'https://github.com/ahaldane' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/20568?v=4"' -alt = 'Avatar of Ondřej Čertík' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/20568?v=4\"" + alt="Avatar of Ondřej Čertík" +>}} Ondřej Čertík''' link = 'https://github.com/certik' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/25111?v=4"' -alt = 'Avatar of David Cournapeau' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/25111?v=4\"" + alt="Avatar of David Cournapeau" +>}} David Cournapeau''' link = 'https://github.com/cournape' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/3343990?v=4"' -alt = 'Avatar of Jaime' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/3343990?v=4\"" + alt="Avatar of Jaime" +>}} Jaime''' link = 'https://github.com/jaimefrio' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/123428?v=4"' -alt = 'Avatar of Jarrod Millman' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/123428?v=4\"" + alt="Avatar of Jarrod Millman" +>}} Jarrod Millman''' link = 'https://github.com/jarrodmillman' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 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src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Avatar of Yeimi Pena" +>}} Yeimi Pena (@yeimiyaz)''' link = 'https://www.linkedin.com/in/yeimipena/' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/3813847?v=4' -alt = 'Avatar of Atsushi Sakai' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/3813847?v=4" + alt="Avatar of Atsushi Sakai" +>}} Atsushi Sakai (@AtsushiSakai)''' link = 'https://github.com/AtsushiSakai' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' -alt = 'Avatar of Getúlio Silva' -{{< /image >}} +body = '''{{< image + src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Avatar of Getúlio Silva" +>}} Getúlio Silva (@getuliosilva)''' link = 'https://scientific-python.crowdin.com' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' -alt = 'Oriol Abril-Pla' -{{< /image >}} +body = '''{{< image + src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Oriol Abril-Pla" +>}} Oriol Abril-Pla (@OriolAbril)''' link = 'https://scientific-python.crowdin.com' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' -alt = 'Avatar of @julio' -{{< /image >}} +body = '''{{< image + src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Avatar of @julio" +>}} @julio''' link = 'https://scientific-python.crowdin.com' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' -alt = 'Avatar of Ali Faraji' -{{< /image >}} +body = '''{{< image + src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Avatar of Ali Faraji" +>}} Ali Faraji (@ali)''' link = 'https://scientific-python.crowdin.com' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' -alt = 'Avatar of Saeed Foroutan' -{{< /image >}} +body = '''{{< image + src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Avatar of Saeed Foroutan" +>}} Saeed Foroutan (@SaeedForoutan)''' link = 'https://scientific-python.crowdin.com' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg' -alt = 'Avatar of @pyjavo' -{{< /image >}} +body = '''{{< image + src="https://raw.githubusercontent.com/numpy/numpy.org/refs/heads/main/static/images/logo.svg" + alt="Avatar of @pyjavo" +>}} @pyjavo''' link = 'https://scientific-python.crowdin.com' diff --git a/content/en/teams/triage-team.toml b/content/en/teams/triage-team.toml index 8bfddc6481..ea8d74d476 100644 --- a/content/en/teams/triage-team.toml +++ b/content/en/teams/triage-team.toml @@ -1,279 +1,279 @@ [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&v=4"' -alt = 'Avatar of Andrew Nelson' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/702934?u=a026c1b1117981cea46e56ba562f3e80dfa71329&v=4\"" + alt="Avatar of Andrew Nelson" +>}} Andrew Nelson''' link = 'https://github.com/andyfaff' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/1522319?v=4"' -alt = 'Avatar of Anirudh Subramanian' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/1522319?v=4\"" + alt="Avatar of Anirudh Subramanian" +>}} Anirudh Subramanian''' link = 'https://github.com/anirudh2290' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/71486?u=cc88e2a4e4c6c496dcb9dd88cead5c0dab496c89&v=4"' -alt = 'Avatar of Aaron Meurer' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/71486?u=cc88e2a4e4c6c496dcb9dd88cead5c0dab496c89&v=4\"" + alt="Avatar of Aaron Meurer" +>}} Aaron Meurer''' link = 'https://github.com/asmeurer' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/3813847?v=4"' -alt = 'Avatar of Atsushi Sakai' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/3813847?v=4\"" + alt="Avatar of Atsushi Sakai" +>}} Atsushi Sakai''' link = 'https://github.com/AtsushiSakai' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/6691888?v=4"' -alt = 'Avatar of Ben Nathanson' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/6691888?v=4\"" + alt="Avatar of Ben Nathanson" +>}} Ben Nathanson''' link = 'https://github.com/bjnath' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/35413198?u=e67bd9ebc361fb207f914979d935fd1956eb626c&v=4"' -alt = 'Avatar of Anne Bonner' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/35413198?u=e67bd9ebc361fb207f914979d935fd1956eb626c&v=4\"" + alt="Avatar of Anne Bonner" +>}} Anne Bonner''' link = 'https://github.com/bonn0062' [[item]] type = 'card' classcard 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-{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/8078968?v=4\"" + alt="Avatar of jbrockmendel" +>}} jbrockmendel''' link = 'https://github.com/jbrockmendel' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/30074037?u=c2549c85c82266302c71aef5c20446871323d91b&v=4"' -alt = 'Avatar of Kai' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/30074037?u=c2549c85c82266302c71aef5c20446871323d91b&v=4\"" + alt="Avatar of Kai" +>}} Kai''' link = 'https://github.com/Kai-Striega' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/16046705?u=1bf01e87adb556503c1fe07789c194cc04d38490&v=4"' -alt = 'Avatar of Yuji Kanagawa' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/16046705?u=1bf01e87adb556503c1fe07789c194cc04d38490&v=4\"" + alt="Avatar of Yuji 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'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&v=4"' -alt = 'Avatar of Ralf Gommers' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/98330?u=22a023f8d191ba200ab13d476c83860d015cc9fe&v=4\"" + alt="Avatar of Ralf Gommers" +>}} Ralf Gommers''' link = 'https://github.com/rgommers' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&v=4"' -alt = 'Avatar of shalz' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/5890484?u=feb15a24e010a434ded00e41d8bd030a2cc31bdb&v=4\"" + alt="Avatar of shalz" +>}} shalz''' link = 'https://github.com/shaloo' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/5774448?u=af1d8beea7d3c37d064e0dcb42d96c41e1318934&v=4"' -alt = 'Avatar of Shekhar Prasad Rajak' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/5774448?u=af1d8beea7d3c37d064e0dcb42d96c41e1318934&v=4\"" + alt="Avatar of Shekhar Prasad Rajak" +>}} Shekhar Prasad Rajak''' link = 'https://github.com/Shekharrajak' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&v=4"' -alt = 'Avatar of Stefan van der Walt' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/45071?u=c779b5e06448fbc638bc987cdfe305c7f9a7175e&v=4\"" + alt="Avatar of Stefan van der Walt" +>}} Stefan van der Walt''' link = 'https://github.com/stefanv' [[item]] type = 'card' classcard = 'text-center' -body = '''{{< image >}} -src = 'https://avatars.githubusercontent.com/u/1953382?u=5df9d41ad2a6d526e7daeec06225274905e7e660&v=4"' -alt = 'Avatar of Albert Steppi' -{{< /image >}} +body = '''{{< image + src="https://avatars.githubusercontent.com/u/1953382?u=5df9d41ad2a6d526e7daeec06225274905e7e660&v=4\"" + alt="Avatar of Albert Steppi" +>}} Albert Steppi''' link = 'https://github.com/steppi' diff --git a/content/en/user-survey-2020.md b/content/en/user-survey-2020.md index d82c32251d..13d9599add 100644 --- a/content/en/user-survey-2020.md +++ b/content/en/user-survey-2020.md @@ -10,11 +10,11 @@ community survey. Over 1,200 users from 75 countries participated to help us map out a landscape of the NumPy community and voiced their thoughts about the future of the project. -{{< figure >}} -src = '/surveys/NumPy_usersurvey_2020_report_cover.png' -alt = 'Cover page of the 2020 NumPy user survey report, titled "NumPy Community Survey 2020 - results"' -width = '250' -{{< /figure >}} +{{< figure + src="/surveys/NumPy_usersurvey_2020_report_cover.png" + alt="Cover page of the 2020 NumPy user survey report, titled 'NumPy Community Survey 2020 - results'" + width=250 +>}} **[Download the report](/surveys/NumPy_usersurvey_2020_report.pdf)** to take a closer look at the survey findings. diff --git a/content/es/case-studies/blackhole-image.md b/content/es/case-studies/blackhole-image.md index 57a3be4196..ff3bd9996b 100644 --- a/content/es/case-studies/blackhole-image.md +++ b/content/es/case-studies/blackhole-image.md @@ -3,13 +3,13 @@ title: "Caso de estudio: La primera imagen de un Agujero Negro" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/blackhole.jpg' -title = 'Agujero Negro M87' -alt = 'Imagen de agujero negro' -attribution = '(Créditos de la imagen: Colaboración del telescopio del Horizonte de Sucesos)' -attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/blackhole.jpg" + title="Agujero Negro M87" + alt="Imagen de agujero negro" + attribution="(Créditos de la imagen: Colaboración del telescopio del Horizonte de Sucesos)" + attributionlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" +>}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="Katie Bouman, *Profesora Asistente, Ciencias de la Computación & Matemáticas, Caltech*" >}} @@ -42,14 +42,14 @@ El [ Telescopio Event Horizon (EHT) ](https://eventhorizontelescope.org), es un Cuando el objetivo es ver algo nunca antes visto, ¿cómo pueden los científicos estar seguros de que la imagen es correcta? -{{< figure >}} -src = '/images/content_images/cs/dataprocessbh.png' -title = 'Flujo de Trabajo de Procesamiento de Datos EHT' -alt = 'flujo de datos' -align = 'center' -attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' -attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/dataprocessbh.png" + title="Flujo de Trabajo de Procesamiento de Datos EHT" + alt="flujo de datos" + align="center" + attribution="(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)" + attributionlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" +>}} ## El Rol de NumPy @@ -59,19 +59,19 @@ La colaboración del EHT respondió a estos desafíos haciendo que los equipos i Su trabajo ilustra el rol que desempeña el ecosistema científico de Python en el avance de la ciencia a través del análisis de datos colaborativos. -{{< figure >}} -src = '/images/content_images/cs/bh_numpy_role.png' -alt = 'rol de numpy' -title = 'El rol de NumPy en la imagen del agujero negro' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/bh_numpy_role.png" + alt="rol de numpy" + title="El rol de NumPy en la imagen del agujero negro" +>}} Por ejemplo, el paquete de Python [`eht-imaging`][ehtim] proporciona herramientas para simular y realizar reconstrucción de imágenes en datos VLBI. NumPy está en el núcleo del procesamiento de datos de matrices utilizados en este paquete, como se muestra a continuación en el gráfico parcial de dependencias de software. -{{< figure >}} -src = '/images/content_images/cs/ehtim_numpy.png' -alt = 'mapa de dependencias de ehtim resaltando a numpy' -title = 'Gráfico de dependencias de software del paquete ehtim resaltando a NumPy' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/ehtim_numpy.png" + alt="mapa de dependencias de ehtim resaltando a numpy" + title="Gráfico de dependencias de software del paquete ehtim resaltando a NumPy" +>}} Además de NumPy, muchos otros paquetes, como [SciPy](https://scipy.org) y [Pandas](https://pandas.pydata.org), son parte del flujo de procesamiento de datos para fotografiar el agujero negro. Los formatos estándar de archivos astronómicos y transformaciones de tiempo/coordenadas fueron manejados por [Astropy][astropy], mientras que [Matplotlib][mpl] fue utilizado en la visualización de datos a través del flujo de análisis, incluyendo la generación de la imagen final del agujero negro. @@ -79,11 +79,11 @@ Además de NumPy, muchos otros paquetes, como [SciPy](https://scipy.org) y [Pand El eficiente y adaptable arreglo n-dimensional que es la característica central de NumPy, permitió a los investigadores manipular grandes conjuntos de datos numéricos, proporcionando una base para la primera imagen de un agujero negro. Un momento histórico en la ciencia ofrece una impresionante evidencia visual de la teoría de Einstein. Este logro abarca no solo los avances tecnológicos sino también la colaboración internacional de más de 200 científicos y algunos de los mejores radio observatorios del mundo. Algoritmos innovadores y técnicas de procesamiento de datos, mejorando los modelos astronómicos existentes, ayudaron a desvelar un misterio del universo. -{{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' -alt = 'beneficios de numpy' -title = 'Capacidades clave de NumPy utilizadas' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_dlc_benefits.png" + alt="beneficios de numpy" + title="Capacidades clave de NumPy utilizadas" +>}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole diff --git a/content/es/case-studies/cricket-analytics.md b/content/es/case-studies/cricket-analytics.md index d19419ccdd..d91ac6e53f 100644 --- a/content/es/case-studies/cricket-analytics.md +++ b/content/es/case-studies/cricket-analytics.md @@ -3,13 +3,13 @@ title: "Estudio de caso: Análisis de críquet, ¡el cambio radical!" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/ipl-stadium.png' -title = 'IPLT20, el festival de críquet más grande en India' -alt = 'Copa y estadio de la Premier League de Críquet de India' -attribution = '(Créditos de imagen: IPLT20 (copa y logo) & Akash Yadav (estadio))' -attributionlink = 'https://unsplash.com/@aksh1802' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/ipl-stadium.png" + title="IPLT20, el festival de críquet más grande en India" + alt="Copa y estadio de la Premier League de Críquet de India" + attribution="(Créditos de imagen: IPLT20 (copa y logo) & Akash Yadav (estadio))" + attributionlink="https://unsplash.com/@aksh1802" +>}} {{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" by="M S Dhoni, *Jugador Internacional de críquet, ex-capitán del equipo de India, juega para Chennai Super Kings en IPL*" >}} No juegas para el público, juegas para el país. @@ -30,14 +30,14 @@ Hoy en día, hay abundantes y casi infinitos tesoros de registros y estadística * obtener información sobre la condición física y el rendimiento de un jugador contra diferentes oponentes, * contribución del jugador a las victorias y derrotas para tomar decisiones estratégicas sobre la composición del equipo -{{< figure >}} -src = '/images/content_images/cs/cricket-pitch.png' -title = 'El campo de críquet, el punto focal en el terreno de juego' -alt = 'Un campo de cricket con lanzador y bateadores' -align = 'center' -attribution = '(Image credit: Debarghya Das)' -attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/cricket-pitch.png" + title="El campo de críquet, el punto focal en el terreno de juego" + alt="Un campo de cricket con lanzador y bateadores" + align="center" + attribution="(Image credit: Debarghya Das)" + attributionlink="http://debarghyadas.com/files/IPLpaper.pdf" +>}} ### Objetivos Clave de Análisis de Datos @@ -45,13 +45,13 @@ attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' * El análisis de datos en tiempo real puede ayudar a obtener información incluso durante el juego para cambiar tácticas por parte del equipo y de las empresas asociadas para beneficios económicos y crecimiento. * Además del análisis histórico, se aprovechan los modelos predictivos para determinar los posibles resultados de los partidos, lo cual requiere una cantidad significativa de procesamiento de datos y conocimientos de ciencia de datos, herramientas de visualización y la capacidad de incluir nuevas observaciones en el análisis. -{{< figure >}} -src = '/images/content_images/cs/player-pose-estimator.png' -alt = 'estimador de postura' -title = 'Estimador de postura en críquet' -attribution = '(Crédito de imagen: connect.vin)' -attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/player-pose-estimator.png" + alt="estimador de postura" + title="Estimador de postura en críquet" + attribution="(Crédito de imagen: connect.vin)" + attributionlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" +>}} ### Los Desafíos @@ -79,8 +79,8 @@ El análisis deportivo es un campo en desarrollo. Muchos investigadores y compa El análisis deportivo ha revolucionado la forma en que se juegan los partidos profesionales, especialmente en cuanto a la toma de decisiones estratégicas, que hasta hace poco se basaba principalmente en la "intuición" o en la adherencia a tradiciones pasadas. NumPy constituye una base sólida para un gran conjunto de paquetes de Python que brindan funciones de nivel superior relacionadas con análisis de datos, el aprendizaje automático y los algoritmos de IA. Estos paquetes están ampliamente desplegados para obtener información en tiempo real que ayudan en la toma de decisiones para resultados revolucionarios, tanto en el campo como para sacar conclusiones y hacer negocios alrededor del juego del críquet. Encontrar los parámetros ocultos, patrones y atributos que conducen al resultado de un partido de críquet ayuda a los interesados a tomar nota de la información del juego que de otra forma estarían ocultos en números y estadísticas. -{{< figure >}} -src = '/images/content_images/cs/numpy_ca_benefits.png' -alt = 'Diagrama que muestra los beneficios de usar NumPy para análisis de críquet' -title = 'Capacidades claves de NumPy utilizadas' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_ca_benefits.png" + alt="Diagrama que muestra los beneficios de usar NumPy para análisis de críquet" + title="Capacidades claves de NumPy utilizadas" +>}} diff --git a/content/es/case-studies/deeplabcut-dnn.md b/content/es/case-studies/deeplabcut-dnn.md index 1281521f73..71516a828e 100644 --- a/content/es/case-studies/deeplabcut-dnn.md +++ b/content/es/case-studies/deeplabcut-dnn.md @@ -3,13 +3,13 @@ title: "Caso de estudio: DeepLabCut Estimación de Postura 3D" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/mice-hand.gif' -title = 'Analizar movimiento de las manos de los ratones usando DeepLapCut' -alt = 'micehandanim' -attribution = '(Fuente: www.deeplabcut.org )' -attributionlink = 'http://www.mousemASElab.org/deeplabcut' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/mice-hand.gif" + title="Analizar movimiento de las manos de los ratones usando DeepLapCut" + alt="micehandanim" + attribution="(Fuente: www.deeplabcut.org)" + attributionlink="http://www.mousemASElab.org/deeplabcut" +>}} {{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" by="Alexander Mathis, *Profesor Asistente, Escuela Politécnica Federal de Lausana* ([EPFL](https://www.epfl.ch/en/))" >}} El software de código abierto está acelerando la biomedicina. DeepLabCut permite el análisis automatizado de video del comportamiento animal utilizando Aprendizaje Profundo. @@ -21,12 +21,12 @@ attributionlink = 'http://www.mousemASElab.org/deeplabcut' Muchas áreas de investigación, incluyendo la neurociencia, la medicina y la biomecánica, utilizan datos para rastrear el movimiento animal. DeepLabCut ayuda a entender lo que los humanos y otros animales están haciendo, analizando las acciones que han sido grabadas en la filmación. Utilizando la automatización para tareas laboriosas de etiquetado y monitoreo, junto con el análisis de datos basado en redes neuronales profundas, DeepLabCut realiza estudios científicos que involucran la observación de animales, tales como primates, ratones, peces, moscas, etc. de manera mucho más rápida y precisa. -{{< figure >}} -src = '/images/content_images/cs/race-horse.gif' -title = 'Puntos de colores rastrean las posiciones de una parte del cuerpo de un caballo de carreras' -alt = 'horserideranim' -attribution = '(Fuente: Mackenzie Mathis)' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/race-horse.gif" + title="Puntos de colores rastrean las posiciones de una parte del cuerpo de un caballo de carreras" + alt="horserideranim" + attribution="(Fuente: Mackenzie Mathis)" +>}} El rastreo del comportamiento no invasivo de animales de DeepLabCut por medio de la extracción de posturas de animales es crucial para propósitos científicos en dominios tales como la biomecánica, genética, etología y & neurociencia. Medir las poses de animales de manera no invasiva a partir de video - sin marcadores - en fondos que cambian dinámicamente es un desafío computacional, tanto técnicamente como en términos de necesidades de recursos y datos de entrenamiento requeridos. @@ -55,14 +55,14 @@ Recientemente, se presentó el [modelo zoo de DeepLabCut](http://www.mousemotorl - código para inferencia a gran escala en videos - graficar las inferencias utilizando herramientas de visualización integradas -{{< figure >}} -src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' -title = 'Pasos de estimación de la postura con DeepLabCut' -alt = 'dlcsteps' -align = 'center' -attribution = '(Source: DeepLabCut)' -attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/deeplabcut-toolkit-steps.png" + title="Pasos de estimación de la postura con DeepLabCut" + alt="dlcsteps" + align="center" + attribution="(Source: DeepLabCut)" + attributionlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" +>}} ### Los Desafíos @@ -78,14 +78,14 @@ attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/pho Por último, pero no menos importante, la manipulación de arreglos - procesamiento de grandes pilas de arreglos correspondientes a varias imágenes, tensores objetivo y puntos clave es bastante desafiante. -{{< figure >}} -src = '/images/content_images/cs/pose-estimation.png' -title = 'Estimación de variedad y complejidad de postura' -alt = 'challengesfig' -align = 'center' -attribution = '(Fuente: Mackenzie Mathis)' -attributionlink = 'https://www.iorxiv.org/content/10.1101/476531v1.full.pdf' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/pose-estimation.png" + title="Estimación de variedad y complejidad de postura" + alt="challengesfig" + align="center" + attribution="(Fuente: Mackenzie Mathis)" + attributionlink="https://www.iorxiv.org/content/10.1101/476531v1.full.pdf" +>}} ## El Papel de NumPy para afrontar los desafíos de la estimación de postura @@ -101,23 +101,23 @@ Las siguientes características de NumPy jugaron un papel clave en abordar el pr DeepLabCut utiliza las capacidades de arreglos de NumPy a lo largo del flujo de trabajo ofrecido por el conjunto de herramientas. En particular, NumPy se utiliza para muestrear diferentes fotogramas para etiquetado de anotaciones humanas, y para escribir, editar y procesar datos de anotación. Dentro de TensorFlow, la red neuronal es entrenada por la tecnología DeepLabCut durante miles de iteraciones para predecir las anotaciones de referencia a partir de fotogramas. Para este propósito, se crean densidades objetivo (mapas de puntuación) para plantear la estimación de poses como un problema de traducción de imagen a imagen. Para hacer que las redes neuronales sean robustas, se emplea el aumento de datos, lo que requiere el cálculo de mapas de puntuación objetivo sujetos a varios pasos geométricos y de procesamiento de imágenes. Para hacer que el entrenamiento sea rápido, se aprovechan las capacidades de vectorización de NumPy. Para la inferencia, es necesario extraer las predicciones más probables de los mapas de puntuación objetivo y "vincular eficientemente las predicciones para ensamblar animales individuales". -{{< figure >}} -src = '/images/content_images/cs/deeplabcut-workflow.png' -title = 'Flujo de Trabajo de DeepLabCut' -alt = 'flujo de trabajo' -attribution = '(Fuente: Mackenzie Mathis)' -attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/deeplabcut-workflow.png" + title="Flujo de Trabajo de DeepLabCut" + alt="flujo de trabajo" + attribution="(Fuente: Mackenzie Mathis)" + attributionlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962" +>}} ## Resumen Observar y describir eficientemente el comportamiento es un punto central de la etología moderna, neurociencia, medicina y tecnología. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) permite a los investigadores estimar la postura del sujeto, permitiéndoles de manera eficiente cuantificar el comportamiento. Con solo un pequeño conjunto de imágenes de entrenamiento, el conjunto de herramientas de Python de DeepLabCut permite entrenar una red neuronal con una precisión de etiquetado a nivel humano, expandiendo así su aplicación no solo al análisis del comportamiento en el laboratorio, sino también potencialmente en deportes, análisis de marcha, medicina y estudios de rehabilitación. Los desafíos de la combinatoria compleja y procesamiento de datos enfrentados por los algoritmos de DeepLabCut se abordan mediante el uso de las capacidades de manipulación de arreglos de NumPy. -{{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' -alt = 'beneficios de NumPy' -title = 'Capacidades claves utilizadas de NumPy' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_dlc_benefits.png" + alt="beneficios de NumPy" + title="Capacidades claves utilizadas de NumPy" +>}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 diff --git a/content/es/case-studies/gw-discov.md b/content/es/case-studies/gw-discov.md index a812b08da2..c0ee5c1f0a 100644 --- a/content/es/case-studies/gw-discov.md +++ b/content/es/case-studies/gw-discov.md @@ -3,13 +3,13 @@ title: "Estudio de Caso: Descubrimiento de Ondas Gravitacionales" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/gw_sxs_image.png' -title = 'Ondas Gravitacionales' -alt = 'coalescencia de un agujero negro binario generando ondas gravitacionales' -attribution= '(Créditos de imagen: El proyecto Simulación de Espacio-tiempos eXtreme (SXS) en LIGO)' -attributionlink = 'https://youtu.be/Zt8Z_uzG71o' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/gw_sxs_image.png" + title="Ondas Gravitacionales" + alt="coalescencia de un agujero negro binario generando ondas gravitacionales" + attribution="(Créditos de imagen: El proyecto Simulación de Espacio-tiempos eXtreme (SXS) en LIGO)" + attributionlink="https://youtu.be/Zt8Z_uzG71o" +>}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *Colaboración científica LIGO*" >}} El ecosistema científico de Python es una infraestructura crítica para la investigación realizada en LIGO. {{< /blockquote >}} @@ -43,13 +43,13 @@ El [Observatorio de Ondas Gravitacionales por Interferometría Láser (LIGO)](ht Una vez superados los obstáculos relacionados con comprender suficientemente bien las ecuaciones de Einstein para resolverlas utilizando supercomputadoras, el siguiente gran desafío fue hacer que los datos fueran comprensibles para el cerebro humano. La modelación de simulación, así como la detección de señales, requieren técnicas de visualización efectivas. La visualización también desempeña un papel en otorgar más credibilidad a la relatividad numérica a los ojos de los aficionados a la ciencia pura, los cuales no le daban suficiente importancia a la relatividad numérica hasta que las imágenes y simulaciones facilitaron la comprensión de los resultados para un público más amplio. La velocidad de los cálculos complejos y la renderización, así como la re-renderización de imágenes y simulaciones utilizando los últimos datos experimentales y conocimientos, puede ser una actividad que consume mucho tiempo y que representa un desafío para los investigadores en este campo. -{{< figure >}} -src = '/images/content_images/cs/gw_strain_amplitude.png' -alt = 'amplitud de deformación de ondas gravitacionales' -title = 'Amplitud de deformación de ondas gravitacionales estimada de GW150914' -attribution = '(Créditos del gráfico: Observación de Ondas Gravitacionales de la Fusión de un Agujero Negro Binario, Publicación de ResearchGate)' -attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/gw_strain_amplitude.png" + alt="amplitud de deformación de ondas gravitacionales" + title="Amplitud de deformación de ondas gravitacionales estimada de GW150914" + attribution="(Créditos del gráfico: Observación de Ondas Gravitacionales de la Fusión de un Agujero Negro Binario, Publicación de ResearchGate)" + attributionlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger" +>}} ## El Papel de NumPy en la Detección de Ondas Gravitacionales @@ -66,26 +66,26 @@ NumPy, el paquete de análisis numérico estándar para Python, fue utilizado po * Cálculo de Correlaciones * [Software clave](https://github.com/lscsoft) desarrollado en análisis de datos de Ondas Gravitacionales como, tales como [GwPy](https://gwpy.github.io/docs/stable/overview.html) y [PyCBC](https://pycbc.org) utiliza NumPy y AstroPy bajo su cubierta para proporcionar interfaces basadas en objetos a utilidades, herramientas y métodos para el estudio de datos provenientes de detectores de ondas gravitacionales. -{{< figure >}} -src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' -alt = 'gwpy-numpy depgraph' -title = 'Gráfico de dependencias que muestra cómo depende el paquete GwPy de NumPy' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/gwpy-numpy-dep-graph.png" + alt="gwpy-numpy depgraph" + title="Gráfico de dependencias que muestra cómo depende el paquete GwPy de NumPy" +>}} ---- -{{< figure >}} -src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' -alt = 'PyCBC-numpy depgraph' -title = 'Gráfico de dependencias que muestra cómo el paquete PyCBC depende de NumPy' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/PyCBC-numpy-dep-graph.png" + alt="PyCBC-numpy depgraph" + title="Gráfico de dependencias que muestra cómo el paquete PyCBC depende de NumPy" +>}} ## Resumen La detección de ondas gravitacionales ha permitido a los investigadores descubrir fenómenos completamente inesperados, al tiempo que proporciona nuevos conocimientos sobre muchos de los fenómenos astrofísicos más profundos conocidos. El procesamiento de datos y la visualización de datos son pasos cruciales que ayudan a los científicos a obtener información a partir de los datos recopilados en las observaciones científicas y a comprender los resultados. Los cálculos son complejos y no pueden ser comprendidos por humanos, a menos que sean visualizados utilizando simulaciones por computador que se alimenten con datos y análisis reales observados. NumPy, junto con otros paquetes de Python como matplotlib, pandas y scikit-learn, está [permitiendo a los investigadores](https://www.gw-openscience.org/events/GW150914/) responder preguntas complejas y descubrir nuevos horizontes en nuestra comprensión del universo. -{{< figure >}} -src = '/images/content_images/cs/numpy_gw_benefits.png' -alt = 'beneficios de NumPy' -title = 'Capacidades clave de NumPy utilizadas' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_gw_benefits.png" + alt="beneficios de NumPy" + title="Capacidades clave de NumPy utilizadas" +>}} diff --git a/content/es/user-survey-2020.md b/content/es/user-survey-2020.md index bf1f2f203e..84f9cfa979 100644 --- a/content/es/user-survey-2020.md +++ b/content/es/user-survey-2020.md @@ -5,12 +5,12 @@ sidebar: false En 2020, el equipo de encuestas de NumPy, en asociación con estudiantes y profesores de un curso de Maestría en Metodología de Encuestas organizado conjuntamente por la Universidad de Michigan y la Universidad de Maryland, llevaron a cabo la primera encuesta oficial de la comunidad NumPy. Más de 1,200 usuarios de 75 países participaron para ayudarnos a proyectar un panorama de la comunidad NumPy y expresaron sus pensamientos sobre el futuro del proyecto. -{{< figure >}} -src = '/surveys/NumPy_usersurvey_2020_report_cover.png' -alt = 'Página de portada del informe de la encuesta de usuarios de NumPy de 2020' -title = 'Encuesta de la Comunidad de NumPy 2020 - resultados' -width = '250' -{{< /figure >}} +{{< figure + src="/surveys/NumPy_usersurvey_2020_report_cover.png" + alt="Página de portada del informe de la encuesta de usuarios de NumPy de 2020" + title="Encuesta de la Comunidad de NumPy 2020 - resultados" + width=250 +>}} **[Descarga el informe](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver a detalle los resultados de la encuesta. @@ -18,4 +18,3 @@ width = '250' Para los puntos destacados, echa un vistazo a **[esta infografía](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)**. ¿Listo para una inmersión profunda? Visita **https://numpy.org/user-survey-2020-details/**. - diff --git a/content/ja/case-studies/blackhole-image.md b/content/ja/case-studies/blackhole-image.md index 896d46dd37..09013fc844 100644 --- a/content/ja/case-studies/blackhole-image.md +++ b/content/ja/case-studies/blackhole-image.md @@ -3,13 +3,13 @@ title: "ケーススタディ：世界初のブラックホール画像" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/blackhole.jpg' -title = 'Black Hole M87' -alt = 'black hole image' -attribution = '(Image Credits: Event Horizon Telescope Collaboration)' -attributionlink = 'https://www.jpl.nasa.gov/images/universe/90410/blackhole20190410.jpg' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/blackhole.jpg" + title="Black Hole M87" + alt="black hole image" + attribution="(Image Credits: Event Horizon Telescope Collaboration)" + attributionlink="https://www.jpl.nasa.gov/images/universe/90410/blackhole20190410.jpg" +>}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" @@ -44,14 +44,14 @@ M87ブラックホールを画像化することは、見ることのできな 今までに見たことのないものを見るのが研究の目標なら、どうやって科学者はその画像が正しいと確信することができるのでしょうか? -{{< figure >}} -src = '/images/content_images/cs/dataprocessbh.png' -title = 'EHTのデータ処理パイプライン' -alt = 'data pipeline' -align = 'center' -attribution = '(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)' -attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/dataprocessbh.png" + title="EHTのデータ処理パイプライン" + alt="data pipeline" + align="center" + attribution="(Diagram Credits: The Astrophysical Journal, Event Horizon Telescope Collaboration)" + attributionlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" +>}} ## NumPyが果たした役割 @@ -61,19 +61,19 @@ EHTの共同研究では、最先端の画像再構成技術を使用して、 彼らの研究は、共同のデータ解析を通じて科学を進歩させる、科学的なPythonエコシステムが果たす役割を如実に表しています。 -{{< figure >}} -src = '/images/content_images/cs/bh_numpy_role.png' -alt = 'role of numpy' -title = 'ブラックホール画像化でNumPyが果たした役割' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/bh_numpy_role.png" + alt="role of numpy" + title="ブラックホール画像化でNumPyが果たした役割" +>}} 例えば、 [`eht-imaging`][ehtim] というPython パッケージは VLBI データで画像の再構築をシミュレートし、実行するためのツールです。 NumPyは、以下のソフトウェア依存関係チャートで示されているように、このパッケージで使用される配列データ処理の中核を担っています。 -{{< figure >}} -src = '/images/content_images/cs/ehtim_numpy.png' -alt = 'ehtim dependency map highlighting numpy' -title = 'NumPyの中心としたehtimのソフトウェア依存図' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/ehtim_numpy.png" + alt="ehtim dependency map highlighting numpy" + title="NumPyの中心としたehtimのソフトウェア依存図" +>}} NumPyだけでなく、[SciPy](https://scipy.org)や[Pandas](https://pandas.pydata.org)などのパッケージもブラックホール画像化におけるデータ処理パイプラインに利用されています。 天文学の標準的なファイル形式や時間/座標変換 は[Astropy][astropy]で実装され、ブラックホールの最終画像の生成を含め、解析パイプライン全体でのデータ可視化には [Matplotlib][mpl]が利用されました。 @@ -81,11 +81,11 @@ NumPyだけでなく、[SciPy](https://scipy.org)や[Pandas](https://pandas.pyda NumPyの中心的な機能である、効率的で適用性の高いn次元配列は、研究者が大規模な数値データを操作することを可能にし、世界で初めてのブラックホールの画像化の基礎を築きました。 アインシュタインの理論に素晴らしい視覚的証拠を与えたのは、科学の画期的な瞬間だといえます。 この科学的に偉大な達成には、技術的の飛躍的な進歩だけでなく、200人以上の科学者と世界で 最高の電波観測所の間での国際協力も寄与しました。 革新的なアルゴリズムとデータ処理技術は、既存の天文学モデルを改良し、宇宙の謎を解き明かす助けになったといえます。 -{{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' -alt = 'numpy benefits' -title = '利用されたNumPyの主要機能' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_bh_benefits.png" + alt="numpy benefits" + title="利用されたNumPyの主要機能" +>}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole diff --git a/content/ja/case-studies/cricket-analytics.md b/content/ja/case-studies/cricket-analytics.md index 6a94285aae..af78cbc4a3 100644 --- a/content/ja/case-studies/cricket-analytics.md +++ b/content/ja/case-studies/cricket-analytics.md @@ -3,13 +3,13 @@ title: "ケーススタディ: クリケット分析、ゲームチェンジャ sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/ipl-stadium.png' -title = ' IPLT20、インド最大のクリケットフェスティバル' -alt = 'Indian Premier League Cricket cup and stadium' -attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' -attributionlink = 'https://unsplash.com/@aksh1802' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/ipl-stadium.png" + title="IPLT20、インド最大のクリケットフェスティバル" + alt="Indian Premier League Cricket cup and stadium" + attribution="(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))" + attributionlink="https://unsplash.com/@aksh1802" +>}} {{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" @@ -33,14 +33,14 @@ attributionlink = 'https://unsplash.com/@aksh1802' * プレイヤーの体力や、異なる相手に対するパフォーマンスについての洞察 * チーム構成に戦略的な決定を下すための、各勝敗へのプレイヤーの貢献 -{{< figure >}} -src = '/images/content_images/cs/cricket-pitch.png' -title = ' フィールドのフォーカルポイントとなるクリケットピッチ' -alt = 'A cricket pitch with bowler and batsmen' -align = 'center' -attribution = '(Image credit: Debarghya Das)' -attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/cricket-pitch.png" + title="フィールドのフォーカルポイントとなるクリケットピッチ" + alt="A cricket pitch with bowler and batsmen" + align="center" + attribution="(Image credit: Debarghya Das)" + attributionlink="http://debarghyadas.com/files/IPLpaper.pdf" +>}} ### データ分析の主要な目標 @@ -48,13 +48,13 @@ attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' * リアルタイムデータ分析は、ゲーム中の洞察を得ることができ、チームや関連ビジネスが経済的利益と成長のために戦術を変更するためも役立ちます。 * 履歴分析に加えて、予測モデルは可能性のある結果を求めることができますが、かなりの数のナンバークランチングとデータサイエンスのノウハウ、可視化ツール、および分析に新しい観測データを含める機能などが必要になります。 -{{< figure >}} -src = '/images/content_images/cs/player-pose-estimator.png' -alt = 'pose estimator' -title = 'クリケットの姿勢推定' -attribution = '(Image credit: connect.vin)' -attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/player-pose-estimator.png" + alt="pose estimator" + title="クリケットの姿勢推定" + attribution="(Image credit: connect.vin)" + attributionlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" +>}} ### 課題 @@ -82,8 +82,8 @@ attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analy スポーツアナリティクスは、プロの試合についてはまさにゲームチェンジャーです。 特に戦略的な意思決定については、最近まで主に「直感」や過去の伝統的な考え方に基づいて行われていたため、大きな影響があります。 NumPyは、データ分析・機械学習・人工知能のアルゴリズムに関連する高レベル関数を提供する沢山のPythonパッケージ群の、堅固な基盤となっています。 これらのパッケージは、ゲームの結果を変えるような意思決定を支援するリアルタイムのインサイトを得るため、クリケットの試合だけでなく関連する推論やビジネスの推進にも広く使用されています。 クリケットの試合結果につながる隠れたパラメータや、パターン、属性を見つけることは、ステークホルダーが数字や統計に隠されているゲームの洞察方法を見つけるのにも役に立つのです。 -{{< figure >}} -src = '/images/content_images/cs/numpy_ca_benefits.png' -alt = 'クリケット分析にNumPyを使用するメリットを示す図' -title = ' 利用されている主なNumPy機能 ' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_ca_benefits.png" + alt="クリケット分析にNumPyを使用するメリットを示す図" + title=" 利用されている主なNumPy機能 " +>}} diff --git a/content/ja/case-studies/deeplabcut-dnn.md b/content/ja/case-studies/deeplabcut-dnn.md index ebfe3edca7..a39a7eafde 100644 --- a/content/ja/case-studies/deeplabcut-dnn.md +++ b/content/ja/case-studies/deeplabcut-dnn.md @@ -3,13 +3,13 @@ title: "ケーススタディ: DeepLabCut 三次元姿勢推定" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/mice-hand.gif' -title = 'DeepLapCutを用いたマウスの手の動きの解析' -alt = 'micehandanim' -attribution = '(Source: www.deeplabcut.org )' -attributionlink = 'http://www.mousemotorlab.org/deeplabcut' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/mice-hand.gif" + title="DeepLapCutを用いたマウスの手の動きの解析" + alt="micehandanim" + attribution="(Source: www.deeplabcut.org )" + attributionlink="http://www.mousemotorlab.org/deeplabcut" +>}} {{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" @@ -24,12 +24,12 @@ attributionlink = 'http://www.mousemotorlab.org/deeplabcut' 神経科学、医学、生体力学などのいくつかの研究分野では、動物の動きを追跡したデータを使用しています。 DeepLabCutは、動画に記録された動きを解析することで、人間やその他の動物が何をしているのかを理解することができます。 タグ付けや監視などの、手間のかかる作業を自動化し、深層学習ベースのデータ解析を実施します。 DeepLabCutは、霊長類、マウス、魚、ハエなどの動物を観察する科学研究をより速く正確にしています。 -{{< figure >}} -src = '/images/content_images/cs/race-horse.gif' -title = '色のついた点は競走馬の体の位置を追跡' -alt = 'horserideranim' -attribution = '(Source: Mackenzie Mathis)' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/race-horse.gif" + title="色のついた点は競走馬の体の位置を追跡" + alt="horserideranim" + attribution="(Source: Mackenzie Mathis)" +>}} DeepLabCutは、動物の姿勢を抽出することで非侵襲的な行動追跡を行います。 これは、生体力学、遺伝学、倫理学、神経科学などの分野での研究に必要不可欠です。 動的に変化する背景の中で、動物の姿勢をビデオデータから非侵襲的に測定することは、技術的にも、必要な計算リソースやトレーニングデータの点でも、非常に困難な計算処理です。 @@ -58,14 +58,14 @@ DeepLabCutでは[転移学習](https://arxiv.org/pdf/1909.11229)という技術 - 動画における大規模推論のためのコード作成 - 統合された可視化ツールを使用した推論の描画 -{{< figure >}} -src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' -title = 'DeepLabCutによる姿勢推定のステップ' -alt = 'dlcsteps' -align = 'center' -attribution = '(Source: DeepLabCut)' -attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/deeplabcut-toolkit-steps.png" + title="DeepLabCutによる姿勢推定のステップ" + alt="dlcsteps" + align="center" + attribution="(Source: DeepLabCut)" + attributionlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" +>}} ### 課題 @@ -81,14 +81,14 @@ attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/pho 最後に、配列の操作もかなり難しい問題です。 様々な画像や、目標のテンソル、キーポイントに対応する大きな配列のスタックを処理しなければならないからです。 -{{< figure >}} -src = '/images/content_images/cs/pose-estimation.png' -title = '姿勢推定の多様性と難しさ' -alt = 'challengesfig' -align = 'center' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/pose-estimation.png" + title="姿勢推定の多様性と難しさ" + alt="challengesfig" + align="center" + attribution="(Source: Mackenzie Mathis)" + attributionlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" +>}} ## 姿勢推定の課題に対応するためのNumPyの役割 @@ -104,23 +104,23 @@ NumPy は DeepLabCutにおける、行動分析の高速化のための数値計 DeepLabCutは、ツールキットが提供するワークフローを通じてNumPyの配列機能を利用しています。 特に、NumPyはヒューマンアノテーションのラベル付けや、アノテーションの書き込み、編集、処理のために、特定のフレームをサンプリングするために使用されています。 TensorFlowを使ったニューラルネットワークは、DeepLabCutの技術によって何千回も訓練され、 フレームから真のアノテーション情報を予測します。 この目的のため、姿勢推定問題を画像-画像変換問題として変換する目標密度(スコアマップ) を作成します。 ニューラルネットワークのロバスト化のため、データの水増しを使用していますが、このためには幾何学・画像的処理を施したスコアマップの計算を行うことが必要になります。 また学習を高速化するため、NumPyのベクトル化機能が利用されています。 推論には、目標のスコアマップから最も可能性の高い予測値を抽出し、効率的に「予測値をリンクさせて個々の動物を組み立てる」ことが必要になります。 -{{< figure >}} -src = '/images/content_images/cs/deeplabcut-workflow.png' -title = 'DeepLabCutのワークフロー' -alt = 'workflow' -attribution = '(Source: Mackenzie Mathis)' -attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/deeplabcut-workflow.png" + title="DeepLabCutのワークフロー" + alt="workflow" + attribution="(Source: Mackenzie Mathis)" + attributionlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962" +>}} ## まとめ 行動を観察し、効率的に表現することは、現代倫理学、神経科学、医学、工学の根幹です。 [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) により、研究者は対象の姿勢を推定し、行動を効率的に定量化できるようになりました。 DeepLabCutというPythonツールボックスを使えば、わずかな学習画像のセットでニューラルネットワークを人間レベルのラベリング精度で学習することができ、実験室での行動分析だけでなく、スポーツ、歩行分析、医学、リハビリテーション研究などへの応用が可能になります。 DeepLabCutアルゴリズムに必要な複雑な組み合わせ処理やデータ処理の問題を、NumPyの配列操作機能が解決しています。 -{{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' -alt = 'numpy benefits' -title = 'NumPyの主要機能' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_dlc_benefits.png" + alt="numpy benefits" + title="NumPyの主要機能" +>}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 diff --git a/content/ja/case-studies/gw-discov.md b/content/ja/case-studies/gw-discov.md index e92252d653..ede2005c01 100644 --- a/content/ja/case-studies/gw-discov.md +++ b/content/ja/case-studies/gw-discov.md @@ -3,13 +3,13 @@ title: "ケーススタディ: 重力波の発見" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/gw_sxs_image.png' -title = '重力波' -alt = 'binary coalesce black hole generating gravitational waves' -attribution = '(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)' -attributionlink = 'https://youtu.be/Zt8Z_uzG71o' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/gw_sxs_image.png" + title="重力波" + alt="binary coalesce black hole generating gravitational waves" + attribution="(Image Credits: The Simulating eXtreme Spacetimes (SXS) Project at LIGO)" + attributionlink="https://youtu.be/Zt8Z_uzG71o" +>}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" by="David Shoemaker, *LIGOサイエンティフィック・コラボレーション*" >}} 科学計算のためのPythonエコシステムはLIGOで行われている研究のための重要なインフラです。 {{< /blockquote >}} @@ -43,13 +43,13 @@ attributionlink = 'https://youtu.be/Zt8Z_uzG71o' アインシュタイン方程式を元にスーパーコンピュータでデータを解析できるようになったら、次はデータを人間の脳で理解できるようにしなければなりません。 シミュレーションのモデリングや信号の検出には、わかりやすい可視化技術が必要です。 画像処理やシミュレーションによって、解析結果をより多くの人に理解してもらえる状態になる前の段階において、可視化は、数値相対性を十分に重要視していなかった純粋な科学愛好家の目に、数値相対性が、より信頼性の高いものとして映るようにするという役割も果たしています。 複雑な計算と描画を行い、また最新の実験結果と洞察に基づいてシミュレーションと再描画を行う作業は時間のかかるもので、この分野の研究者にとっての課題です。 -{{< figure >}} -src = '/images/content_images/cs/gw_strain_amplitude.png' -alt = 'gravitational waves strain amplitude' -title = 'GW150914から推定される重力波の歪みの振幅' -attribution = '(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' -attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/gw_strain_amplitude.png" + alt="gravitational waves strain amplitude" + title="GW150914から推定される重力波の歪みの振幅" + attribution="(Graph Credits: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)" + attributionlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger" +>}} ## 重力波の検出におけるNumPyの役割 @@ -66,26 +66,26 @@ Python用の標準的な数値解析パッケージNumPyは、LIGOの重力波 * 相関計算 * 重力波データ解析のために開発された[ソフトウェア群](https://github.com/lscsoft): [GwPy](https://gwpy.github.io/docs/stable/overview.html)や [PyCBC](https://pycbc.org)は、NumPyやAstroPyを用いて、重力波検出器データを研究するためのユーティリティー・ツール・関数へのオブジェクト指向インターフェースを提供しています。 -{{< figure >}} -src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' -alt = 'gwpy-numpy depgraph' -title = 'GwPyのNumPy依存グラフ' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/gwpy-numpy-dep-graph.png" + alt="gwpy-numpy depgraph" + title="GwPyのNumPy依存グラフ" +>}} ---- -{{< figure >}} -src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' -alt = 'PyCBC-numpy depgraph' -title = 'PyCBCのNumPy依存グラフ' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/PyCBC-numpy-dep-graph.png" + alt="PyCBC-numpy depgraph" + title="PyCBCのNumPy依存グラフ" +>}} ## まとめ 一方で、これまで知られてきた深遠な天体物理学の現象に、多くに新たな洞察を提供しました。 数値処理とデータの可視化は、科学者が科学的な観測から収集したデータについての洞察を得て、その結果を理解するのに役立つ重要なステップです。 しかし、その計算は複雑であり、実際の観測データと分析を用いたコンピュータシミュレーションを用いて可視化されない限り、人間が理解することはできませんでした。 NumPyは、matplotlib・pandas・scikit-learnなどのPythonパッケージとともに、研究者が複雑な質問に答え、私たちの宇宙に対するの理解において、新しい地平を発見することを[可能にしています](https://www.gw-openscience.org/events/GW150914/)。 -{{< figure >}} -src = '/images/content_images/cs/numpy_gw_benefits.png' -alt = 'numpy benefits' -title = '利用されたNumPyの主要機能' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_gw_benefits.png" + alt="numpy benefits" + title="利用されたNumPyの主要機能" +>}} diff --git a/content/ja/user-survey-2020.md b/content/ja/user-survey-2020.md index 3541e5b59b..bf8e390709 100644 --- a/content/ja/user-survey-2020.md +++ b/content/ja/user-survey-2020.md @@ -5,11 +5,11 @@ sidebar: false 2020年に、NumPyの調査チームは、ミシガン大学とメリーランド大学が共同で開催した、調査方法学の修士コースの学生と教員と共同で、初めて公式のNumPyコミュニティ調査を実施しました。 75カ国から1,200人以上のNumPyユーザーが参加してくれました。 NumPyコミュニティの全体像を描き、プロジェクトの未来像についての意見を述べてもらいました。 -{{< figure >}} -src = '/surveys/NumPy_usersurvey_2020_report_cover.png' -alt = 'Cover page of the 2020 Numpy User survey report, titled "Numpyコミュニティ調査2020 - 結果"' -width = '250' -{{< /figure >}} +{{< figure + src="/surveys/NumPy_usersurvey_2020_report_cover.png" + alt="Cover page of the 2020 Numpy User survey report, titled \"Numpyコミュニティ調査2020 - 結果\"" + width=250 +>}} 調査結果を詳細を知りたい場合は、**[こちらのレポート](/surveys/NumPy_usersurvey_2020_report.pdf)** をダウンロードしてください。 @@ -17,4 +17,3 @@ width = '250' 結果の概要については、 **[こちらの図](https://github.com/numpy/numpy-surveys/blob/master/images/2020NumPysurveyresults_community_infographic.pdf)** をチェックしてください。 より詳細が知りたくなりましたか？ **https://numpy.org/user-survey-2020-details/** をご覧ください。 - diff --git a/content/pt/case-studies/blackhole-image.md b/content/pt/case-studies/blackhole-image.md index ec740bf5d0..9519e0c239 100644 --- a/content/pt/case-studies/blackhole-image.md +++ b/content/pt/case-studies/blackhole-image.md @@ -3,13 +3,13 @@ title: "Estudo de Caso: A Primeira Imagem de um Buraco Negro" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/blackhole.jpg' -title = 'Black Hole M87' -alt = 'black hole image' -attribution = '(Créditos: Event Horizon Telescope Collaboration)' -attributionlink = 'https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/blackhole.jpg" + title="Black Hole M87" + alt="black hole image" + attribution="(Créditos: Event Horizon Telescope Collaboration)" + attributionlink="https://www.jpl.nasa.gov/images/universe/20190410/blackhole20190410.jpg" +>}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" @@ -44,14 +44,14 @@ O [telescópio Event Horizon (EHT)](https://eventhorizontelescope.org), é um co Quando o objetivo é algo que nunca foi visto, como os cientistas podem ter confiança de que sua imagem está correta? -{{< figure >}} -src = '/images/content_images/cs/dataprocessbh.png' -title = 'Etapas de Processamento de Dados do EHT' -alt = 'data pipeline' -align = 'center' -attribution = '(Créditos do diagrama: The Astrophysical Journal, Event Horizon Telescope Collaboration)' -attributionlink = 'https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/dataprocessbh.png" + title="Etapas de Processamento de Dados do EHT" + alt="data pipeline" + align="center" + attribution="(Créditos do diagrama: The Astrophysical Journal, Event Horizon Telescope Collaboration)" + attributionlink="https://iopscience.iop.org/article/10.3847/2041-8213/ab0c57" +>}} ## O papel do NumPy @@ -61,19 +61,19 @@ A colaboração do EHT venceu esses desafios ao estabelecer equipes independente O trabalho desse grupo ilustra o papel do ecossistema científico do Python no avanço da ciência através da análise de dados colaborativa. -{{< figure >}} -src = '/images/content_images/cs/bh_numpy_role.png' -alt = 'role of numpy' -title = 'O papel do NumPy na criação da primeira imagem de um Buraco Negro' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/bh_numpy_role.png" + alt="role of numpy" + title="O papel do NumPy na criação da primeira imagem de um Buraco Negro" +>}} Por exemplo, o pacote Python [`eht-imaging`][ehtim] fornece ferramentas para simular e realizar reconstrução de imagem nos dados do VLBI. O NumPy está no coração do processamento de dados vetoriais usado neste pacote, como ilustrado pelo gráfico parcial de dependências de software abaixo. -{{< figure >}} -src = '/images/content_images/cs/ehtim_numpy.png' -alt = 'ehtim dependency map highlighting numpy' -title = 'Diagrama de dependência de software do pacote ehtim evidenciando o NumPy' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/ehtim_numpy.png" + alt="ehtim dependency map highlighting numpy" + title="Diagrama de dependência de software do pacote ehtim evidenciando o NumPy" +>}} Além do NumPy, muitos outros pacotes como [SciPy](https://scipy.org) e [Pandas](https://pandas.pydata.org) foram usados na *pipeline* de processamento de dados para criar a imagem do buraco negro. Os arquivos astronômicos de formato padrão e transformações de tempo/coordenadas foram tratados pelo [Astropy][astropy] enquanto a [Matplotlib][mpl] foi usada na visualização de dados em todas as etapas de análise, incluindo a geração da imagem final do buraco negro. @@ -81,11 +81,11 @@ Além do NumPy, muitos outros pacotes como [SciPy](https://scipy.org) e [Pandas] A estrutura de dados n-dimensional que é a funcionalidade central do NumPy permitiu aos pesquisadores manipular grandes conjuntos de dados, fornecendo a base para a primeira imagem de um buraco negro. Esse momento marcante na ciência fornece evidências visuais impressionantes para a teoria de Einstein. Esta conquista abrange não apenas avanços tecnológicos, mas colaboração científica em escala internacional entre mais de 200 cientistas e alguns dos melhores observatórios de rádio do mundo. Eles usaram algoritmos e técnicas de processamento de dados inovadores, que aperfeiçoaram os modelos astronômicos existentes, para ajudar a descobrir um dos mistérios do universo. -{{< figure >}} -src = '/images/content_images/cs/numpy_bh_benefits.png' -alt = 'numpy benefits' -title = 'Funcionalidades-chave do NumPy utilizadas' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_bh_benefits.png" + alt="numpy benefits" + title="Funcionalidades-chave do NumPy utilizadas" +>}} [resolution]: https://eventhorizontelescope.org/press-release-april-10-2019-astronomers-capture-first-image-black-hole diff --git a/content/pt/case-studies/cricket-analytics.md b/content/pt/case-studies/cricket-analytics.md index 8d70c776a6..bb188cc750 100644 --- a/content/pt/case-studies/cricket-analytics.md +++ b/content/pt/case-studies/cricket-analytics.md @@ -3,13 +3,13 @@ title: "Estudo de Caso: Análise de Críquete, a revolução!" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/ipl-stadium.png' -title = 'IPLT20, o maior festival de Críquete da Índia' -alt = 'Copa e estádio da Indian Premier League Cricket' -attribution = '(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))' -attributionlink = 'https://unsplash.com/@aksh1802' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/ipl-stadium.png" + title="IPLT20, o maior festival de Críquete da Índia" + alt="Copa e estádio da Indian Premier League Cricket" + attribution="(Image credits: IPLT20 (cup and logo) & Akash Yadav (stadium))" + attributionlink="https://unsplash.com/@aksh1802" +>}} {{< blockquote cite="https://www.scoopwhoop.com/sports/ms-dhoni/" @@ -33,14 +33,14 @@ Hoje, existem conjuntos ricos e quase infinitos de estatísticas e informações * ganho de informações sobre desempenho e condição física de um determinado jogador contra determinado adversário, * contribuições dos jogadores para vitórias e derrotas para a tomada de decisões estratégicas na composição do time -{{< figure >}} -src = '/images/content_images/cs/cricket-pitch.png' -title = 'Pitch de críquete, o ponto focal do campo' -alt = 'Um pitch de críquete com um boleador e batsmen' -align = 'center' -attribution = '(Créditos de imagem: Debarghya Das)' -attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/cricket-pitch.png" + title="Pitch de críquete, o ponto focal do campo" + alt="Um pitch de críquete com um boleador e batsmen" + align="center" + attribution="(Créditos de imagem: Debarghya Das)" + attributionlink="http://debarghyadas.com/files/IPLpaper.pdf" +>}} ### Objetivos Principais da Análise de Dados @@ -48,13 +48,13 @@ attributionlink = 'http://debarghyadas.com/files/IPLpaper.pdf' * A análise de dados em tempo real pode ajudar a obtenção de informações mesmo durante o jogo para orientar mudanças nas táticas da equipe e dos negócios associados para benefícios e crescimento econômicos. * Além da análise histórica, os modelos preditivos explorados para determinar os possíveis resultados das partidas requerem um conhecimento significativo sobre processamento numérico e ciência de dados, ferramentas de visualização e a possibilidade de incluir observações mais recentes na análise. -{{< figure >}} -src = '/images/content_images/cs/player-pose-estimator.png' -alt = 'estimador de postura' -title = 'Estimador de Postura de Críquete' -attribution = '(Créditos de imagem: connect.vin)' -attributionlink = 'https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/player-pose-estimator.png" + alt="estimador de postura" + title="Estimador de Postura de Críquete" + attribution="(Créditos de imagem: connect.vin)" + attributionlink="https://connect.vin/2019/05/ai-for-cricket-batsman-pose-analysis/" +>}} ### Desafios @@ -82,8 +82,8 @@ A análise de dados esportivos é um campo próspero. Muitos pesquisadores e emp A análise de dados esportivos é revolucionária quando se trata de como os jogos profissionais são jogados, especialmente se consideramos como acontece a tomada de decisões estratégicas, que até pouco tempo era principalmente feita com base na "intuição" ou adesão a tradições passadas. O NumPy forma uma fundação sólida para um grande conjunto de pacotes Python que fornecem funções de alto nível relacionadas à análise de dados, aprendizagem de máquina e algoritmos de IA. Estes pacotes são amplamente implantados para se obter informações em tempo real que ajudam na tomada de decisão para resultados decisivos, tanto em campo como para se derivar inferências e orientar negócios em torno do jogo de críquete. Encontrar os parâmetros ocultos, padrões, e atributos que levam ao resultado de uma partida de críquete ajuda os envolvidos a tomar nota das percepções do jogo que estariam de outra forma ocultas nos números e estatísticas. -{{< figure >}} -src = '/images/content_images/cs/numpy_ca_benefits.png' -alt = 'Diagrama mostrando os benefícios de usar a NumPy para análise de críquete' -title = 'Recursos principais da NumPy utilizados' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_ca_benefits.png" + alt="Diagrama mostrando os benefícios de usar a NumPy para análise de críquete" + title="Recursos principais da NumPy utilizados" +>}} diff --git a/content/pt/case-studies/deeplabcut-dnn.md b/content/pt/case-studies/deeplabcut-dnn.md index 557b336ab8..2348360804 100644 --- a/content/pt/case-studies/deeplabcut-dnn.md +++ b/content/pt/case-studies/deeplabcut-dnn.md @@ -3,13 +3,13 @@ title: "Estudo de Caso: Estimativa de Pose 3D com DeepLabCut" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/mice-hand.gif' -title = 'Análise de movimentos de mãos de camundongos usando DeepLapCut' -alt = 'micehandanim' -attribution = '(Fonte: www.deeplabcut.org )' -attributionlink = 'http://www.mousemotorlab.org/deeplabcut' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/mice-hand.gif" + title="Análise de movimentos de mãos de camundongos usando DeepLapCut" + alt="micehandanim" + attribution="(Fonte: www.deeplabcut.org )" + attributionlink="http://www.mousemotorlab.org/deeplabcut" +>}} {{< blockquote cite="https://news.harvard.edu/gazette/story/newsplus/harvard-researchers-awarded-czi-open-source-award/" @@ -24,12 +24,12 @@ Software de código aberto está acelerando a Biomedicina. DeepLabCut permite a Várias áreas de pesquisa, incluindo a neurociência, a medicina e a biomecânica, utilizam dados de rastreamento da movimentação de animais. A DeepLabCut ajuda a compreender o que os seres humanos e outros animais estão fazendo, analisando ações que foram registradas em vídeo. Ao usar automação para tarefas trabalhosas de monitoramento e marcação, junto com análise de dados baseada em redes neurais profundas, a DeepLabCut garante que estudos científicos envolvendo a observação de animais como primatas, camundongos, peixes, moscas etc. sejam mais rápidos e precisos. -{{< figure >}} -src = '/images/content_images/cs/race-horse.gif' -title = 'Pontos coloridos rastreiam as posições das partes do corpo de um cavalo de corrida' -alt = 'horserideranim' -attribution = '(Fonte: Mackenzie Mathis)' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/race-horse.gif" + title="Pontos coloridos rastreiam as posições das partes do corpo de um cavalo de corrida" + alt="horserideranim" + attribution="(Fonte: Mackenzie Mathis)" +>}} O rastreamento não invasivo dos animais pela DeepLabCut através da extração de poses é crucial para pesquisas científicas em domínios como a biomecânica, genética, etologia e neurociência. Medir as poses dos animais de maneira não invasiva através de vídeo - sem marcadores - com fundos dinâmicos é computacionalmente desafiador, tanto tecnicamente quanto em termos de recursos e dados de treinamento necessários. @@ -58,14 +58,14 @@ Recentemente, foi introduzido o [modelo DeepLabCut zoo](http://www.mousemotorlab - código para inferência em larga escala em vídeos - inferências de desenho usando ferramentas integradas de visualização -{{< figure >}} -src = '/images/content_images/cs/deeplabcut-toolkit-steps.png' -title = 'Passos na estimação de poses com DeepLabCut' -alt = 'dlcsteps' -align = 'center' -attribution = '(Fonte: DeepLabCut)' -attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/deeplabcut-toolkit-steps.png" + title="Passos na estimação de poses com DeepLabCut" + alt="dlcsteps" + align="center" + attribution="(Fonte: DeepLabCut)" + attributionlink="https://twitter.com/DeepLabCut/status/1198046918284210176/photo/1" +>}} ### Desafios @@ -81,14 +81,14 @@ attributionlink = 'https://twitter.com/DeepLabCut/status/1198046918284210176/pho Por último, mas não menos importante, manipulação de matrizes - processar grandes conjuntos de matrizes correspondentes a várias imagens, tensores alvo e pontos-chave é bastante desafiador. -{{< figure >}} -src = '/images/content_images/cs/pose-estimation.png' -title = 'Estimação de poses e complexidade' -alt = 'challengesfig' -align = 'center' -attribution = '(Fonte: Mackenzie Mathis)' -attributionlink = 'https://www.biorxiv.org/content/10.1101/476531v1.full.pdf' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/pose-estimation.png" + title="Estimação de poses e complexidade" + alt="challengesfig" + align="center" + attribution="(Fonte: Mackenzie Mathis)" + attributionlink="https://www.biorxiv.org/content/10.1101/476531v1.full.pdf" +>}} ## O papel da NumPy nos desafios da estimação de poses @@ -104,23 +104,23 @@ As seguintes características da NumPy desempenharam um papel fundamental para a A DeepLabCut utiliza as capacidades de manipulação de arrays da NumPy em todo o fluxo de trabalho oferecido pelo seu conjunto de ferramentas. Em particular, a NumPy é usada para amostragem de quadros distintos para serem rotulados com anotações humanas e para escrita, edição e processamento de dados de anotação. Dentro da TensorFlow, a rede neural é treinada pela tecnologia DeepLabCut em milhares de iterações para prever as anotações verdadeiras dos quadros. Para este propósito, densidades de alvo (*scoremaps*) são criadas para colocar a estimativa como um problema de tradução de imagem a imagem. Para tornar as redes neurais robustas, o aumento de dados é empregado, o que requer o cálculo de scoremaps alvo sujeitos a várias etapas geométricas e de processamento de imagem. Para tornar o treinamento rápido, os recursos de vectorização da NumPy são utilizados. Para inferência, as previsões mais prováveis de scoremaps alvo precisam ser extraídas e é necessário "vincular previsões para montar animais individuais" de maneira eficiente. -{{< figure >}} -src = '/images/content_images/cs/deeplabcut-workflow.png' -title = 'Fluxo de dados DeepLabCut' -alt = 'workflow' -attribution = '(Fonte: Mackenzie Mathis)' -attributionlink = 'https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/deeplabcut-workflow.png" + title="Fluxo de dados DeepLabCut" + alt="workflow" + attribution="(Fonte: Mackenzie Mathis)" + attributionlink="https://www.researchgate.net/figure/DeepLabCut-work-flow-The-diagram-delineates-the-work-flow-as-well-as-the-directory-and_fig1_329185962" +>}} ## Resumo Observação e descrição eficiente do comportamento é uma peça fundamental da etologia, neurociência, medicina e tecnologia modernas. [DeepLabCut](http://orga.cvss.cc/wp-content/uploads/2019/05/NathMathis2019.pdf) permite que os pesquisadores estimem a pose do sujeito, permitindo efetivamente que o seu comportamento seja quantificado. Com apenas um pequeno conjunto de imagens de treinamento, o conjunto de ferramentas em Python da DeepLabCut permite treinar uma rede neural tão precisa quanto a rotulagem humana, expandindo assim sua aplicação para não só análise de comportamento dentro do laboratório, mas também potencialmente em esportes, análise de locomoção, medicina e estudos sobre reabilitação. Desafios complexos em combinatória e processamento de dados enfrentados pelos algoritmos da DeepLabCut são tratados através do uso de recursos de manipulação de matriz do NumPy. -{{< figure >}} -src = '/images/content_images/cs/numpy_dlc_benefits.png' -alt = 'numpy benefits' -title = 'Recursos chave do NumPy utilizados' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_dlc_benefits.png" + alt="numpy benefits" + title="Recursos chave do NumPy utilizados" +>}} [cheetah-movement]: https://www.technologynetworks.com/neuroscience/articles/interview-a-deeper-cut-into-behavior-with-mackenzie-mathis-327618 diff --git a/content/pt/case-studies/gw-discov.md b/content/pt/case-studies/gw-discov.md index cb371914fc..1b40c3ec71 100644 --- a/content/pt/case-studies/gw-discov.md +++ b/content/pt/case-studies/gw-discov.md @@ -3,13 +3,13 @@ title: "Estudo de Caso: Descoberta de Ondas Gravitacionais" sidebar: false --- -{{< figure >}} -src = '/images/content_images/cs/gw_sxs_image.png' -title = 'Ondas gravitacionais' -alt = 'binary coalesce black hole generating gravitational waves' -attribution = '(Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO)' -attributionlink = 'https://youtu.be/Zt8Z_uzG71o' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/gw_sxs_image.png" + title="Ondas gravitacionais" + alt="binary coalesce black hole generating gravitational waves" + attribution="(Créditos de imagem: O projeto Simulating eXtreme Spacetimes (SXS) no LIGO)" + attributionlink="https://youtu.be/Zt8Z_uzG71o" +>}} {{< blockquote cite="https://www.youtube.com/watch?v=BIvezCVcsYs" @@ -46,13 +46,13 @@ O [Observatório Interferômetro Laser de Ondas Gravitacionais (LIGO)](https://w Uma vez que os obstáculos relacionados a compreender as equações de Einstein bem o suficiente para resolvê-las usando supercomputadores foram ultrapassados, o próximo grande desafio era tornar os dados compreensíveis para o cérebro humano. A modelagem de simulações, assim como a detecção de sinais, exigem técnicas de visualização efetiva. A visualização também desempenha um papel de fornecer mais credibilidade à relatividade numérica aos olhos dos aficionados pela ciência pura, que não dão importância suficiente à relatividade numérica até que a imagem e as simulações tornem mais fácil a compreensão dos resultados para um público maior. A velocidade da computação complexa, e da renderização, re-renderização de imagens e simulações usando as últimas entradas e informações experimentais pode ser uma atividade demorada que desafia pesquisadores neste domínio. -{{< figure >}} -src = '/images/content_images/cs/gw_strain_amplitude.png' -alt = 'gravitational waves strain amplitude' -title = 'Amplitude estimada da deformação das ondas gravitacionais do evento GW150914' -attribution = '(Créditos do gráfico: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)' -attributionlink = 'https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/gw_strain_amplitude.png" + alt="gravitational waves strain amplitude" + title="Amplitude estimada da deformação das ondas gravitacionais do evento GW150914" + attribution="(Créditos do gráfico: Observation of Gravitational Waves from a Binary Black Hole Merger, ResearchGate Publication)" + attributionlink="https://www.researchgate.net/publication/293886905_Observation_of_Gravitational_Waves_from_a_Binary_Black_Hole_Merger" +>}} ## O papel da NumPy na detecção de ondas gravitacionais @@ -69,26 +69,26 @@ NumPy, o pacote padrão de análise numérica para Python, foi parte do software * Cálculo de correlações * [Software](https://github.com/lscsoft) fundamental desenvolvido na análise de ondas gravitacionais, como [GwPy](https://gwpy.github.io/docs/stable/overview.html) e [PyCBC](https://pycbc.org) usam NumPy e AstroPy internamente para fornecer interfaces baseadas em objetos para utilidades, ferramentas e métodos para o estudo de dados de detectores de ondas gravitacionais. -{{< figure >}} -src = '/images/content_images/cs/gwpy-numpy-dep-graph.png' -alt = 'gwpy-numpy depgraph' -title = 'Grafo de dependências mostrando como o pacote GwPy depended da NumPy' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/gwpy-numpy-dep-graph.png" + alt="gwpy-numpy depgraph" + title="Grafo de dependências mostrando como o pacote GwPy depended da NumPy" +>}} ---- -{{< figure >}} -src = '/images/content_images/cs/PyCBC-numpy-dep-graph.png' -alt = 'PyCBC-numpy depgraph' -title = 'Grafo de dependências mostrando como o pacote PyCBC depended da NumPy' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/PyCBC-numpy-dep-graph.png" + alt="PyCBC-numpy depgraph" + title="Grafo de dependências mostrando como o pacote PyCBC depended da NumPy" +>}} ## Resumo A detecção de ondas gravitacionais permitiu que pesquisadores descobrissem fenômenos totalmente inesperados ao mesmo tempo em que proporcionaram novas idéias sobre muitos dos fenômenos mais profundos conhecidos na astrofísica. O processamento e a visualização de dados é um passo crucial que ajuda cientistas a obter informações coletadas de observações científicas e a entender os resultados. Os cálculos são complexos e não podem ser compreendidos por humanos a não ser que sejam visualizados usando simulações de computador que são alimentadas com dados e análises reais observados. A NumPy, junto com outras bibliotecas Python, como matplotlib, pandas, e scikit-learn [permitem que pesquisadores](https://www.gw-openscience.org/events/GW150914/) respondam perguntas complexas e descubram novos horizontes em nossa compreensão do universo. -{{< figure >}} -src = '/images/content_images/cs/numpy_gw_benefits.png' -alt = 'numpy benefits' -title = 'Recursos chave da NumPy utilizados' -{{< /figure >}} +{{< figure + src="/images/content_images/cs/numpy_gw_benefits.png" + alt="numpy benefits" + title="Recursos chave da NumPy utilizados" +>}} diff --git a/content/pt/user-survey-2020.md b/content/pt/user-survey-2020.md index 8747efca88..d7eb4d25cd 100644 --- a/content/pt/user-survey-2020.md +++ b/content/pt/user-survey-2020.md @@ -5,11 +5,11 @@ sidebar: false Em 2020, o time de pesquisas do NumPy realizou a primeira pesquisa oficial sobre a comunidade NumPy, em parceria com alunos e docentes de um Mestrado em metodologia de pesquisa realizado conjuntamente pela Universidade de Michigan e pela Universidade da Maryland. Mais de 1200 usuários de 75 países participaram para nos ajudar a mapear uma paisagem da comunidade NumPy e expressaram seus pensamentos sobre o futuro do projeto. -{{< figure >}} -src = '/surveys/NumPy_usersurvey_2020_report_cover.png' -alt = 'Página de capa do relatório da pesquisa de usuários do NumPy 2020, chamado "NumPy Community Survey 2020 - results"' -width = '250' -{{< /figure >}} +{{< figure + src="/surveys/NumPy_usersurvey_2020_report_cover.png" + alt="Página de capa do relatório da pesquisa de usuários do NumPy 2020, chamado \"NumPy Community Survey 2020 - results\"" + width=250 +>}} **[Faça o download do relatório](/surveys/NumPy_usersurvey_2020_report.pdf)** para ver os detalhes sobre os resultados encontrados.