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Updated guide to version 2 (#10)
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* Guide updates for v2

* Guide updates

* More guide updates

* Update self ssl troubleshooting section

* Add hyperlinks and code files

* Formatting readme

* Formatting readme

* Formatting readme

* Change nut graph and add Insights Desktop topic

* Readme update

* Adding diagrams

* Add client diagram

* Desktop and Cloud diagrams

* Desktop and Client revisions diagrams

* Connection section

* Create connection w/ url table

* Start command logic

* Stop instructions

* superscript note

* Connection images, connection section

* new images

* image updates

* image updates

* image updates

* more doc

* new images

* Adds tool table, general feature section

* Table styling

* Table styling

* Table styling markdowm

* more table styling markdowm

* more styling

* Changes not honored

* Table update

* Magic commands and shortcuts

* Edits and readme clean up

* More edits and readme clean up

* readme clean up

* readme

* readme

* readme update

* Docker section

* Readme update

* Readme update connection section

* Readme update

* Readme update

* Readme update

* Readme update

* Readme update

* more edits

* more edits

* tweaks

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* added code sample

* Sample tweaks

* Sample tweaks

* Sample tweaks

* Sample update
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Doug Carroll committed Mar 3, 2020
1 parent 486e7e4 commit f81ac43
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32 changes: 32 additions & 0 deletions Py/addresses/README.md
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## Create a Panda Data Frame with Geocode Ready Data

Creates a Panda data frame that can be imported and persisted into Insights workbooks for further analysis.

__Steps__

1) In the first cell paste this code and then click Run (or _Shift-Enter_)

```python
import pandas as pd
import numpy as np

df = pd.DataFrame(np.array([
["1001 19th St North", "Arlington", "VA", "Esri R&D"],
["380 New York St", "Redlands", "CA", "Esri Headquarters"],
["920 SW #rd Avenue", "Portland", "OR", "Esri R&D"],
["75 Broad St", "New York City", "NY", "Esri Regional Office"]
]), columns=["Address", "City", "State", "Office"])

df
```

2) In second cell paste this code (or use the magic command _Ctrl-Alt-B_ passing in ```df```) and then click Run. Notice the layer named ```Layer``` is created in the _Data Pane_

```python
%insights_return(df)
```

3) Next find the layer named ```Layer``` in the _Data Pane_ and click the ellipse. From the context menu select __Enable Location__. Choose _Address_ and _Esri World Geocoder_. Then click Run.


4) Notice a new field _Location_ is created in the layer. This is a geometry field. Next drag the layer onto the canvas to create a Map (by dropping the layer in a _drop zone_ named _Map_). Once the map is created you can visualise and style each location and dive deeper into spatial and non-spatial analytics.
11 changes: 11 additions & 0 deletions Py/addresses/address_dataframe.py
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import pandas as pd
import numpy as np

df = pd.DataFrame(np.array([
["1001 19th St North", "Arlington", "VA", "Esri R&D"],
["380 New York St", "Redlands", "CA", "Esri Headquarters"],
["920 SW #rd Avenue", "Portland", "OR", "Esri R&D"],
["75 Broad St", "New York City", "NY", "Esri Regional Office"]
]), columns=["Address", "City", "State", "Office"])

df
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31 changes: 31 additions & 0 deletions diagrams.md
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## Kernel Gateway Deployment Planning

Want to learn more about deploying a Jupyter Kernel Gateway? These conceptual diagrams were designed to help organizations visualize different Jupyter Kernel Gateway configurations.

### Insights Desktop

![Insights Desktop and Kernel Gateway](diagrams/jkg-desktop-diagram.png)


### ArcGIS Enterprise with Insights and Kernel Gateway

#### Dedicated

![Dedicated Kernel Gateway](diagrams/jkg-dedicated-diagram.png)

#### Co-Located

![Co-Located Kernel Gateway](diagrams/jkg-colocated-diagram.png)

#### Client Kernel Gateway System Design

![Client Kernel Gateway](diagrams/jkg-client-diagram.png)


### On-premises ArcGIS Enterprise with Insights Insights and Cloud Kernel Gateway


![Cloud Kernel Gateway](diagrams/jkg-cloud-diagram.png)


*Other configurations and designs may exisit.
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13 changes: 6 additions & 7 deletions Docker/Dockerfile → gateway/Dockerfile
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# jupyter image w/ Python
# Jupyter image w/ Python
FROM jupyter/scipy-notebook

LABEL maintainer="Insights"

USER root

# uncomment to set working directory in container
# WORKDIR /data
WORKDIR /data

# uncomment to copy certificate and key for tls (https)
# COPY /certs/* /etc/ssl/certs/
COPY server.crt /etc/ssl/certs/
COPY server.key /etc/ssl/certs/

# uncomment to copy data to the container
# COPY /data/* /data/
COPY /data/* /data/

# R pre-requisites
RUN apt-get update && \
Expand Down Expand Up @@ -64,4 +63,4 @@ ENV KG_LIST_KERNELS='True'
EXPOSE 9999

# set tls (https) on the kernel gateway
CMD ["jupyter", "kernelgateway", "--KernelGatewayApp.ip=0.0.0.0", "--KernelGatewayApp.port=9999", "--KernelGatewayApp.allow_credentials='*'", "--KernelGatewayApp.allow_headers='*'", "--KernelGatewayApp.allow_methods='*'", "--KernelGatewayApp.certfile=/etc/ssl/certs/<your_cert.crt>", "--KernelGatewayApp.keyfile=/etc/ssl/certs/<your_key>.key"]
CMD ["jupyter", "kernelgateway", "--KernelGatewayApp.ip=0.0.0.0", "--KernelGatewayApp.port=9999", "--KernelGatewayApp.allow_credentials='*'", "--KernelGatewayApp.allow_headers='*'", "--KernelGatewayApp.allow_methods='*'", "--KernelGatewayApp.certfile=/etc/ssl/certs/server.crt", "--KernelGatewayApp.keyfile=/etc/ssl/certs/server.key"]
17 changes: 17 additions & 0 deletions gateway/insights-base.yml
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name: insights-base
channels:
- defaults
- esri
dependencies:
- python=3.7
- jupyter_kernel_gateway=2.1.0
- pandas
- numpy
- shapely
- requests
- msgpack-python
- matplotlib
- geopandas
- arcgis
- r-itertools
- r-essentials

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