Tests and docs

Author: mgoldmansight

docs/entities/kpis.md

@@ -36,10 +36,42 @@ In order to make use of the data viz function you'll need the name of the KPI yo
 ### Get KPI Data Viz
 Once you have the name of the KPI you wish to access and a logged in client you can make a call to the data viz api with the following SDK function call:
 ```
-cli.get_kpi_data_viz(**data_viz_query)
+cli.get_kpi_data_viz(machine_source, kpis, i_vars, time_selection, **optional_data_viz_query)
 ```
 
-The d_vars section of your data_viz_query should include an d_var with the name of the KPI you wish to query.  For more information on [data_viz_queries](/docs/commonly_used_data_types/data_viz_query.md) click on the previous link.  After some time the SDK should return a list that looks something like this:
+After some time the SDK should return a list that looks something like this:
 ```
 [{'i_vals': {'endtime': {'i_pos': 0, 'bin_no': 0, 'bin_min': '2022-10-20T00:00:00-07:00', 'bin_max': '2022-10-20T00:00:00-07:00', 'bin_avg': '2022-10-20T00:00:00-07:00'}}, 'd_vals': {'quality': {'avg': 95.18072289156626}}, '_count': 418, 'kpi_dependencies': {'quality': {'Output': 395.0, 'ScrapQuantity': 20.0}}},...]
+```
+
+There's two ways to call this function you can use a data_viz_query,For more information on [data_viz_queries](/docs/commonly_used_data_types/data_viz_query.md) click on the previous link, or have the function fill out the query for you by passing in a few variable we will now go over one at a time.
+
+#### machine_source
+This is a string and is the name of machine you wish to run a query on.
+
+#### kpis
+This is a list of the names of all the kpis you wish to run this query on.
+
+#### i_vars
+This is a list, this is the same as the i_vars object in the data_viz_query and is the axis you are querying the kpis against it will look like the following:
+```
+[
+    {
+      "name": "endtime",
+      "time_resolution": "day",
+      "query_tz": "America/Los_Angeles",
+      "output_tz": "America/Los_Angeles"
+    }
+]
+```
+
+#### time_selection
+This is an object, this is the same as the [time_selection](/docs/commonly_used_data_types/data_viz_query.md#time_selection) object in the data_viz_query and more info can be found at that link.  The most common form is the relative time selection and looks like this:
+```
+{
+    "time_type": "relative",
+    "relative_start": 7,
+    "relative_unit": "day",
+    "ctime_tz": "America/Los_Angeles"
+}
 ```

docs/entities/machine.md

@@ -0,0 +1,15 @@
+# Machines
+Machines are just that, machines in factories.  The machine object is how the Sight Machine software replesents and is the key to accessing data that we collect from them
+
+## Functions
+
+### get_type_from_machine
+The get_type_from_machine function allows you to get the type of any machine from it's name and is called this way:
+```
+cli.get_type_from_machine(machine_name)
+```
+
+And will return something like the following:
+```
+'Lasercut'
+```

tests/kpi/test_kpi.py

@@ -28,6 +28,7 @@ def json():
     assert len(kpis) == 5
 
     assert kpis[0]["name"] == 'Scrap_Rate'
+
 def test_get_kpi_for_asset(monkeypatch):
     # Setup
     def mockapi(self, session, endpoint):
@@ -77,4 +78,4 @@ def json():
     dt = Client("demo")
     data = dt.get_kpi_data_viz()
     assert len(data) == 3
-    assert data[0]['d_vals']['quality']['avg'] == 95.18072289156626
+    assert data[0]['d_vals']['quality']['avg'] == 95.18072289156626

tests/machine/test_machine.py

@@ -1,5 +1,8 @@
+from unittest.mock import MagicMock
+from mock import patch
 import pandas as pd
 from requests.sessions import Session
+from smsdk.client import Client
 from tests.machine.machine_data import JSON_MACHINE
 from smsdk.smsdk_entities.machine.machine import Machine
 
@@ -32,3 +35,27 @@ def mockapi(self, session, endpoint):
     ]
 
     assert cols == df.columns.sort_values().tolist()
+
+
+@patch("smsdk.ma_session.Session")
+def test_get_type(mocked):
+    class ResponseGet:
+        ok = True
+        text = "Success"
+        status_code=200
+
+        @staticmethod
+        def json():
+            return {'results':[{"machine":[{"name":"test", "type":'test_type'}]}]}
+
+    mocked.return_value = MagicMock(
+       get=MagicMock(return_value=ResponseGet())
+    )
+
+    dt = Client("demo")
+
+    # Run
+    type = dt.get_type_from_machine('test')
+
+    # Verify
+    assert type == 'test_type'