filter-collections, docs and version 0.0.6

README.md

@@ -41,7 +41,7 @@ print(pyaro.timeseries.filters.list)
 
 with engines['csv_timeseries'].open(
     filename=TEST_FILE,
-    filters=[pyaro.timeseries.filters.get('countries', include=['NO'])]
+    filters={'countries': {include=['NO']}}
     ) as ts:
     for var in ts.variables():
         # stations

docs/tutorials/basic_api.ipynb

@@ -6,12 +6,12 @@
    "source": [
     "# Pyaro basic example\n",
     "\n",
-    "Install pyaro and check if installation is new enough:"
+    "* Install pyaro and check if installation is new enough:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -20,7 +20,7 @@
        "'0.0.5'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -34,13 +34,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Check a list of installed engines. The most basic installation will install only the `csv_timeseries` engine.\n",
+    "* Check a list of installed engines. The most basic installation will install only the `csv_timeseries` engine.\n",
     "Install e.g. `https://github.com/metno/pyaro-readers` for many more engines."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -49,7 +49,7 @@
        "{'csv_timeseries': <pyaro.csvreader.CSVTimeseriesReader.CSVTimeseriesEngine at 0x7ff77705f250>}"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -62,12 +62,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Learn a bit about the engine."
+    "* Learn a bit about the engine."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -144,12 +144,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Check the description and the open-arguments to open a database with this engine:"
+    "* Check the description and the open-arguments to open a database with this engine:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -170,6 +170,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Opening a datasource with an engine\n",
+    "\n",
     "Open now the timeseries `ts` with a table. You could do that with a `with` clause in larger code, \n",
     "but for simplicity, we don't do that here. `columns` map the files columns to the data, starting\n",
     "with first column as 0, which contains the variable-name in our example file.\n",
@@ -180,7 +182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -212,20 +214,22 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "`ts` is now the handle to all data."
+    "`ts` is now the handle to the data-source.\n",
+    "\n",
+    "* Accessing metadata in the datasource, like available variables and stations"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "dict_keys(['SOx', 'NOx'])\n",
-      "{'station1': <pyaro.timeseries.Station.Station object at 0x7ff776c60d30>, 'station2': <pyaro.timeseries.Station.Station object at 0x7ff776c60790>}\n"
+      "{'station1': <pyaro.timeseries.Station.Station object at 0x7ff776cc9d20>, 'station2': <pyaro.timeseries.Station.Station object at 0x7ff776cca6e0>}\n"
      ]
     }
    ],
@@ -234,11 +238,26 @@
     "print(ts.stations())"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* The timeseries must be accessed per variable. It will be returned for all\n",
+    "stations. The data-columns can be accessed by `keys()`:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('values', 'stations', 'latitudes', 'longitudes', 'altitudes', 'start_times', 'end_times', 'flags', 'standard_deviations')\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -265,14 +284,15 @@
        "       93.348305 , 97.57919  , 19.217777 , 11.676097 ], dtype=float32)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "var = 'SOx'\n",
     "ts_data = ts.data(var)\n",
+    "print(ts_data.keys())\n",
     "ts_data.stations\n",
     "ts_data.start_times\n",
     "ts_data.end_times\n",
@@ -287,12 +307,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Conversion to pandas\n",
+    "\n",
     "For pandas users, the timeseries data can be converted to a dataframe:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -495,7 +517,7 @@
        "[104 rows x 9 columns]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }