Merge pull request #23 from tskisner/format

Enforce coding format with uncrustify and black.

.atom/config.cson

@@ -0,0 +1,24 @@
+"*":
+  "atom-beautify":
+    c:
+      beautify_on_save: true
+      configPath: "src/uncrustify.cfg"
+    cpp:
+      beautify_on_save: true
+      configPath: "src/uncrustify.cfg"
+    python: {}
+  editor:
+    autoIndentOnPaste: false
+    fontSize: 18
+    preferredLineLength: 88
+    scrollPastEnd: true
+    showInvisibles: true
+    softWrap: true
+    softWrapAtPreferredLineLength: true
+    tabLength: 4
+    tabType: "soft"
+  "linter-flake8":
+    maxLineLength: 88
+  "python-black":
+    fmtOnSave: true
+    lineLength: 88

examples/demo_telescope.py

@@ -1,10 +1,10 @@
 #!/usr/bin/env python3
-##
-##  TImestream DAta Storage (TIDAS)
-##  Copyright (c) 2014-2018, all rights reserved.  Use of this source code
-##  is governed by a BSD-style license that can be found in the top-level
-##  LICENSE file.
-##
+#
+# TImestream DAta Storage (TIDAS).
+#
+# Copyright (c) 2015-2019 by the parties listed in the AUTHORS file.  All rights
+# reserved.  Use of this source code is governed by a BSD-style license that can be
+# found in the LICENSE file.
 
 # WARNING:  Running this script will generate a several GB of data...
 
@@ -37,12 +37,12 @@
 # ---- Data from a weather station ----
 
 wfields = list()
-wfields.append( tidas.Field("windspeed", tdt.float32, "Meters / second") )
-wfields.append( tidas.Field("windangle", tdt.float32, "Degrees") )
-wfields.append( tidas.Field("temperature", tdt.float32, "Degrees Celsius") )
-wfields.append( tidas.Field("pressure", tdt.float32, "Millibars") )
-wfields.append( tidas.Field("humidity", tdt.float32, "Percent") )
-wfields.append( tidas.Field("PWV", tdt.float32, "mm") )
+wfields.append(tidas.Field("windspeed", tdt.float32, "Meters / second"))
+wfields.append(tidas.Field("windangle", tdt.float32, "Degrees"))
+wfields.append(tidas.Field("temperature", tdt.float32, "Degrees Celsius"))
+wfields.append(tidas.Field("pressure", tdt.float32, "Millibars"))
+wfields.append(tidas.Field("humidity", tdt.float32, "Percent"))
+wfields.append(tidas.Field("PWV", tdt.float32, "mm"))
 weather_schema = tidas.Schema(wfields)
 
 # sampled every 10 seconds
@@ -52,8 +52,8 @@
 # ---- Housekeeping data ----
 
 hfields = list()
-hfields.append( tidas.Field("thermo1", tdt.float32, "Degrees Kelvin") )
-hfields.append( tidas.Field("thermo2", tdt.float32, "Degrees Kelvin") )
+hfields.append(tidas.Field("thermo1", tdt.float32, "Degrees Kelvin"))
+hfields.append(tidas.Field("thermo2", tdt.float32, "Degrees Kelvin"))
 hk_schema = tidas.Schema(hfields)
 
 # sampled once per minute
@@ -63,9 +63,9 @@
 # ---- Pointing data ----
 
 pfields = list()
-pfields.append( tidas.Field("az", tdt.float32, "Radians") )
-pfields.append( tidas.Field("el", tdt.float32, "Radians") )
-pfields.append( tidas.Field("psi", tdt.float32, "Radians") )
+pfields.append(tidas.Field("az", tdt.float32, "Radians"))
+pfields.append(tidas.Field("el", tdt.float32, "Radians"))
+pfields.append(tidas.Field("psi", tdt.float32, "Radians"))
 pointing_schema = tidas.Schema(pfields)
 
 # sampled at 20 Hz
@@ -80,7 +80,7 @@
 
 for d in range(ndet):
     detname = "det_{:04d}".format(d)
-    dfields.append( tidas.Field(detname, tdt.int16, "ADU") )
+    dfields.append(tidas.Field(detname, tdt.int16, "ADU"))
 
 det_schema = tidas.Schema(dfields)
 
@@ -98,16 +98,15 @@
 # Create the volume all at once.  To keep the size of the volume
 # reasonable for this demo, only write 3 days of data.
 
-vol = tidas.Volume(path, tidas.BackendType.hdf5,
-    tidas.CompressionType.none, dict())
+vol = tidas.Volume(path, tidas.BackendType.hdf5, tidas.CompressionType.none, dict())
 
 # Get the root block of the volume
 root = vol.root()
 
 volstart = datetime.datetime(2018, 1, 1)
 volstartsec = volstart.timestamp()
 
-for year in ["2018",]:
+for year in ["2018"]:
     # Add a block for this year
     yb = root.block_add(year, tidas.Block())
 
@@ -120,8 +119,7 @@
         for dy in range(1, 4):
 
             daystart = datetime.datetime(int(year), monthnum, dy)
-            daystartsec = (daystart - volstart).total_seconds() \
-                + volstartsec
+            daystartsec = (daystart - volstart).total_seconds() + volstartsec
 
             # Add a block for the day
             day = "{:02d}".format(dy)
@@ -137,83 +135,112 @@
 
             print("  writing weather data")
 
-            weather = tidas.Group(weather_schema, tidas.Dictionary(),
-                weather_daysamples)
+            weather = tidas.Group(
+                weather_schema, tidas.Dictionary(), weather_daysamples
+            )
             weather = db.group_add("weather", weather)
-            weather.write_times(0, np.linspace(daystartsec,
-                daystartsec + day_seconds, num=weather_daysamples))
-
-            data = np.absolute(np.random.normal(loc=0.0, scale=5.0,
-                size=weather_daysamples)).astype(np.float32)
+            weather.write_times(
+                0,
+                np.linspace(
+                    daystartsec, daystartsec + day_seconds, num=weather_daysamples
+                ),
+            )
+
+            data = np.absolute(
+                np.random.normal(loc=0.0, scale=5.0, size=weather_daysamples)
+            ).astype(np.float32)
             weather.write("windspeed", 0, data)
 
-            data = 360.0 * np.absolute(np.random.random(
-                size=weather_daysamples)).astype(np.float32)
+            data = 360.0 * np.absolute(
+                np.random.random(size=weather_daysamples)
+            ).astype(np.float32)
             weather.write("windangle", 0, data)
 
-            data = np.absolute(np.random.normal(loc=25.0, scale=5.0,
-                size=weather_daysamples)).astype(np.float32)
+            data = np.absolute(
+                np.random.normal(loc=25.0, scale=5.0, size=weather_daysamples)
+            ).astype(np.float32)
             weather.write("temperature", 0, data)
 
-            data = np.absolute(np.random.normal(loc=1013.25, scale=30.0,
-                size=weather_daysamples)).astype(np.float32)
+            data = np.absolute(
+                np.random.normal(loc=1013.25, scale=30.0, size=weather_daysamples)
+            ).astype(np.float32)
             weather.write("pressure", 0, data)
 
-            data = np.absolute(np.random.normal(loc=30.0, scale=10.0,
-                size=weather_daysamples)).astype(np.float32)
+            data = np.absolute(
+                np.random.normal(loc=30.0, scale=10.0, size=weather_daysamples)
+            ).astype(np.float32)
             weather.write("humidity", 0, data)
 
-            data = np.absolute(np.random.normal(loc=10.0, scale=5.0,
-                size=weather_daysamples)).astype(np.float32)
+            data = np.absolute(
+                np.random.normal(loc=10.0, scale=5.0, size=weather_daysamples)
+            ).astype(np.float32)
             weather.write("PWV", 0, data)
 
             print("  writing housekeeping data")
 
             hk = tidas.Group(hk_schema, tidas.Dictionary(), hk_daysamples)
             hk = db.group_add("hk", hk)
-            hk.write_times(0, np.linspace(daystartsec,
-                daystartsec + day_seconds, num=hk_daysamples))
-
-            data = np.random.normal(loc=273.0, scale=5.0,
-                size=hk_daysamples).astype(np.float32)
+            hk.write_times(
+                0,
+                np.linspace(daystartsec, daystartsec + day_seconds, num=hk_daysamples),
+            )
+
+            data = np.random.normal(loc=273.0, scale=5.0, size=hk_daysamples).astype(
+                np.float32
+            )
             hk.write("thermo1", 0, data)
 
-            data = np.random.normal(loc=77.0, scale=5.0,
-                size=hk_daysamples).astype(np.float32)
+            data = np.random.normal(loc=77.0, scale=5.0, size=hk_daysamples).astype(
+                np.float32
+            )
             hk.write("thermo2", 0, data)
 
             print("  writing pointing data")
 
-            pointing = tidas.Group(pointing_schema, tidas.Dictionary(),
-                pointing_daysamples)
+            pointing = tidas.Group(
+                pointing_schema, tidas.Dictionary(), pointing_daysamples
+            )
             pointing = db.group_add("pointing", pointing)
-            pointing.write_times(0, np.linspace(daystartsec,
-                daystartsec + day_seconds, num=pointing_daysamples))
-
-            data = 2.0 * np.pi * np.random.random(
-                size=pointing_daysamples).astype(np.float32)
+            pointing.write_times(
+                0,
+                np.linspace(
+                    daystartsec, daystartsec + day_seconds, num=pointing_daysamples
+                ),
+            )
+
+            data = (
+                2.0
+                * np.pi
+                * np.random.random(size=pointing_daysamples).astype(np.float32)
+            )
             pointing.write("az", 0, data)
 
-            data = 0.4 * np.pi * np.random.random(
-                size=pointing_daysamples).astype(np.float32)
+            data = (
+                0.4
+                * np.pi
+                * np.random.random(size=pointing_daysamples).astype(np.float32)
+            )
             pointing.write("el", 0, data)
 
-            data = np.random.normal(loc=0.0, scale=(0.01*np.pi),
-                size=pointing_daysamples).astype(np.float32)
+            data = np.random.normal(
+                loc=0.0, scale=(0.01 * np.pi), size=pointing_daysamples
+            ).astype(np.float32)
             pointing.write("psi", 0, data)
 
             print("  writing detector data")
 
-            det = tidas.Group(det_schema, tidas.Dictionary(),
-                det_daysamples)
+            det = tidas.Group(det_schema, tidas.Dictionary(), det_daysamples)
             det = db.group_add("detectors", det)
-            det.write_times(0, np.linspace(daystartsec,
-                daystartsec + day_seconds, num=det_daysamples))
+            det.write_times(
+                0,
+                np.linspace(daystartsec, daystartsec + day_seconds, num=det_daysamples),
+            )
 
             for d in range(ndet):
                 detname = "det_{:04d}".format(d)
-                data = np.random.normal(loc=32768, scale=2000,
-                size=det_daysamples).astype(np.int16)
+                data = np.random.normal(
+                    loc=32768, scale=2000, size=det_daysamples
+                ).astype(np.int16)
                 det.write(detname, 0, data)
 
 # Take a quick peek at organization: