Polished up SqDist_usage.md

- (hopefully) a more clear explanation of how --sqdistd-alpha, --sqdist-beta,
  --sqdist-m, and --sqdist-gamma options work;
- actually tested the examples against an updated geomag-algorithms install,
  and it works!

docs/algorithms/SqDist_usage.md

 Solar Quiet and Disturbance Algorithm Usage
 ===========================================
 
-The Solar Quiet and Disturbance Algorithm calculates `Solar Variation`,
+The Solar Quiet and Disturbance Algorithm calculates `Secular Variation`,
 `Solar Quiet` and `Disturbance` from raw Geomagnetic data.
 
 `geomag.py --algorithm sqdist`
 
 
-### Example
+### Examples
+
+This example processes one Edge channel (X) for the BOU observatory, for one
+day of minute data. The "memory" of SV is 30 days, or 43200 samples, so the
+forgetting factor (--sqdist-alpha) is 1/43200, or ~2.315e-5. The initial
+slope/trend of SV is assumed to be zero, and the --sqdist-beta option used here
+ensures it will remain zero. The SQ signal repeats once-per-day, so the length
+of the SQ vector (--sqdist-m) is 1440. SQ also has a memory of 30 days, but
+because each element of SQ is updated only once-per-day, --sqdist-gamma must
+be 1440 times larger than --sqdist-alpha. Finally, the outputs are renamed to
+valid Edge channels, and written to standard output using IAGA2002 format.
 
-This example uses a state file to produce magnetic-h-based Dist, SQ, and SV
-channels using the EDGE channel naming convention.
 
     bin/geomag.py \
       --input edge \
       --observatory BOU \
-      --inchannels H E Z F \
+      --inchannels X \
       --starttime 2016-01-03T00:01:00 \
       --endtime 2016-01-04T00:00:00 \
+      --interval minute \
       --algorithm sqdist \
-      --sqdist-mag \
-      --sqdist-statefile=/tmp/sqdist_h_state.json \
-      --rename-output-channel H_SQ MSQ \
-      --rename-output-channel H_SV MSV \
-      --rename-output-channel H_Dist MDT \
-      --outchannels MDT MSQ MSV \
+      --sqdist-alpha 2.315e-5 \
+      --sqdist-beta 0 \
+      --sqdist-m 1440 \
+      --sqdist-gamma 3.333e-2 \
+      --sqdist-statefile /tmp/sqdist_x_state.json \
+      --rename-output-channel X_Dist MXT \
+      --rename-output-channel X_SQ MXQ \
+      --rename-output-channel X_SV MXV \
+      --outchannels MXT MXQ MXV \
       --output iaga2002 \
       --output-stdout
 
-This example processes just one channel (X), but explicitly sets parameters to
-ensure 1-minute data is loaded, and the SV and SQ values will evolve with ~30
-day time constant.
+
+This example decomposes the total horizontal field (--sqdist-mag) into SQ, SV,
+and Dist. 1/minute data is assumed by default, so again SV has a memory of 43200
+samples, or forgets 1/43200 (~2.315e-5) with each new observation processed
+(--sqdist-alpha). By neglecting to specify --sqdist-beta, --sqdist-m, and
+--sqdist-gamma, default values mean that no SQ correction is estimated (or more
+precisely, SQ is zero). In other words, this is "simple exponential smoothing".
 
     bin/geomag.py \
       --input edge \
       --observatory BOU \
-      --inchannels X \
+      --inchannels H E Z F \
       --starttime 2016-01-03T00:01:00 \
       --endtime 2016-01-04T00:00:00 \
-      --interval minute \
       --algorithm sqdist \
-      --sqdist-m = 1440 \
-      --sqdist-alpha = 2.315e-5 \
-      --sqdist-gamma = 3.333e-2 \
-      --sqdist-statefile=/tmp/sqdist_x_state.json \
-      --rename-output-channel X_Dist MXT \
-      --rename-output-channel X_SQ MXQ \
-      --rename-output-channel X_SV MXV \
-      --outchannels MXT MXQ MXV \
-      --output iaga \
+      --sqdist-mag \
+      --sqdist-alpha 2.315e-5 \
+      --sqdist-statefile /tmp/sqdist_h_state.json \
+      --rename-output-channel H_SQ MSQ \
+      --rename-output-channel H_SV MSV \
+      --rename-output-channel H_Dist MDT \
+      --outchannels MDT MSQ MSV \
+      --output iaga2002 \
       --output-stdout
 
-> Note only one inchannel is specified, the --sqdist-mag option is omitted, the 
-> --interval option is explicit (and there are 1440 of them in a day), alpha is 
-> approximately 1/30/1440, and gamma is approximately 1/30.
-
 
 ### Library Notes