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-rw-r--r--docs/user_guide/timestamped_data/data_synchronisation.md10
-rw-r--r--docs/user_guide/timestamped_data/introduction.md2
-rw-r--r--docs/user_guide/timestamped_data/ordered_dictionary.md2
-rw-r--r--docs/user_guide/timestamped_data/pandas_dataframe_conversion.md4
-rw-r--r--docs/user_guide/timestamped_data/saving_and_loading.md2
5 files changed, 10 insertions, 10 deletions
diff --git a/docs/user_guide/timestamped_data/data_synchronisation.md b/docs/user_guide/timestamped_data/data_synchronisation.md
index 24a474c..5190eab 100644
--- a/docs/user_guide/timestamped_data/data_synchronisation.md
+++ b/docs/user_guide/timestamped_data/data_synchronisation.md
@@ -3,13 +3,13 @@ Data synchronisation
Recorded data needs to be synchronized to link them before further processings.
-The [TimeStampedBuffer](../../../argaze/#argaze.DataStructures.TimeStampedBuffer) class provides various methods to help in such task.
+The [TimeStampedBuffer](../../argaze.md/#argaze.DataStructures.TimeStampedBuffer) class provides various methods to help in such task.
## Pop last before
![Pop last before](../../img/pop_last_before.png)
-The code below shows how to use [pop_last_before](../../../argaze/#argaze.DataStructures.TimeStampedBuffer.pop_last_before) method in order to synchronise two timestamped data buffers with different timestamps:
+The code below shows how to use [pop_last_before](../../argaze.md/#argaze.DataStructures.TimeStampedBuffer.pop_last_before) method in order to synchronise two timestamped data buffers with different timestamps:
``` python
from argaze import DataStructures
@@ -34,7 +34,7 @@ for A_ts, A_data in A_data_record.items():
![Pop last until](../../img/pop_last_until.png)
-The code below shows how to use [pop_last_until](../../../argaze/#argaze.DataStructures.TimeStampedBuffer.pop_last_until) method in order to synchronise two timestamped data buffers with different timestamps:
+The code below shows how to use [pop_last_until](../../argaze.md/#argaze.DataStructures.TimeStampedBuffer.pop_last_until) method in order to synchronise two timestamped data buffers with different timestamps:
``` python
from argaze import DataStructures
@@ -59,7 +59,7 @@ for A_ts, A_data in A_data_record.items():
![Get last before](../../img/get_last_before.png)
-The code below shows how to use [get_last_before](../../../argaze/#argaze.DataStructures.TimeStampedBuffer.get_last_before) method in order to synchronise two timestamped data buffers with different timestamps:
+The code below shows how to use [get_last_before](../../argaze.md/#argaze.DataStructures.TimeStampedBuffer.get_last_before) method in order to synchronise two timestamped data buffers with different timestamps:
``` python
from argaze import DataStructures
@@ -84,7 +84,7 @@ for A_ts, A_data in A_data_record.items():
![Get last until](../../img/get_last_until.png)
-The code below shows how to use [get_last_until](../../../argaze/#argaze.DataStructures.TimeStampedBuffer.get_last_until) method in order to synchronise two timestamped data buffers with different timestamps:
+The code below shows how to use [get_last_until](../../argaze.md/#argaze.DataStructures.TimeStampedBuffer.get_last_until) method in order to synchronise two timestamped data buffers with different timestamps:
``` python
from argaze import DataStructures
diff --git a/docs/user_guide/timestamped_data/introduction.md b/docs/user_guide/timestamped_data/introduction.md
index df8b9b4..974e2be 100644
--- a/docs/user_guide/timestamped_data/introduction.md
+++ b/docs/user_guide/timestamped_data/introduction.md
@@ -3,4 +3,4 @@ Timestamped data
Working with wearable eye tracker devices implies to handle various timestamped data like gaze positions, pupills diameter, fixations, saccades, ...
-This section mainly refers to [DataStructures.TimeStampedBuffer](../../../argaze/#argaze.DataStructures.TimeStampedBuffer) class.
+This section mainly refers to [DataStructures.TimeStampedBuffer](../../argaze.md/#argaze.DataStructures.TimeStampedBuffer) class.
diff --git a/docs/user_guide/timestamped_data/ordered_dictionary.md b/docs/user_guide/timestamped_data/ordered_dictionary.md
index a3154eb..64dd899 100644
--- a/docs/user_guide/timestamped_data/ordered_dictionary.md
+++ b/docs/user_guide/timestamped_data/ordered_dictionary.md
@@ -1,7 +1,7 @@
Ordered dictionary
==================
-[TimeStampedBuffer](../../../argaze/#argaze.DataStructures.TimeStampedBuffer) class inherits from [OrderedDict](https://docs.python.org/3/library/collections.html#collections.OrderedDict) as data are de facto ordered by time.
+[TimeStampedBuffer](../../argaze.md/#argaze.DataStructures.TimeStampedBuffer) class inherits from [OrderedDict](https://docs.python.org/3/library/collections.html#collections.OrderedDict) as data are de facto ordered by time.
Any data type can be stored using int or float keys as timestamp.
diff --git a/docs/user_guide/timestamped_data/pandas_dataframe_conversion.md b/docs/user_guide/timestamped_data/pandas_dataframe_conversion.md
index eaa7b69..7614e73 100644
--- a/docs/user_guide/timestamped_data/pandas_dataframe_conversion.md
+++ b/docs/user_guide/timestamped_data/pandas_dataframe_conversion.md
@@ -9,7 +9,7 @@ A [Pandas DataFrame](https://pandas.pydata.org/docs/getting_started/intro_tutori
## Export as dataframe
-[TimeStampedBuffer](../../../argaze/#argaze.DataStructures.TimeStampedBuffer) instance can be converted into dataframe provided that data values are stored as dictionaries.
+[TimeStampedBuffer](../../argaze.md/#argaze.DataStructures.TimeStampedBuffer) instance can be converted into dataframe provided that data values are stored as dictionaries.
```python
from argaze import DataStructures
@@ -38,4 +38,4 @@ ts_buffer_dataframe would look like:
## Import from dataframe
-Reversely, [TimeStampedBuffer](../../../argaze/#argaze.DataStructures.TimeStampedBuffer) instance can be created from dataframe, as a result of which each dataframe columns label will become a key of data value dictionary. Notice that the column containing timestamp values have to be called 'timestamp'.
+Reversely, [TimeStampedBuffer](../../argaze.md/#argaze.DataStructures.TimeStampedBuffer) instance can be created from dataframe, as a result of which each dataframe columns label will become a key of data value dictionary. Notice that the column containing timestamp values have to be called 'timestamp'.
diff --git a/docs/user_guide/timestamped_data/saving_and_loading.md b/docs/user_guide/timestamped_data/saving_and_loading.md
index ae26052..4e6a094 100644
--- a/docs/user_guide/timestamped_data/saving_and_loading.md
+++ b/docs/user_guide/timestamped_data/saving_and_loading.md
@@ -1,7 +1,7 @@
Saving and loading
==================
-[TimeStampedBuffer](../../../argaze/#argaze.DataStructures.TimeStampedBuffer) instance can be saved as and loaded from JSON file format.
+[TimeStampedBuffer](../../argaze.md/#argaze.DataStructures.TimeStampedBuffer) instance can be saved as and loaded from JSON file format.
```python