updated markdown.

Author: frey-perez

tests/sample_data/mims_test_data_code.md

@@ -15,7 +15,7 @@ The MIMS algorithm consists of 5 main steps:
 Each major step has associated test data that was used to validate the wristpy implementation. Test data was gathered using an actigraph device model: wGT3XBT which has a dynamic range of
 ±8
 
-### 1. Interpolation Test data
+### 1. Interpolation test data.
 
 The interpolation test data was generated using the following R script and saved as 'actigraph_interpolation_r_version.csv' :
 
@@ -27,7 +27,7 @@ interpolate_df <- MIMSunit::interpolate_signal(df = accel_df, method = "spline_n
 write.csv(interpolate_df, "actigraph_interpolation_r_version.csv", row.names = FALSE)
 ```
 
-### 1. Interpolation Test data
+### 1. Interpolation test data.
 
 The interpolation test data was generated using the following R script and saved as 'actigraph_interpolation_r_version.csv' :
 
@@ -39,7 +39,7 @@ interpolate_df <- MIMSunit::interpolate_signal(df = accel_df, method = "spline_n
 write.csv(interpolate_df, "actigraph_interpolation_r_version.csv", row.names = FALSE)
 ```
 
-### 2. Bandpass Filter Test data
+### 2. Bandpass Filter test data.
 
 The Bandpass Filter test data was generated using the following R script and saved as 'mims_butterworth_filtered_data.csv' :
 
@@ -52,7 +52,7 @@ filtered_df <- MIMSunit::iir(interpolate_df, sr = 100, cutoff_freq = c(0.2, 5),
 write.csv(filtered_df, "mims_butterworth_filtered_data.csv", row.names = FALSE)
 ```
 
-### 3. Aggregation Test data
+### 3. Aggregation test data.
 
 The Aggregation test data was generated using the following R script and saved as 'aggregation_r_version.csv' :
 
@@ -65,5 +65,19 @@ aggregated_df <- <- MIMSunit::aggregate_for_mims( interpolate_df,  epoch = "1 mi
 write.csv(aggredgated_df, "aggregation_r_version.csv", row.names = FALSE)
 ```
 
+### 4. Full-Run Test Data.
+
+The Full-Run test data was generated using the following R script and saved as 'mims_example_full_run.csv' :
+
+```r
+library(MIMSunit)
+raw_data <- read.gt3x("~/Github/wristpy/tests/sample_data/example_actigraph.gt3x")
+accel_df <- as.data.frame(raw_data)
+mims_unit_one_sec <- MIMSunit::mims_unit(df = accel_df, dynamic_range = c(-8, 8), epoch = "1 sec")
+output_file <- "~/Documents/mims_example_full_run.csv"
+write.csv(mims_unit_one_sec, file = output_file, row.names = FALSE)
+```
+
+
 ## References
 [1] John D, Tang Q, Albinali F, Intille S. An Open-Source Monitor-Independent Movement Summary for Accelerometer Data Processing. J Meas Phys Behav. 2019 Dec;2(4):268-281. doi: 10.1123/jmpb.2018-0068. PMID: 34308270; PMCID: PMC8301210.

tests/unit/test_metrics.py

@@ -15,23 +15,6 @@
 TEST_LENGTH = 100
 
 
-@pytest.fixture
-def create_acceleration() -> pl.DataFrame:
-    """Fixture to create a dummy acceleration DataFrame to be used in multiple tests."""
-    dummy_date = datetime(2024, 5, 2)
-    dummy_datetime_list = [dummy_date + timedelta(seconds=i) for i in range(1000)]
-    test_time = pl.Series("time", dummy_datetime_list)
-    acceleration_polars_df = pl.DataFrame(
-        {
-            "X": np.ones(1000),
-            "Y": np.ones(1000),
-            "Z": np.ones(1000),
-            "time": test_time,
-        }
-    )
-    return acceleration_polars_df
-
-
 @pytest.mark.parametrize(
     "x,y,z, expected_enmo",
     [