diff --git a/PSPops/data_handling.py b/PSPops/data_handling.py
index 3772ca29c3fe956936c625488ba57f8e30934eb7..7ef416da906b1fdd274d3c125f265aad1c97f393 100644
--- a/PSPops/data_handling.py
+++ b/PSPops/data_handling.py
@@ -9,21 +9,22 @@ os.environ["CDF_LIB"] = "/usr/local/cdf/lib"
def cdf_slice(cdf_file, key: str):
"""
- DOC
+ Simple call to specific slice of cdf data.
- :param cdf_file:
- :param key:
- :return:
+ :param cdf_file: Name of cdf file
+ :param key: Name of desired key from cdf file
+ :return: Datta slice
"""
return cdf_file[key][...]
def data_generation(cdf_file) -> tp.Dict:
"""
- DOC
+ Generate dictionary of measurement data from cdf file
- :param cdf_file:
- :return:
+ :param cdf_file: CDF file
+ :return: DICT,
+ Data dictionary
"""
data = {
"dqf": cdf_slice(cdf_file, key="general_flag"),
@@ -61,11 +62,14 @@ def array_reduction(data_array: np.ndarray,
def full_reduction(data_dict: tp.Dict,
bad_indices: np.array) -> tp.Dict:
"""
- DOC
+ Fully reduce data dictionary by all determined "bad" indices
- :param data_dict:
- :param bad_indices:
- :return:
+ :param data_dict: DICT;
+ Data dictionary (for PSP data)
+ :param bad_indices: NDARRAY,
+ Array of "bad" indices
+ :return: DICT,
+ Same as input, but with reduced value arrays
"""
for key in data_dict.keys():
data_dict[key] = array_reduction(data_dict[key], bad_indices)
diff --git a/PSPops/data_quality.py b/PSPops/data_quality.py
index 10c31fb3993673a5e38a3d5ad9ec9cc9f606dcee..df01170b4ae93ff49f14fd4c254883ac1eaac549 100644
--- a/PSPops/data_quality.py
+++ b/PSPops/data_quality.py
@@ -40,9 +40,13 @@ def meas_failed(meas_array: np.ndarray) -> np.ndarray:
def full_meas_eval(data_dict: tp.Dict):
"""
+ Assess additional failure indices by evaluating all desired
+ measurement values.
- :param data_dict:
- :return:
+ :param data_dict: DICT,
+ Dictionary of PSP measurement data
+ :return: LIST,
+ List of "bad" indices
"""
col_ind = []
diff --git a/Statistics/__pycache__/stats.cpython-38.pyc b/Statistics/__pycache__/stats.cpython-38.pyc
index 6cf3cebff9e7cfe9f1c54bf64905437512a9e55e..328c94c0d7534544b009e4cd6d9061d3c9bdeed3 100644
Binary files a/Statistics/__pycache__/stats.cpython-38.pyc and b/Statistics/__pycache__/stats.cpython-38.pyc differ
diff --git a/Statistics/stats.py b/Statistics/stats.py
index 8529f94856ff8acdfd1cb14389fbdadb1acc0f1f..6c8698bcd13e0fb76c467a1659cf13904e601bc9 100644
--- a/Statistics/stats.py
+++ b/Statistics/stats.py
@@ -27,6 +27,10 @@ def stat_ana(value_sample):
median = np.median(value_sample)
std = np.std(value_sample)
- return mean, median, std
+ return {
+ "mean": mean,
+ "median": median,
+ "stddev": std
+ }
\ No newline at end of file
diff --git a/TESTING/testing.py b/TESTING/testing.py
index 335e604b22960a6096bcf288c489dc26ace8db64..748e8d50383b6b1bbdc8d9e1ab31b239fae08110 100644
--- a/TESTING/testing.py
+++ b/TESTING/testing.py
@@ -1,7 +1,6 @@
import os
import sys
from unipath import Path
-import scipy.stats as st
import numpy as np
import matplotlib.pyplot as plt
from spacepy import pycdf
@@ -48,14 +47,6 @@ data["dTlo"] = dt.wp_to_temp(data["dwplo"])
distance_bins = db.create_bins(0, 100, .5)
bin_indices = db.sort_bins(distance_bins, data["r"] * 1e3 / R_sun.value)
-for key in bin_indices.keys():
- print(key)
- test = key.strip("()").strip().split(",")
- print(test)
- print("".join(test))
-
-exit()
-
test_r = st.slice_index_list(data["r"], bin_indices["(46.5, 47.0)"]) * 1e3 / R_sun.value
test_v = st.slice_index_list(data["vr"], bin_indices["(46.5, 47.0)"])
diff --git a/binned_stats.py b/binned_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7c76d24ecd53f695af9f6014373939cde09c3d8
--- /dev/null
+++ b/binned_stats.py
@@ -0,0 +1,55 @@
+import sys
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+from astropy.constants import R_sun
+from PSPops import plot_settings as ps
+from Statistics import stats as st
+
+# DESIGNATE BINNED DATA LOCATION
+BIN_DATA_LOCATION = sys.path[0]+"/BINNED_DATA"
+
+# SANITY CHECK: Does the data directory even exist?
+if not os.path.isdir(BIN_DATA_LOCATION):
+ print(f"\n{BIN_DATA_LOCATION} IS NOT A VALID DIRECTORY!\n")
+ sys.exit(0)
+
+
+def main():
+
+ test_r = test_v = np.array([])
+ std_r = std_v = np.array([])
+
+ # Loop over all files in the binned data directory
+ for name in sorted(os.listdir(BIN_DATA_LOCATION)):
+
+ # Generate correct pointer to data file
+ file = BIN_DATA_LOCATION + f"/{name}"
+
+ # Generate numpy array from data files (multi-dim)
+ all_data = np.loadtxt(file, skiprows=1)
+
+ # Generate sub-arrays from all data
+ r = all_data[:, 0]
+ vr = all_data[:, 1]
+
+ stat_r = st.stat_ana(r * 1e3 / R_sun.value)
+ stat_vr = st.stat_ana(vr)
+
+ test_r = np.append(test_r, stat_r["mean"])
+ test_v = np.append(test_v, stat_vr["mean"])
+
+ std_r = np.append(std_r, stat_r["stddev"])
+ std_v = np.append(std_v, stat_vr["stddev"])
+
+ plt.plot(test_r, test_v)
+ plt.fill_between(test_r, test_v + std_v, test_v - std_v,
+ color="grey", alpha=0.35, zorder=1)
+
+
+if __name__ == "__main__":
+ ps.rc_setup()
+
+ main()
+
+ plt.show()
diff --git a/main.py b/main.py
index 08645c2efc22fd5e348cc00ff7762e79b1487ba0..dfbf82f4f38fe17dd175c20d18a29cc06a666771 100644
--- a/main.py
+++ b/main.py
@@ -1,6 +1,5 @@
import os
import sys
-import matplotlib.pyplot as plt
import numpy as np
from spacepy import pycdf
from PSPops import data_quality as dq
@@ -18,7 +17,7 @@ os.environ["CDF_LIB"] = "/usr/local/cdf/lib"
ENCOUNTER_NUM = "encounter_5"
DATA_LOCATION = sys.path[0]+"/DATA/"+ENCOUNTER_NUM
-# SANITY CHECK IF DATA LOCATION EXISTS
+# SANITY CHECK: Does the data directory even exist?
if not os.path.isdir(DATA_LOCATION):
print(f"\n{DATA_LOCATION} IS NOT A VALID DIRECTORY!\n")
sys.exit(0)
@@ -53,15 +52,6 @@ def main():
r_tot = np.append(r_tot, data["r"])
vr_tot = np.append(vr_tot, data["vr"])
- """
- PSEUDOCODE
-
- Here - total arrays of distance, vr etc.
- Create distance bins for whole distance range
- - Sort data into sub-arrays based on distance bins
- - Save files for each distance bin
- This way, data can be handled by a different routine
- """
# Create distance bins and determine indices of data arrays that
# correspond to the respective distance bins. Some of these
# sub-arrays might be empty and have to be handled accordingly
@@ -71,7 +61,6 @@ def main():
# Create a loop for all sub-arrays, and "continue" if the index
# array is empty
for key in bin_indices:
-
# Skip if the index array is empty
if not np.size(bin_indices[key]):
continue
@@ -84,29 +73,16 @@ def main():
binned_r = st.slice_index_list(r_tot, bin_indices[key])
binned_vr = st.slice_index_list(vr_tot, bin_indices[key])
+ ###
+ # EMPTY DIRECTORY BEFORE RUNNING
+ ###
+
file_name = f"BINNED_DATA/PSP-RBIN-{name_append}.dat"
with open(file_name, "w") as f:
f.write("r [km] \t vr [km/s] \n")
for i in range(sub_len):
f.write(f"{binned_r[i]} \t {binned_vr[i]}\n")
-
-
-
- exit()
- # Find the turn-around index for the encounter
- ta_index = np.argmin(r_tot)
-
- # Create "approach" and "recession" values
- r_app = r_tot[:ta_index]
- v_app = vr_tot[:ta_index]
-
- r_rec = r_tot[ta_index + 1:]
- v_rec = vr_tot[ta_index + 1:]
-
- plt.plot(r_app, v_app)
- plt.plot(r_rec, v_rec)
- plt.show()
if __name__ == "__main__":